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Sendecki A, Ledwoń D, Tuszy A, Nycz J, Wąsowska A, Boguszewska-Chachulska A, Mitas AW, Wylęgała E, Teper S. Fundus Image Deep Learning Study to Explore the Association of Retinal Morphology with Age-Related Macular Degeneration Polygenic Risk Score. Biomedicines 2024; 12:2092. [PMID: 39335605 PMCID: PMC11429376 DOI: 10.3390/biomedicines12092092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2024] [Revised: 09/10/2024] [Accepted: 09/10/2024] [Indexed: 09/30/2024] Open
Abstract
BACKGROUND Age-related macular degeneration (AMD) is a complex eye disorder with an environmental and genetic origin, affecting millions worldwide. The study aims to explore the association between retinal morphology and the polygenic risk score (PRS) for AMD using fundus images and deep learning techniques. METHODS The study used and pre-processed 23,654 fundus images from 332 subjects (235 patients with AMD and 97 controls), ultimately selecting 558 high-quality images for analysis. The fine-tuned DenseNet121 deep learning model was employed to estimate PRS from single fundus images. After training, deep features were extracted, fused, and used in machine learning regression models to estimate PRS for each subject. The Grad-CAM technique was applied to examine the relationship between areas of increased model activity and the retina's morphological features specific to AMD. RESULTS Using the hybrid approach improved the results obtained by DenseNet121 in 5-fold cross-validation. The final evaluation metrics for all predictions from the best model from each fold are MAE = 0.74, MSE = 0.85, RMSE = 0.92, R2 = 0.18, MAPE = 2.41. Grad-CAM heatmap evaluation showed that the model decisions rely on lesion area, focusing mostly on the presence of drusen. The proposed approach was also shown to be sensitive to artifacts present in the image. CONCLUSIONS The findings indicate an association between fundus images and AMD PRS, suggesting that deep learning models may effectively estimate genetic risk for AMD from retinal images, potentially aiding in early detection and personalized treatment strategies.
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Affiliation(s)
- Adam Sendecki
- Chair and Clinical Department of Ophthalmology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-752 Katowice, Poland; (A.S.); (E.W.); (S.T.)
| | - Daniel Ledwoń
- Faculty of Biomedical Engineering, Silesian University of Technology, 41-800 Zabrze, Poland; (A.T.); (A.W.M.)
| | - Aleksandra Tuszy
- Faculty of Biomedical Engineering, Silesian University of Technology, 41-800 Zabrze, Poland; (A.T.); (A.W.M.)
| | - Julia Nycz
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693 Ilmenau, Germany;
| | - Anna Wąsowska
- Department of Bioinformatics, Polish-Japanese Academy of Information Technology, 02-008 Warszawa, Poland
| | | | - Andrzej W. Mitas
- Faculty of Biomedical Engineering, Silesian University of Technology, 41-800 Zabrze, Poland; (A.T.); (A.W.M.)
| | - Edward Wylęgała
- Chair and Clinical Department of Ophthalmology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-752 Katowice, Poland; (A.S.); (E.W.); (S.T.)
| | - Sławomir Teper
- Chair and Clinical Department of Ophthalmology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-752 Katowice, Poland; (A.S.); (E.W.); (S.T.)
- Department of Scientific Research, Branch in Bielsko-Biala, Medical University of Silesia, 40-752 Katowice, Poland
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An S, Squirrell D. Validation of neuron activation patterns for artificial intelligence models in oculomics. Sci Rep 2024; 14:20940. [PMID: 39251780 PMCID: PMC11383926 DOI: 10.1038/s41598-024-71517-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Accepted: 08/28/2024] [Indexed: 09/11/2024] Open
Abstract
Recent advancements in artificial intelligence (AI) have prompted researchers to expand into the field of oculomics; the association between the retina and systemic health. Unlike conventional AI models trained on well-recognized retinal features, the retinal phenotypes that most oculomics models use are more subtle. Consequently, applying conventional tools, such as saliency maps, to understand how oculomics models arrive at their inference is problematic and open to bias. We hypothesized that neuron activation patterns (NAPs) could be an alternative way to interpret oculomics models, but currently, most existing implementations focus on failure diagnosis. In this study, we designed a novel NAP framework to interpret an oculomics model. We then applied our framework to an AI model predicting systolic blood pressure from fundus images in the United Kingdom Biobank dataset. We found that the NAP generated from our framework was correlated to the clinically relevant endpoint of cardiovascular risk. Our NAP was also able to discern two biologically distinct groups among participants who were assigned the same predicted systolic blood pressure. These results demonstrate the feasibility of our proposed NAP framework for gaining deeper insights into the functioning of oculomics models. Further work is required to validate these results on external datasets.
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Affiliation(s)
- Songyang An
- School of Optometry and Vision Science, The University of Auckland, 85 Park Rd, Grafton, Auckland, 1023, New Zealand.
- Toku Eyes Limited NZ, Auckland, New Zealand.
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Nielsen C, Souza R, Wilms M, Forkert ND. Foundation model-driven distributed learning for enhanced retinal age prediction. J Am Med Inform Assoc 2024:ocae220. [PMID: 39225790 DOI: 10.1093/jamia/ocae220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 07/24/2024] [Accepted: 08/21/2024] [Indexed: 09/04/2024] Open
Abstract
OBJECTIVES The retinal age gap (RAG) is emerging as a potential biomarker for various diseases of the human body, yet its utility depends on machine learning models capable of accurately predicting biological retinal age from fundus images. However, training generalizable models is hindered by potential shortages of diverse training data. To overcome these obstacles, this work develops a novel and computationally efficient distributed learning framework for retinal age prediction. MATERIALS AND METHODS The proposed framework employs a memory-efficient 8-bit quantized version of RETFound, a cutting-edge foundation model for retinal image analysis, to extract features from fundus images. These features are then used to train an efficient linear regression head model for predicting retinal age. The framework explores federated learning (FL) as well as traveling model (TM) approaches for distributed training of the linear regression head. To evaluate this framework, we simulate a client network using fundus image data from the UK Biobank. Additionally, data from patients with type 1 diabetes from the UK Biobank and the Brazilian Multilabel Ophthalmological Dataset (BRSET) were utilized to explore the clinical utility of the developed methods. RESULTS Our findings reveal that the developed distributed learning framework achieves retinal age prediction performance on par with centralized methods, with FL and TM providing similar performance (mean absolute error of 3.57 ± 0.18 years for centralized learning, 3.60 ± 0.16 years for TM, and 3.63 ± 0.19 years for FL). Notably, the TM was found to converge with fewer local updates than FL. Moreover, patients with type 1 diabetes exhibited significantly higher RAG values than healthy controls in all models, for both the UK Biobank and BRSET datasets (P < .001). DISCUSSION The high computational and memory efficiency of the developed distributed learning framework makes it well suited for resource-constrained environments. CONCLUSION The capacity of this framework to integrate data from underrepresented populations for training of retinal age prediction models could significantly enhance the accessibility of the RAG as an important disease biomarker.
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Affiliation(s)
- Christopher Nielsen
- Department of Radiology, University of Calgary, Calgary, AB T2N 4N1, Canada
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Raissa Souza
- Department of Radiology, University of Calgary, Calgary, AB T2N 4N1, Canada
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB T2N 4N1, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Matthias Wilms
- Department of Radiology, University of Calgary, Calgary, AB T2N 4N1, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Pediatrics, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Community Health Sciences, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Nils D Forkert
- Department of Radiology, University of Calgary, Calgary, AB T2N 4N1, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB T2N 4N1, Canada
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Lin S, Gao M, Zhang J, Wu Y, Yu T, Peng Y, Jia Y, Zou H, Lu L, Li D, Ma Y. Sleep onset time as a mediator in the association between screen exposure and aging: a cross-sectional study. GeroScience 2024:10.1007/s11357-024-01321-x. [PMID: 39190220 DOI: 10.1007/s11357-024-01321-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Accepted: 08/19/2024] [Indexed: 08/28/2024] Open
Abstract
Excessive screen exposure has become a significant health concern. This study investigates the impact of screen time on aging in middle-aged and elderly populations. Healthy working adults over 45 years old in Shanghai, China, underwent general and ocular examinations. Questionnaires collected demographics, medical history, and screen exposure details. Aging was assessed using the retinal age gap, defined as the difference between the retinal age predicted by deep learning algorithms based on fundus images and chronological age. Pathway analysis tested the mediation effect of sleep duration and onset time on the relationship between screen usage and retinal age gap. The retinal age gap increased with longer screen exposure, from 0.49 ± 3.51 years in the lowest tertile to 5.13 ± 4.96 years in the highest tertile (Jonckheere-Terpstra test, p < 0.001). Each additional hour of screen exposure accelerated the retinal age gap by 0.087 years (95% CI, 0.027, 0.148, p = 0.005) in the fully adjusted linear model. Sleep onset time mediated the impact of screen usage on the retinal age gap (indirect effect, β = 0.11; 95% CI 0.04-0.24). The impact of screen usage in a light-off environment on the retinal age gap was fully mediated by sleep onset time (indirect effect, β = 0.22; 95% CI 0.07-0.38), with the proportion being 100%. Our study identified a correlation between excessive screen time and a wider retinal age gap in middle-aged and elderly individuals, likely due to delayed sleep onset. To mitigate the adverse effects on the retina and aging, it is important to limit screen usage and avoid screens before bedtime.
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Affiliation(s)
- Senlin Lin
- Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, No. 1440, Hongqiao Road, Shanghai, 200336, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Meng Gao
- Sijing Community Health Service Center, Shanghai, China
| | - Juzhao Zhang
- Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, No. 1440, Hongqiao Road, Shanghai, 200336, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Yuting Wu
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Tao Yu
- Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, No. 1440, Hongqiao Road, Shanghai, 200336, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Yajun Peng
- Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, No. 1440, Hongqiao Road, Shanghai, 200336, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Yingnan Jia
- Key Lab of Public Health Safety of the Ministry of Education, School of Public Health, Fudan University, 130 Dongan Road, Shanghai, 200032, China
- Health Communication Institute, Fudan University, Shanghai, 200032, China
| | - Haidong Zou
- Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, No. 1440, Hongqiao Road, Shanghai, 200336, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 100, Haining Road, Shanghai, 200080, China
| | - Lina Lu
- Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, No. 1440, Hongqiao Road, Shanghai, 200336, China.
- National Clinical Research Center for Eye Diseases, Shanghai, China.
- Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China.
| | - Deshang Li
- Shihudang Community Health Service Center, No. 1 to 5, Lane 50, Yanshou Road, Shanghai, China.
| | - Yingyan Ma
- Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, No. 1440, Hongqiao Road, Shanghai, 200336, China.
- National Clinical Research Center for Eye Diseases, Shanghai, China.
- Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China.
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 100, Haining Road, Shanghai, 200080, China.
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Grimbly MJ, Koopowitz SM, Chen R, Sun Z, Foster PJ, He M, Stein DJ, Ipser J, Zhu Z. Estimating biological age from retinal imaging: a scoping review. BMJ Open Ophthalmol 2024; 9:e001794. [PMID: 39181547 PMCID: PMC11344507 DOI: 10.1136/bmjophth-2024-001794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Accepted: 07/25/2024] [Indexed: 08/27/2024] Open
Abstract
BACKGROUND/AIMS The emerging concept of retinal age, a biomarker derived from retinal images, holds promise in estimating biological age. The retinal age gap (RAG) represents the difference between retinal age and chronological age, which serves as an indicator of deviations from normal ageing. This scoping review aims to collate studies on retinal age to determine its potential clinical utility and to identify knowledge gaps for future research. METHODS Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist, eligible non-review, human studies were identified, selected and appraised. PubMed, Scopus, SciELO, PsycINFO, Google Scholar, Cochrane, CINAHL, Africa Wide EBSCO, MedRxiv and BioRxiv databases were searched to identify literature pertaining to retinal age, the RAG and their associations. No restrictions were imposed on publication date. RESULTS Thirteen articles published between 2022 and 2023 were analysed, revealing four models capable of determining biological age from retinal images. Three models, 'Retinal Age', 'EyeAge' and a 'convolutional network-based model', achieved comparable mean absolute errors: 3.55, 3.30 and 3.97, respectively. A fourth model, 'RetiAGE', predicting the probability of being older than 65 years, also demonstrated strong predictive ability with respect to clinical outcomes. In the models identified, a higher predicted RAG demonstrated an association with negative occurrences, notably mortality and cardiovascular health outcomes. CONCLUSION This review highlights the potential clinical application of retinal age and RAG, emphasising the need for further research to establish their generalisability for clinical use, particularly in neuropsychiatry. The identified models showcase promising accuracy in estimating biological age, suggesting its viability for evaluating health status.
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Affiliation(s)
- Michaela Joan Grimbly
- SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Sheri-Michelle Koopowitz
- SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Ruiye Chen
- Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, Victoria, Australia
- Ophthalmology, Department of Surgery, Univeristy of Melbourne, Melbourne, Victoria, Australia
| | - Zihan Sun
- NIHR Biomedical Research Centre, Moorfields NHS Foundation Trust and The UCL Institute of Ophthalmology, London, United Kingdon
| | - Paul J Foster
- NIHR Biomedical Research Centre, Moorfields NHS Foundation Trust and The UCL Institute of Ophthalmology, London, United Kingdon
| | - Mingguang He
- Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, Victoria, Australia
- Ophthalmology, Department of Surgery, Univeristy of Melbourne, Melbourne, Victoria, Australia
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Dan J Stein
- SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Jonathan Ipser
- SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Zhuoting Zhu
- Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, Victoria, Australia
- Ophthalmology, Department of Surgery, Univeristy of Melbourne, Melbourne, Victoria, Australia
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Grzybowski A, Jin K, Zhou J, Pan X, Wang M, Ye J, Wong TY. Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review. Ophthalmol Ther 2024; 13:2125-2149. [PMID: 38913289 PMCID: PMC11246322 DOI: 10.1007/s40123-024-00981-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 04/15/2024] [Indexed: 06/25/2024] Open
Abstract
We conducted a systematic review of research in artificial intelligence (AI) for retinal fundus photographic images. We highlighted the use of various AI algorithms, including deep learning (DL) models, for application in ophthalmic and non-ophthalmic (i.e., systemic) disorders. We found that the use of AI algorithms for the interpretation of retinal images, compared to clinical data and physician experts, represents an innovative solution with demonstrated superior accuracy in identifying many ophthalmic (e.g., diabetic retinopathy (DR), age-related macular degeneration (AMD), optic nerve disorders), and non-ophthalmic disorders (e.g., dementia, cardiovascular disease). There has been a significant amount of clinical and imaging data for this research, leading to the potential incorporation of AI and DL for automated analysis. AI has the potential to transform healthcare by improving accuracy, speed, and workflow, lowering cost, increasing access, reducing mistakes, and transforming healthcare worker education and training.
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Affiliation(s)
- Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznań , Poland.
| | - Kai Jin
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jingxin Zhou
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiangji Pan
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Meizhu Wang
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Juan Ye
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Tien Y Wong
- School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
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Chew EY, Burns SA, Abraham AG, Bakhoum MF, Beckman JA, Chui TYP, Finger RP, Frangi AF, Gottesman RF, Grant MB, Hanssen H, Lee CS, Meyer ML, Rizzoni D, Rudnicka AR, Schuman JS, Seidelmann SB, Tang WHW, Adhikari BB, Danthi N, Hong Y, Reid D, Shen GL, Oh YS. Standardization and clinical applications of retinal imaging biomarkers for cardiovascular disease: a Roadmap from an NHLBI workshop. Nat Rev Cardiol 2024:10.1038/s41569-024-01060-8. [PMID: 39039178 DOI: 10.1038/s41569-024-01060-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/21/2024] [Indexed: 07/24/2024]
Abstract
The accessibility of the retina with the use of non-invasive and relatively low-cost ophthalmic imaging techniques and analytics provides a unique opportunity to improve the detection, diagnosis and monitoring of systemic diseases. The National Heart, Lung, and Blood Institute conducted a workshop in October 2022 to examine this concept. On the basis of the discussions at that workshop, this Roadmap describes current knowledge gaps and new research opportunities to evaluate the relationships between the eye (in particular, retinal biomarkers) and the risk of cardiovascular diseases, including coronary artery disease, heart failure, stroke, hypertension and vascular dementia. Identified gaps include the need to simplify and standardize the capture of high-quality images of the eye by non-ophthalmic health workers and to conduct longitudinal studies using multidisciplinary networks of diverse at-risk populations with improved implementation and methods to protect participant and dataset privacy. Other gaps include improving the measurement of structural and functional retinal biomarkers, determining the relationship between microvascular and macrovascular risk factors, improving multimodal imaging 'pipelines', and integrating advanced imaging with 'omics', lifestyle factors, primary care data and radiological reports, by using artificial intelligence technology to improve the identification of individual-level risk. Future research on retinal microvascular disease and retinal biomarkers might additionally provide insights into the temporal development of microvascular disease across other systemic vascular beds.
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Affiliation(s)
- Emily Y Chew
- Division of Epidemiology and Clinical Applications, National Eye Institute, NIH, Bethesda, MD, USA.
| | - Stephen A Burns
- School of Optometry, Indiana University, Bloomington, IN, USA
| | - Alison G Abraham
- Department of Epidemiology, Colorado School of Public Health, University of Colorado, Aurora, CO, USA
| | - Mathieu F Bakhoum
- Departments of Ophthalmology and Visual Science and Pathology, School of Medicine, Yale University, New Haven, CT, USA
| | - Joshua A Beckman
- Division of Vascular Medicine, University of Southwestern Medical Center, Dallas, TX, USA
| | - Toco Y P Chui
- Department of Ophthalmology, New York Eye and Ear Infirmary of Mount Sinai, New York, NY, USA
| | - Robert P Finger
- Department of Ophthalmology, Mannheim Medical Faculty, University of Heidelberg, Mannheim, Germany
| | - Alejandro F Frangi
- Division of Informatics, Imaging and Data Science (School of Health Sciences), Department of Computer Science (School of Engineering), University of Manchester, Manchester, UK
- Alan Turing Institute, London, UK
| | - Rebecca F Gottesman
- Stroke Branch, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
| | - Maria B Grant
- Department of Ophthalmology and Visual Sciences, School of Medicine, University of Alabama Heersink School of Medicine, Birmingham, AL, USA
| | - Henner Hanssen
- Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Cecilia S Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
| | - Michelle L Meyer
- Department of Emergency Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Damiano Rizzoni
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Alicja R Rudnicka
- Population Health Research Institute, St. George's University of London, London, UK
| | - Joel S Schuman
- Wills Eye Hospital, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA, USA
| | - Sara B Seidelmann
- Department of Clinical Medicine, Columbia College of Physicians and Surgeons, Greenwich, CT, USA
| | - W H Wilson Tang
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Bishow B Adhikari
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD, USA
| | - Narasimhan Danthi
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD, USA
| | - Yuling Hong
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD, USA
| | - Diane Reid
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD, USA
| | - Grace L Shen
- Retinal Diseases Program, Division of Extramural Science Programs, National Eye Institute, NIH, Bethesda, MD, USA
| | - Young S Oh
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD, USA
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Wang R, Tan Y, Zhong Z, Rao S, Zhou Z, Zhang L, Zhang C, Chen W, Ruan L, Sun X. Deep Learning-Based Vascular Aging Prediction From Retinal Fundus Images. Transl Vis Sci Technol 2024; 13:10. [PMID: 38984914 PMCID: PMC11238877 DOI: 10.1167/tvst.13.7.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 04/19/2024] [Indexed: 07/11/2024] Open
Abstract
Purpose The purpose of this study was to establish and validate a deep learning model to screen vascular aging using retinal fundus images. Although vascular aging is considered a novel cardiovascular risk factor, the assessment methods are currently limited and often only available in developed regions. Methods We used 8865 retinal fundus images and clinical parameters of 4376 patients from two independent datasets for training a deep learning algorithm. The gold standard for vascular aging was defined as a pulse wave velocity ≥1400 cm/s. The probability of the presence of vascular aging was defined as deep learning retinal vascular aging score, the Reti-aging score. We compared the performance of the deep learning model and clinical parameters by calculating the area under the receiver operating characteristics curve (AUC). We recruited clinical specialists, including ophthalmologists and geriatricians, to assess vascular aging in patients using retinal fundus images, aiming to compare the diagnostic performance between deep learning models and clinical specialists. Finally, the potential of Reti-aging score for identifying new-onset hypertension (NH) and new-onset carotid artery plaque (NCP) in the subsequent three years was examined. Results The Reti-aging score model achieved an AUC of 0.826 (95% confidence interval [CI] = 0.793-0.855) and 0.779 (95% CI = 0.765-0.794) in the internal and external dataset. It showed better performance in predicting vascular aging compared with the prediction with clinical parameters. The average accuracy of ophthalmologists (66.3%) was lower than that of the Reti-aging score model, whereas geriatricians were unable to make predictions based on retinal fundus images. The Reti-aging score was associated with the risk of NH and NCP (P < 0.05). Conclusions The Reti-aging score model might serve as a novel method to predict vascular aging through analysis of retinal fundus images. Reti-aging score provides a novel indicator to predict new-onset cardiovascular diseases. Translational Relevance Given the robust performance of our model, it provides a new and reliable method for screening vascular aging, especially in undeveloped areas.
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Affiliation(s)
- Ruohong Wang
- Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Yuhe Tan
- Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Zheng Zhong
- Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Suyun Rao
- Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Ziqing Zhou
- Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Lisha Zhang
- Department of Health Management Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Cuntai Zhang
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Wei Chen
- Department of Computer Center, Tongji Hospital affiliated to Tongji Medical College of Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Lei Ruan
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Xufang Sun
- Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
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Yang X, Li R, Yang X, Zhou Y, Liu Y, Han JDJ. Coordinate-wise monotonic transformations enable privacy-preserving age estimation with 3D face point cloud. SCIENCE CHINA. LIFE SCIENCES 2024; 67:1489-1501. [PMID: 38573362 DOI: 10.1007/s11427-023-2518-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 12/25/2023] [Indexed: 04/05/2024]
Abstract
The human face is a valuable biomarker of aging, but the collection and use of its image raise significant privacy concerns. Here we present an approach for facial data masking that preserves age-related features using coordinate-wise monotonic transformations. We first develop a deep learning model that estimates age directly from non-registered face point clouds with high accuracy and generalizability. We show that the model learns a highly indistinguishable mapping using faces treated with coordinate-wise monotonic transformations, indicating that the relative positioning of facial information is a low-level biomarker of facial aging. Through visual perception tests and computational 3D face verification experiments, we demonstrate that transformed faces are significantly more difficult to perceive for human but not for machines, except when only the face shape information is accessible. Our study leads to a facial data protection guideline that has the potential to broaden public access to face datasets with minimized privacy risks.
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Affiliation(s)
- Xinyu Yang
- School of Life Sciences, Peking University, Beijing, 100871, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China
| | - Runhan Li
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China
| | - Xindi Yang
- Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China
| | - Yong Zhou
- Clinical Research Institute, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yi Liu
- Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
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10
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Vabalas A, Hartonen T, Vartiainen P, Jukarainen S, Viippola E, Rodosthenous RS, Liu A, Hägg S, Perola M, Ganna A. Deep learning-based prediction of one-year mortality in Finland is an accurate but unfair aging marker. NATURE AGING 2024; 4:1014-1027. [PMID: 38914859 PMCID: PMC11257968 DOI: 10.1038/s43587-024-00657-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 05/27/2024] [Indexed: 06/26/2024]
Abstract
Short-term mortality risk, which is indicative of individual frailty, serves as a marker for aging. Previous age clocks focused on predicting either chronological age or longer-term mortality. Aging clocks predicting short-term mortality are lacking and their algorithmic fairness remains unexamined. We developed a deep learning model to predict 1-year mortality using nationwide longitudinal data from the Finnish population (FinRegistry; n = 5.4 million), incorporating more than 8,000 features spanning up to 50 years. We achieved an area under the curve (AUC) of 0.944, outperforming a baseline model that included only age and sex (AUC = 0.897). The model generalized well to different causes of death (AUC > 0.800 for 45 of 50 causes), including coronavirus disease 2019, which was absent in the training data. Performance varied among demographics, with young females exhibiting the best and older males the worst results. Extensive prediction fairness analyses highlighted disparities among disadvantaged groups, posing challenges to equitable integration into public health interventions. Our model accurately identified short-term mortality risk, potentially serving as a population-wide aging marker.
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Affiliation(s)
- Andrius Vabalas
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Tuomo Hartonen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Pekka Vartiainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Pediatric Research Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Sakari Jukarainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Essi Viippola
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | | | - Aoxing Liu
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Markus Perola
- The Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Andrea Ganna
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
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11
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Wu G, Zhang X, Borchert GA, Zheng C, Liang Y, Wang Y, Du Z, Huang Y, Shang X, Yang X, Hu Y, Yu H, Zhu Z. Association of retinal age gap with chronic kidney disease and subsequent cardiovascular disease sequelae: a cross-sectional and longitudinal study from the UK Biobank. Clin Kidney J 2024; 17:sfae088. [PMID: 38989278 PMCID: PMC11233993 DOI: 10.1093/ckj/sfae088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Indexed: 07/12/2024] Open
Abstract
Background Chronic kidney disease (CKD) increases the risk of cardiovascular disease (CVD) and is more prevalent in older adults. Retinal age gap, a biomarker of aging based on fundus images, has been previously developed and validated. This study aimed to investigate the association of retinal age gap with CKD and subsequent CVD complications. Methods A deep learning model was trained to predict the retinal age using 19 200 fundus images of 11 052 participants without any medical history at baseline. Retinal age gap, calculated as retinal age predicted minus chronological age, was calculated for the remaining 35 906 participants. Logistic regression models and Cox proportional hazards regression models were used for the association analysis. Results A total of 35 906 participants (56.75 ± 8.04 years, 55.68% female) were included in this study. In the cross-sectional analysis, each 1-year increase in retinal age gap was associated with a 2% increase in the risk of CKD prevalence [odds ratio 1.02, 95% confidence interval (CI) 1.01-1.04, P = .012]. A longitudinal analysis of 35 039 participants demonstrated that 2.87% of them developed CKD in follow-up, and each 1-year increase in retinal age gap was associated with a 3% increase in the risk of CKD incidence (hazard ratio 1.03, 95% CI 1.01-1.05, P = .004). In addition, a total of 111 CKD patients (15.81%) developed CVD in follow-up, and each 1-year increase in retinal age gap was associated with a 10% increase in the risk of incident CVD (hazard ratio 1.10, 95% CI 1.03-1.17, P = .005). Conclusions We found that retinal age gap was independently associated with the prevalence and incidence of CKD, and also associated with CVD complications in CKD patients. This supports the use of this novel biomarker in identifying individuals at high risk of CKD and CKD patients with increased risk of CVD.
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Affiliation(s)
- Guanrong Wu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Xiayin Zhang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Grace A Borchert
- Ophthalmology, Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia
| | - Chunwen Zheng
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Shantou University Medical College, Shantou, China
| | - Yingying Liang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yaxin Wang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Zijing Du
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yu Huang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xianwen Shang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xiaohong Yang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yijun Hu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Honghua Yu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Zhuoting Zhu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Ophthalmology, Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia
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12
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Castilho RM, Castilho LS, Palomares BH, Squarize CH. Determinants of Chromatin Organization in Aging and Cancer-Emerging Opportunities for Epigenetic Therapies and AI Technology. Genes (Basel) 2024; 15:710. [PMID: 38927646 PMCID: PMC11202709 DOI: 10.3390/genes15060710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 05/21/2024] [Accepted: 05/26/2024] [Indexed: 06/28/2024] Open
Abstract
This review article critically examines the pivotal role of chromatin organization in gene regulation, cellular differentiation, disease progression and aging. It explores the dynamic between the euchromatin and heterochromatin, coded by a complex array of histone modifications that orchestrate essential cellular processes. We discuss the pathological impacts of chromatin state misregulation, particularly in cancer and accelerated aging conditions such as progeroid syndromes, and highlight the innovative role of epigenetic therapies and artificial intelligence (AI) in comprehending and harnessing the histone code toward personalized medicine. In the context of aging, this review explores the use of AI and advanced machine learning (ML) algorithms to parse vast biological datasets, leading to the development of predictive models for epigenetic modifications and providing a framework for understanding complex regulatory mechanisms, such as those governing cell identity genes. It supports innovative platforms like CEFCIG for high-accuracy predictions and tools like GridGO for tailored ChIP-Seq analysis, which are vital for deciphering the epigenetic landscape. The review also casts a vision on the prospects of AI and ML in oncology, particularly in the personalization of cancer therapy, including early diagnostics and treatment optimization for diseases like head and neck and colorectal cancers by harnessing computational methods, AI advancements and integrated clinical data for a transformative impact on healthcare outcomes.
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Affiliation(s)
- Rogerio M. Castilho
- Laboratory of Epithelial Biology, Department of Periodontics and Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, MI 48109-1078, USA; (L.S.C.); (C.H.S.)
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109-1078, USA
| | - Leonard S. Castilho
- Laboratory of Epithelial Biology, Department of Periodontics and Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, MI 48109-1078, USA; (L.S.C.); (C.H.S.)
| | - Bruna H. Palomares
- Oral Diagnosis Department, Piracicaba School of Dentistry, State University of Campinas, Piracicaba 13414-903, Sao Paulo, Brazil;
| | - Cristiane H. Squarize
- Laboratory of Epithelial Biology, Department of Periodontics and Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, MI 48109-1078, USA; (L.S.C.); (C.H.S.)
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109-1078, USA
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13
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Tan YY, Kang HG, Lee CJ, Kim SS, Park S, Thakur S, Da Soh Z, Cho Y, Peng Q, Lee K, Tham YC, Rim TH, Cheng CY. Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging. EYE AND VISION (LONDON, ENGLAND) 2024; 11:17. [PMID: 38711111 PMCID: PMC11071258 DOI: 10.1186/s40662-024-00384-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 04/17/2024] [Indexed: 05/08/2024]
Abstract
BACKGROUND Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina's unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care. MAIN TEXT This narrative review extensively surveys the literature for relevant studies in PubMed and Google Scholar, investigating the application of AI-based retina biomarkers in predicting systemic diseases using retinal fundus photography. The study settings, sample sizes, utilized AI models and corresponding results were extracted and analysed. This review highlights the substantial potential of AI-based retinal biomarkers in predicting neurodegenerative, cardiovascular, and chronic kidney diseases. Notably, DL algorithms have demonstrated effectiveness in identifying retinal image features associated with cognitive decline, dementia, Parkinson's disease, and cardiovascular risk factors. Furthermore, longitudinal prediction models leveraging retinal images have shown potential in continuous disease risk assessment and early detection. AI-based retinal biomarkers are non-invasive, accurate, and efficient for disease forecasting and personalized care. CONCLUSION AI-based retinal imaging hold promise in transforming primary care and systemic disease management. Together, the retina's unique features and the power of AI enable early detection, risk stratification, and help revolutionizing disease management plans. However, to fully realize the potential of AI in this domain, further research and validation in real-world settings are essential.
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Affiliation(s)
| | - Hyun Goo Kang
- Division of Retina, Severance Eye Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Chan Joo Lee
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Sung Soo Kim
- Division of Retina, Severance Eye Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Sungha Park
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Sahil Thakur
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Zhi Da Soh
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Yunnie Cho
- Mediwhale Inc, Seoul, Republic of Korea
- Department of Education and Human Resource Development, Seoul National University Hospital, Seoul, South Korea
| | - Qingsheng Peng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Kwanghyun Lee
- Department of Ophthalmology, National Health Insurance Service Ilsan Hospital, Goyang, Republic of Korea
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program, Singapore, Singapore
| | - Tyler Hyungtaek Rim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
- Mediwhale Inc, Seoul, Republic of Korea.
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program, Singapore, Singapore
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14
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Tang H, Luo N, Zhang X, Huang J, Yang Q, Lin H, Zhang X. Association between biological aging and diabetic retinopathy. Sci Rep 2024; 14:10123. [PMID: 38698194 PMCID: PMC11065862 DOI: 10.1038/s41598-024-60913-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 04/29/2024] [Indexed: 05/05/2024] Open
Abstract
The impact of aging on diabetic retinopathy (DR) remains underestimated. The current study aimed to investigate the association between biological aging and DR, in contrast to chronological age (CA). Using the National Health and Nutrition Survey data from 2005 to 2008. Biological aging was evaluated through the biological age (BA) and phenotypic age (PA), which were calculated from clinical markers. DR was identified in participants with diabetes mellitus (DM) when they exhibited one or more retinal microaneurysms or retinal blot hemorrhages under retinal imaging, with or without the presence of more severe lesions. Survey-weighted multivariable logistic regression was performed, and the regression model was further fitted using restricted cubic splines. The discriminatory capability and clinical utility of the model were evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Based on weighted analyses, of the 3100 participants included in this study, of which 162 had DR. In the adjusted model, BA (odds ratio [OR] = 1.12, 95% CI, 1.06-1.18) and PA (OR = 1.11, 95% CI, 1.07-1.14) were associated with DR, while CA was not significantly (OR = 1.01, 95% CI, 0.99-1.03). Narrowing the analysis to DM participants and adjusting for factors like insulin showed similar results. ROC and DCA analyses indicate that BA/PA predicted DR better than CA and offer greater clinical utility. The positive association between BA/PA and DR was consistent across subgroups despite potential interactions. Biological aging heightens DR risk, with BA/PA showing a stronger association than CA. Our findings underscored the importance of timely anti-aging interventions for preventing DR.
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Affiliation(s)
- Haoxian Tang
- Shantou University Medical College, Shantou, Guangdong, China
- Department of Cardiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Nan Luo
- Shantou University Medical College, Shantou, Guangdong, China
- Department of Psychiatry, Shantou University Mental Health Center, Shantou, Guangdong, China
| | - Xuan Zhang
- Shantou University Medical College, Shantou, Guangdong, China
- Department of Bone and Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Jingtao Huang
- Shantou University Medical College, Shantou, Guangdong, China
- Department of Sports Medicine and Rehabilitation, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Qinglong Yang
- Shantou University Medical College, Shantou, Guangdong, China
- Department of Urology, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Hanyuan Lin
- Shantou University Medical College, Shantou, Guangdong, China
- Department of Urology, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Xinyi Zhang
- Department of Ophthalmology, The First Affiliated Hospital of Shantou University Medical College, No. 57 Changping Road, Shantou, 515041, Guangdong, China.
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15
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Chen R, Zhang S, Peng G, Meng W, Borchert G, Wang W, Yu Z, Liao H, Ge Z, He M, Zhu Z. Deep neural network-estimated age using optical coherence tomography predicts mortality. GeroScience 2024; 46:1703-1711. [PMID: 37733221 PMCID: PMC10828229 DOI: 10.1007/s11357-023-00920-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 08/22/2023] [Indexed: 09/22/2023] Open
Abstract
The concept of biological age has emerged as a measurement that reflects physiological and functional decline with ageing. Here we aimed to develop a deep neural network (DNN) model that predicts biological age from optical coherence tomography (OCT). A total of 84,753 high-quality OCT images from 53,159 individuals in the UK Biobank were included, among which 12,631 3D-OCT images from 8,541 participants without any reported medical conditions at baseline were used to develop an age prediction model. For the remaining 44,618 participants, OCT age gap, the difference between the OCT-predicted age and chronological age, was calculated for each participant. Cox regression models assessed the association between OCT age gap and mortality. The DNN model predicted age with a mean absolute error of 3.27 years and showed a strong correlation of 0.85 with chronological age. After a median follow-up of 11.0 years (IQR 10.9-11.1 years), 2,429 deaths (5.44%) were recorded. For each 5-year increase in OCT age gap, there was an 8% increased mortality risk (hazard ratio [HR] = 1.08, CI:1.02-1.13, P = 0.004). Compared with an OCT age gap within ± 4 years, OCT age gap less than minus 4 years was associated with a 16% decreased mortality risk (HR = 0.84, CI: 0.75-0.94, P = 0.002) and OCT age gap more than 4 years showed an 18% increased risk of death incidence (HR = 1.18, CI: 1.02-1.37, P = 0.026). OCT imaging could serve as an ageing biomarker to predict biological age with high accuracy and the OCT age gap, defined as the difference between the OCT-predicted age and chronological age, can be used as a marker of the risk of mortality.
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Affiliation(s)
- Ruiye Chen
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
| | - Shiran Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China
| | - Guankai Peng
- Guangzhou Vision Tech Medical Technology Co., Ltd, GuangZhou, China
| | - Wei Meng
- Guangzhou Vision Tech Medical Technology Co., Ltd, GuangZhou, China
| | - Grace Borchert
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China
| | - Zhen Yu
- Central Clinical School, Monash University, Melbourne, Australia
| | - Huan Liao
- Epigenetics and Neural Plasticity Laboratory, Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
| | - Zongyuan Ge
- Faculty of IT, Monash University, Melbourne, Australia
- Monash Medical AI, Monash University, Melbourne, Australia
| | - Mingguang He
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia.
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia.
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China.
| | - Zhuoting Zhu
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia.
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia.
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China.
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16
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Meng D, Zhang S, Huang Y, Mao K, Han JDJ. Application of AI in biological age prediction. Curr Opin Struct Biol 2024; 85:102777. [PMID: 38310737 DOI: 10.1016/j.sbi.2024.102777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/12/2023] [Accepted: 01/15/2024] [Indexed: 02/06/2024]
Abstract
The development of anti-aging interventions requires quantitative measurement of biological age. Machine learning models, known as "aging clocks," are built by leveraging diverse aging biomarkers that vary across lifespan to predict biological age. In addition to traditional aging clocks harnessing epigenetic signatures derived from bulk samples, emerging technologies allow the biological age estimating at single-cell level to dissect cellular diversity in aging tissues. Moreover, imaging-based aging clocks are increasingly employed with the advantage of non-invasive measurement, making it suitable for large-scale human cohort studies. To fully capture the features in the ever-growing multi-modal and high-dimensional aging-related data and uncover disease associations, deep-learning based approaches, which are effective to learn complex and non-linear relationships without relying on pre-defined features, are increasingly applied. The use of big data and AI-based aging clocks has achieved high accuracy, interpretability and generalizability, guiding clinical applications to delay age-related diseases and extend healthy lifespans.
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Affiliation(s)
- Dawei Meng
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, China
| | - Shiqiang Zhang
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, China
| | - Yuanfang Huang
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, China
| | - Kehang Mao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, China
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, China.
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17
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Guo M, Higashita R, Lin C, Hu L, Chen W, Li F, Lai GWK, Nguyen A, Sakata R, Okamoto K, Tang B, Xu Y, Fu H, Gao F, Aihara M, Zhang X, Yuan J, Lin S, Leung CKS, Liu J. Crystalline lens nuclear age prediction as a new biomarker of nucleus degeneration. Br J Ophthalmol 2024; 108:513-521. [PMID: 37495263 DOI: 10.1136/bjo-2023-323176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 05/22/2023] [Indexed: 07/28/2023]
Abstract
BACKGROUND The crystalline lens is a transparent structure of the eye to focus light on the retina. It becomes muddy, hard and dense with increasing age, which makes the crystalline lens gradually lose its function. We aim to develop a nuclear age predictor to reflect the degeneration of the crystalline lens nucleus. METHODS First we trained and internally validated the nuclear age predictor with a deep-learning algorithm, using 12 904 anterior segment optical coherence tomography (AS-OCT) images from four diverse Asian and American cohorts: Zhongshan Ophthalmic Center with Machine0 (ZOM0), Tomey Corporation (TOMEY), University of California San Francisco and the Chinese University of Hong Kong. External testing was done on three independent datasets: Tokyo University (TU), ZOM1 and Shenzhen People's Hospital (SPH). We also demonstrate the possibility of detecting nuclear cataracts (NCs) from the nuclear age gap. FINDINGS In the internal validation dataset, the nuclear age could be predicted with a mean absolute error (MAE) of 2.570 years (95% CI 1.886 to 2.863). Across the three external testing datasets, the algorithm achieved MAEs of 4.261 years (95% CI 3.391 to 5.094) in TU, 3.920 years (95% CI 3.332 to 4.637) in ZOM1-NonCata and 4.380 years (95% CI 3.730 to 5.061) in SPH-NonCata. The MAEs for NC eyes were 8.490 years (95% CI 7.219 to 9.766) in ZOM1-NC and 9.998 years (95% CI 5.673 to 14.642) in SPH-NC. The nuclear age gap outperformed both ophthalmologists in detecting NCs, with areas under the receiver operating characteristic curves of 0.853 years (95% CI 0.787 to 0.917) in ZOM1 and 0.909 years (95% CI 0.828 to 0.978) in SPH. INTERPRETATION The nuclear age predictor shows good performance, validating the feasibility of using AS-OCT images as an effective screening tool for nucleus degeneration. Our work also demonstrates the potential use of the nuclear age gap to detect NCs.
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Affiliation(s)
- Mengjie Guo
- Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, Guangdong, China
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Risa Higashita
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
- Tomey Corporation, Nagoya, Aichi, Japan
| | - Chen Lin
- Shenzhen People's Hospital, Shenzhen, Guangdong, China
| | - Lingxi Hu
- Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Wan Chen
- Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Fei Li
- Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Gilda Wing Ki Lai
- Department of Ophthalmology, The University of Hong Kong, Hong Kong, Hong Kong
| | - Anwell Nguyen
- Department of Ophthalmology, University of California, San Francisco, California, USA
| | - Rei Sakata
- Department of Ophthalmology, The University of Tokyo, Tokyo, Japan
| | | | - Bo Tang
- Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Yanwu Xu
- Intelligent Healthcare Unit, Baidu Inc, Beijing, China
| | - Huazhu Fu
- Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore
| | - Fei Gao
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Makoto Aihara
- Department of Ophthalmology, The University of Tokyo, Tokyo, Japan
| | - Xiulan Zhang
- Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Jin Yuan
- Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Shan Lin
- Department of Ophthalmology, University of California, San Francisco, California, USA
- Glaucoma Center of San Francisco, San Francisco, California, USA
| | - Christopher Kai-Shun Leung
- Department of Ophthalmology, The University of Hong Kong, Hong Kong, Hong Kong
- Department of Ophthalmology and Visual Sciences, The Chinese University, Hong Kong, Hong Kong
| | - Jiang Liu
- Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, Guangdong, China
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
- Cixi Institute of Biomedical Engineering, Chinese Academy of Sciences, Cixi, Zhejiang, China
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18
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Chen R, Chen Y, Zhang J, Wang W, Hu W, He M, Zhu Z. Retinal age gap as a predictive biomarker for future risk of clinically significant diabetic retinopathy. Acta Diabetol 2024; 61:373-380. [PMID: 37987832 DOI: 10.1007/s00592-023-02199-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 10/01/2023] [Indexed: 11/22/2023]
Abstract
AIMS Retinal age derived from fundus images has been verified as a novel ageing biomarker. We aim to explore the association between retinal age gap (retinal age minus chronological age) and incident diabetic retinopathy (DR). METHODS Retinal age prediction was performed by a deep learning model, trained and validated based on 19,200 fundus images of 11,052 disease-free participants. Retinal age gaps were determined for 2311 patients with diabetes who had no history of diabetic retinopathy at baseline. DR events were ascertained by data linkage to hospital admissions. Cox proportional hazards regression models were performed to evaluate the association between retinal age gaps and incident DR. RESULTS During the median follow-up period of 11.0 (interquartile range: 10.8-11.1) years, 183 of 2311 participants with diabetes developed incident DR. Each additional year of the retinal age gap was associated with a 7% increase in the risk of incident DR (hazard ratio [HR] = 1.07, 95% confidence interval [CI] 1.02-1.12, P = 0.004), after adjusting for confounding factors. Participants with retinal age gaps in the fourth quartile had a significantly higher DR risk compared to participants with retinal age gaps in the lowest quartile (HR = 2.88, 95% CI 1.61-5.15, P < 0.001). CONCLUSIONS We found that higher retinal age gap was associated with an increased risk of incident DR. As an easy and non-invasive biomarker, the retinal age gap may serve as an informative tool to facilitate the individualized risk assessment and personalized screening protocol for DR.
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Affiliation(s)
- Ruiye Chen
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
| | - Yanping Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Sun Yat-Sen University, Guangzhou, China
| | - Junyao Zhang
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Sun Yat-Sen University, Guangzhou, China
| | - Wenyi Hu
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
| | - Mingguang He
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia.
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia.
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Sun Yat-Sen University, Guangzhou, China.
| | - Zhuoting Zhu
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia.
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia.
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19
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Ungvari Z, Tabák AG, Adany R, Purebl G, Kaposvári C, Fazekas-Pongor V, Csípő T, Szarvas Z, Horváth K, Mukli P, Balog P, Bodizs R, Ujma P, Stauder A, Belsky DW, Kovács I, Yabluchanskiy A, Maier AB, Moizs M, Östlin P, Yon Y, Varga P, Vokó Z, Papp M, Takács I, Vásárhelyi B, Torzsa P, Ferdinandy P, Csiszar A, Benyó Z, Szabó AJ, Dörnyei G, Kivimäki M, Kellermayer M, Merkely B. The Semmelweis Study: a longitudinal occupational cohort study within the framework of the Semmelweis Caring University Model Program for supporting healthy aging. GeroScience 2024; 46:191-218. [PMID: 38060158 PMCID: PMC10828351 DOI: 10.1007/s11357-023-01018-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 11/11/2023] [Indexed: 12/08/2023] Open
Abstract
The Semmelweis Study is a prospective occupational cohort study that seeks to enroll all employees of Semmelweis University (Budapest, Hungary) aged 25 years and older, with a population of 8866 people, 70.5% of whom are women. The study builds on the successful experiences of the Whitehall II study and aims to investigate the complex relationships between lifestyle, environmental, and occupational risk factors, and the development and progression of chronic age-associated diseases. An important goal of the Semmelweis Study is to identify groups of people who are aging unsuccessfully and therefore have an increased risk of developing age-associated diseases. To achieve this, the study takes a multidisciplinary approach, collecting economic, social, psychological, cognitive, health, and biological data. The Semmelweis Study comprises a baseline data collection with open healthcare data linkage, followed by repeated data collection waves every 5 years. Data are collected through computer-assisted self-completed questionnaires, followed by a physical health examination, physiological measurements, and the assessment of biomarkers. This article provides a comprehensive overview of the Semmelweis Study, including its origin, context, objectives, design, relevance, and expected contributions.
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Affiliation(s)
- Zoltan Ungvari
- International Training Program in Geroscience/Healthy Aging Program, Doctoral School of Basic and Translational Medicine/Department of Public Health, Semmelweis University, Budapest, Hungary.
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
- Department of Health Promotion Sciences, The Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
| | - Adam G Tabák
- International Training Program in Geroscience/Healthy Aging Program, Doctoral School of Basic and Translational Medicine/Department of Public Health, Semmelweis University, Budapest, Hungary
- Department of Public Health, Faculty of Medicine, Semmelweis University, Budapest, Hungary
- UCL Brain Sciences, University College London, London, UK
- Department of Internal Medicine and Oncology, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Roza Adany
- International Training Program in Geroscience/Healthy Aging Program, Doctoral School of Basic and Translational Medicine/Department of Public Health, Semmelweis University, Budapest, Hungary
- Department of Public Health, Faculty of Medicine, Semmelweis University, Budapest, Hungary
- HUN-REN-UD Public Health Research Group, Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - György Purebl
- Institute of Behavioral Sciences, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Csilla Kaposvári
- Department of Public Health, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Vince Fazekas-Pongor
- Department of Public Health, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Tamás Csípő
- International Training Program in Geroscience/Healthy Aging Program, Doctoral School of Basic and Translational Medicine/Department of Public Health, Semmelweis University, Budapest, Hungary
- Department of Public Health, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Zsófia Szarvas
- International Training Program in Geroscience/Healthy Aging Program, Doctoral School of Basic and Translational Medicine/Department of Public Health, Semmelweis University, Budapest, Hungary
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Department of Health Promotion Sciences, The Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Department of Public Health, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Krisztián Horváth
- Department of Public Health, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Peter Mukli
- International Training Program in Geroscience/Healthy Aging Program, Doctoral School of Basic and Translational Medicine/Department of Public Health, Semmelweis University, Budapest, Hungary
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Department of Public Health, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Piroska Balog
- Institute of Behavioral Sciences, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Robert Bodizs
- Institute of Behavioral Sciences, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Peter Ujma
- Institute of Behavioral Sciences, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Adrienne Stauder
- Institute of Behavioral Sciences, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Daniel W Belsky
- Robert N. Butler Columbia Aging Center, Columbia University, New York, NY, USA
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Illés Kovács
- Department of Ophthalmology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
- Department of Ophthalmology, Weill Cornell Medical College, New York City, NY, USA
- Department of Clinical Ophthalmology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary
| | - Andriy Yabluchanskiy
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Department of Health Promotion Sciences, The Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Andrea B Maier
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Healthy Longevity, National University Health System, Singapore, Singapore
- Department of Human Movement Sciences, @AgeAmsterdam, Vrije Universiteit, Amsterdam Movement Sciences, Amsterdam, The Netherlands
| | - Mariann Moizs
- Department of Public Health, Faculty of Medicine, Semmelweis University, Budapest, Hungary
- Ministry of Interior of Hungary, Budapest, Hungary
| | | | - Yongjie Yon
- WHO Regional Office for Europe, Copenhagen, Denmark
| | - Péter Varga
- Department of Public Health, Faculty of Medicine, Semmelweis University, Budapest, Hungary
- Clinical Center, Semmelweis University, Budapest, Hungary
| | - Zoltán Vokó
- Center for Health Technology Assessment, Semmelweis University, Budapest, Hungary
| | - Magor Papp
- Department of Public Health, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - István Takács
- UCL Brain Sciences, University College London, London, UK
| | - Barna Vásárhelyi
- Department of Laboratory Medicine, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Péter Torzsa
- Department of Family Medicine, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Péter Ferdinandy
- Department of Pharmacology and Pharmacotherapy, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Anna Csiszar
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Department of Health Promotion Sciences, The Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Zoltán Benyó
- Department of Translational Medicine, Faculty of Medicine, Semmelweis University, Budapest, Hungary
- HUN-REN-SU Cerebrovascular and Neurocognitive Diseases Research Group, Budapest, Hungary
| | - Attila J Szabó
- First Department of Pediatrics, Faculty of Medicine, Semmelweis University, Budapest, Hungary
- HUN-REN-SU Pediatrics and Nephrology Research Group, Semmelweis University, Budapest, Hungary
| | - Gabriella Dörnyei
- Department of Morphology and Physiology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary
| | - Mika Kivimäki
- UCL Brain Sciences, University College London, London, UK
| | - Miklos Kellermayer
- Department of Biophysics and Radiation Biology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Bela Merkely
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary
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20
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Bahr T, Vu TA, Tuttle JJ, Iezzi R. Deep Learning and Machine Learning Algorithms for Retinal Image Analysis in Neurodegenerative Disease: Systematic Review of Datasets and Models. Transl Vis Sci Technol 2024; 13:16. [PMID: 38381447 PMCID: PMC10893898 DOI: 10.1167/tvst.13.2.16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 11/26/2023] [Indexed: 02/22/2024] Open
Abstract
Purpose Retinal images contain rich biomarker information for neurodegenerative disease. Recently, deep learning models have been used for automated neurodegenerative disease diagnosis and risk prediction using retinal images with good results. Methods In this review, we systematically report studies with datasets of retinal images from patients with neurodegenerative diseases, including Alzheimer's disease, Huntington's disease, Parkinson's disease, amyotrophic lateral sclerosis, and others. We also review and characterize the models in the current literature which have been used for classification, regression, or segmentation problems using retinal images in patients with neurodegenerative diseases. Results Our review found several existing datasets and models with various imaging modalities primarily in patients with Alzheimer's disease, with most datasets on the order of tens to a few hundred images. We found limited data available for the other neurodegenerative diseases. Although cross-sectional imaging data for Alzheimer's disease is becoming more abundant, datasets with longitudinal imaging of any disease are lacking. Conclusions The use of bilateral and multimodal imaging together with metadata seems to improve model performance, thus multimodal bilateral image datasets with patient metadata are needed. We identified several deep learning tools that have been useful in this context including feature extraction algorithms specifically for retinal images, retinal image preprocessing techniques, transfer learning, feature fusion, and attention mapping. Importantly, we also consider the limitations common to these models in real-world clinical applications. Translational Relevance This systematic review evaluates the deep learning models and retinal features relevant in the evaluation of retinal images of patients with neurodegenerative disease.
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Affiliation(s)
- Tyler Bahr
- Mayo Clinic, Department of Ophthalmology, Rochester, MN, USA
| | - Truong A. Vu
- University of the Incarnate Word, School of Osteopathic Medicine, San Antonio, TX, USA
| | - Jared J. Tuttle
- University of Texas Health Science Center at San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
| | - Raymond Iezzi
- Mayo Clinic, Department of Ophthalmology, Rochester, MN, USA
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21
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Huang Y, Cheung CY, Li D, Tham YC, Sheng B, Cheng CY, Wang YX, Wong TY. AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook. Eye (Lond) 2024; 38:464-472. [PMID: 37709926 PMCID: PMC10858189 DOI: 10.1038/s41433-023-02724-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 07/26/2023] [Accepted: 08/25/2023] [Indexed: 09/16/2023] Open
Abstract
Cardiovascular disease (CVD) remains the leading cause of death worldwide. Assessing of CVD risk plays an essential role in identifying individuals at higher risk and enables the implementation of targeted intervention strategies, leading to improved CVD prevalence reduction and patient survival rates. The ocular vasculature, particularly the retinal vasculature, has emerged as a potential means for CVD risk stratification due to its anatomical similarities and physiological characteristics shared with other vital organs, such as the brain and heart. The integration of artificial intelligence (AI) into ocular imaging has the potential to overcome limitations associated with traditional semi-automated image analysis, including inefficiency and manual measurement errors. Furthermore, AI techniques may uncover novel and subtle features that contribute to the identification of ocular biomarkers associated with CVD. This review provides a comprehensive overview of advancements made in AI-based ocular image analysis for predicting CVD, including the prediction of CVD risk factors, the replacement of traditional CVD biomarkers (e.g., CT-scan measured coronary artery calcium score), and the prediction of symptomatic CVD events. The review covers a range of ocular imaging modalities, including colour fundus photography, optical coherence tomography, and optical coherence tomography angiography, and other types of images like external eye images. Additionally, the review addresses the current limitations of AI research in this field and discusses the challenges associated with translating AI algorithms into clinical practice.
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Affiliation(s)
- Yu Huang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Dawei Li
- College of Future Technology, Peking University, Beijing, China
| | - Yih Chung Tham
- Centre for Innovation and Precision Eye Health and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Bin Sheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ching Yu Cheng
- Centre for Innovation and Precision Eye Health and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore.
- Tsinghua Medicine, Tsinghua University, Beijing, China.
- School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Beijing, China.
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22
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Zekavat SM, Jorshery SD, Rauscher FG, Horn K, Sekimitsu S, Koyama S, Nguyen TT, Costanzo MC, Jang D, Burtt NP, Kühnapfel A, Shweikh Y, Ye Y, Raghu V, Zhao H, Ghassemi M, Elze T, Segrè AV, Wiggs JL, Del Priore L, Scholz M, Wang JC, Natarajan P, Zebardast N. Phenome- and genome-wide analyses of retinal optical coherence tomography images identify links between ocular and systemic health. Sci Transl Med 2024; 16:eadg4517. [PMID: 38266105 DOI: 10.1126/scitranslmed.adg4517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 01/03/2024] [Indexed: 01/26/2024]
Abstract
The human retina is a multilayered tissue that offers a unique window into systemic health. Optical coherence tomography (OCT) is widely used in eye care and allows the noninvasive, rapid capture of retinal anatomy in exquisite detail. We conducted genotypic and phenotypic analyses of retinal layer thicknesses using macular OCT images from 44,823 UK Biobank participants. We performed OCT layer cross-phenotype association analyses (OCT-XWAS), associating retinal thicknesses with 1866 incident conditions (median 10-year follow-up) and 88 quantitative traits and blood biomarkers. We performed genome-wide association studies (GWASs), identifying inherited genetic markers that influence retinal layer thicknesses and replicated our associations among the LIFE-Adult Study (N = 6313). Last, we performed a comparative analysis of phenome- and genome-wide associations to identify putative causal links between retinal layer thicknesses and both ocular and systemic conditions. Independent associations with incident mortality were detected for thinner photoreceptor segments (PSs) and, separately, ganglion cell complex layers. Phenotypic associations were detected between thinner retinal layers and ocular, neuropsychiatric, cardiometabolic, and pulmonary conditions. A GWAS of retinal layer thicknesses yielded 259 unique loci. Consistency between epidemiologic and genetic associations suggested links between a thinner retinal nerve fiber layer with glaucoma, thinner PS with age-related macular degeneration, and poor cardiometabolic and pulmonary function with a thinner PS. In conclusion, we identified multiple inherited genetic loci and acquired systemic cardio-metabolic-pulmonary conditions associated with thinner retinal layers and identify retinal layers wherein thinning is predictive of future ocular and systemic conditions.
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Affiliation(s)
- Seyedeh Maryam Zekavat
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Saman Doroodgar Jorshery
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Departments of Computer Science/Medicine, University of Toronto, Toronto, ON M5S 1A1, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON M5G 1M1, Canada
- Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Franziska G Rauscher
- Institute for Medical Informatics, Statistics, and Epidemiology (IMISE), Leipzig University, Leipzig 04107, Germany
- Leipzig Research Centre for Civilization Diseases (LIFE), Leipzig University, Leipzig 04103, Germany
| | - Katrin Horn
- Institute for Medical Informatics, Statistics, and Epidemiology (IMISE), Leipzig University, Leipzig 04107, Germany
| | | | - Satoshi Koyama
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Trang T Nguyen
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Maria C Costanzo
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Dongkeun Jang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Noël P Burtt
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Andreas Kühnapfel
- Institute for Medical Informatics, Statistics, and Epidemiology (IMISE), Leipzig University, Leipzig 04107, Germany
| | - Yusrah Shweikh
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
| | - Yixuan Ye
- Computational Biology and Bioinformatics Program, Yale School of Medicine, New Haven, CT 06511, USA
| | - Vineet Raghu
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Hongyu Zhao
- Computational Biology and Bioinformatics Program, Yale School of Medicine, New Haven, CT 06511, USA
- School of Public Health, Yale University, New Haven, CT 06510, USA
| | - Marzyeh Ghassemi
- Departments of Computer Science/Medicine, University of Toronto, Toronto, ON M5S 1A1, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON M5G 1M1, Canada
- Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Tobias Elze
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
| | - Ayellet V Segrè
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Janey L Wiggs
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Lucian Del Priore
- Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT 06510, USA
| | - Markus Scholz
- Institute for Medical Informatics, Statistics, and Epidemiology (IMISE), Leipzig University, Leipzig 04107, Germany
- Leipzig Research Centre for Civilization Diseases (LIFE), Leipzig University, Leipzig 04103, Germany
| | - Jay C Wang
- Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT 06510, USA
- Northern California Retina Vitreous Associates, Mountain View, CA 94040, USA
| | - Pradeep Natarajan
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Nazlee Zebardast
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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23
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Wang J, Gao Y, Wang F, Zeng S, Li J, Miao H, Wang T, Zeng J, Baptista-Hon D, Monteiro O, Guan T, Cheng L, Lu Y, Luo Z, Li M, Zhu JK, Nie S, Zhang K, Zhou Y. Accurate estimation of biological age and its application in disease prediction using a multimodal image Transformer system. Proc Natl Acad Sci U S A 2024; 121:e2308812120. [PMID: 38190540 PMCID: PMC10801873 DOI: 10.1073/pnas.2308812120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 10/12/2023] [Indexed: 01/10/2024] Open
Abstract
Aging in an individual refers to the temporal change, mostly decline, in the body's ability to meet physiological demands. Biological age (BA) is a biomarker of chronological aging and can be used to stratify populations to predict certain age-related chronic diseases. BA can be predicted from biomedical features such as brain MRI, retinal, or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. In this paper, we developed a multimodal Transformer-based architecture with cross-attention which was able to combine facial, tongue, and retinal images to estimate BA. We trained our model using facial, tongue, and retinal images from 11,223 healthy subjects and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. We validated our approach on a test population of 2,840 individuals with six chronic diseases and obtained significant difference between chronological age and BA (AgeDiff) than that of healthy subjects. We showed that AgeDiff has the potential to be utilized as a standalone biomarker or conjunctively alongside other known factors for risk stratification and progression prediction of chronic diseases. Our results therefore highlight the feasibility of using multimodal images to estimate and interrogate the aging process.
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Affiliation(s)
- Jinzhuo Wang
- Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing100871, China
| | - Yuanxu Gao
- Macau Institute for AI in Medicine and Zhuhai People’s Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Macau999087, China
| | - Fangfei Wang
- Macau Institute for AI in Medicine and Zhuhai People’s Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Macau999087, China
- Guangzhou National Laboratory, Guangzhou510005, China
| | - Simiao Zeng
- Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou510623, China
| | - Jiahui Li
- Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou510623, China
| | - Hanpei Miao
- Dongguan People’s Hospital, Southern Medical University, Dongguan523059, China
| | - Taorui Wang
- Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou510623, China
| | - Jin Zeng
- Guangzhou National Laboratory, Guangzhou510005, China
| | - Daniel Baptista-Hon
- Macau Institute for AI in Medicine and Zhuhai People’s Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Macau999087, China
| | - Olivia Monteiro
- Macau Institute for AI in Medicine and Zhuhai People’s Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Macau999087, China
| | - Taihua Guan
- Guangzhou National Laboratory, Guangzhou510005, China
| | - Linling Cheng
- Macau Institute for AI in Medicine and Zhuhai People’s Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Macau999087, China
| | - Yuxing Lu
- Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing100871, China
| | - Zhengchao Luo
- Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing100871, China
| | - Ming Li
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou325027, China
| | - Jian-kang Zhu
- Institute of Advanced Biotechnology and School of Life Sciences, Southern University of Science and Technology, Shenzhen518055, China
| | - Sheng Nie
- National Clinical Research Center for Kidney Diseases, State Key Laboratory for Organ Failure Research, Nanfang Hospital, Southern Medical University, Guangzhou510515, China
| | - Kang Zhang
- Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing100871, China
- Macau Institute for AI in Medicine and Zhuhai People’s Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Macau999087, China
- Guangzhou National Laboratory, Guangzhou510005, China
- Dongguan People’s Hospital, Southern Medical University, Dongguan523059, China
| | - Yong Zhou
- Clinical Research Institute, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai201620, China
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Yusufu M, Chen Y, Dayimu A, Bulloch G, Jin S, Vingrys AJ, Zhang L, Shang X, Shi D, He M. Retinal Vascular Measurements and Mortality Risk: Evidence From the UK Biobank Study. Transl Vis Sci Technol 2024; 13:2. [PMID: 38165718 PMCID: PMC10773151 DOI: 10.1167/tvst.13.1.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 11/03/2023] [Indexed: 01/04/2024] Open
Abstract
Purpose This study aimed to investigate the association between quantitative retinal vascular measurements and the risk of all-cause and premature mortality. Methods In this population-based cohort study using the UK Biobank data, we employed the Retina-based Microvascular Health Assessment System to assess fundus images for image quality and extracted 392 retinal vascular measurements per fundus image. These measurements encompass six categories of vascular features: caliber, density, length, tortuosity, branching angle, and complexity. Univariate Cox regression models were used to identify potential indicators of mortality risk using data on all-cause and premature mortality from death registries. Multivariate Cox regression models were then used to test these associations while controlling for confounding factors. Results The final analysis included 66,415 participants. After adjusting for demographic, health, and lifestyle factors and genetic risk score, 18 and 10 retinal vascular measurements were significantly associated with all-cause mortality and premature mortality, respectively. In the fully adjusted model, the following measurements of different vascular features were significantly associated with all-cause mortality and premature mortality: arterial bifurcation density (branching angle), number of arterial segments (complexity), interquartile range and median absolute deviation of arterial curve angle (tortuosity), mean and median values of mean pixel widths of all arterial segments in each image (caliber), skeleton density of arteries in macular area (density), and minimum venular arc length (length). Conclusions The study revealed 18 retinal vascular measurements significantly associated with all-cause mortality and 10 associated with premature mortality. Those identified parameters should be further studied for biological mechanisms connecting them to increased mortality risk. Translational Relevance This study identifies retinal biomarkers for increased mortality risk and provides novel targets for investigating the underlying biological mechanisms.
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Affiliation(s)
- Mayinuer Yusufu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Victoria, Australia
| | - Yutong Chen
- Faculty of Medicine, Nursing and Health Science, Monash University, Clayton, Victoria, Australia
| | - Alimu Dayimu
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Gabriella Bulloch
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Victoria, Australia
| | - Shanshan Jin
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Algis J. Vingrys
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Lei Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Xianwen Shang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Victoria, Australia
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Danli Shi
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Mingguang He
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Victoria, Australia
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong
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Kalyakulina A, Yusipov I, Moskalev A, Franceschi C, Ivanchenko M. eXplainable Artificial Intelligence (XAI) in aging clock models. Ageing Res Rev 2024; 93:102144. [PMID: 38030090 DOI: 10.1016/j.arr.2023.102144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/07/2023] [Accepted: 11/23/2023] [Indexed: 12/01/2023]
Abstract
XAI is a rapidly progressing field of machine learning, aiming to unravel the predictions of complex models. XAI is especially required in sensitive applications, e.g. in health care, when diagnosis, recommendations and treatment choices might rely on the decisions made by artificial intelligence systems. AI approaches have become widely used in aging research as well, in particular, in developing biological clock models and identifying biomarkers of aging and age-related diseases. However, the potential of XAI here awaits to be fully appreciated. We discuss the application of XAI for developing the "aging clocks" and present a comprehensive analysis of the literature categorized by the focus on particular physiological systems.
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Affiliation(s)
- Alena Kalyakulina
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia; Research Center for Trusted Artificial Intelligence, The Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow 109004, Russia; Department of Applied Mathematics, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia.
| | - Igor Yusipov
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia; Research Center for Trusted Artificial Intelligence, The Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow 109004, Russia; Department of Applied Mathematics, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia
| | - Alexey Moskalev
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia
| | - Claudio Franceschi
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia
| | - Mikhail Ivanchenko
- Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia; Department of Applied Mathematics, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia
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26
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Danielescu C, Dabija MG, Nedelcu AH, Lupu VV, Lupu A, Ioniuc I, Gîlcă-Blanariu GE, Donica VC, Anton ML, Musat O. Automated Retinal Vessel Analysis Based on Fundus Photographs as a Predictor for Non-Ophthalmic Diseases-Evolution and Perspectives. J Pers Med 2023; 14:45. [PMID: 38248746 PMCID: PMC10817503 DOI: 10.3390/jpm14010045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 12/27/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024] Open
Abstract
The study of retinal vessels in relation to cardiovascular risk has a long history. The advent of a dedicated tool based on digital imaging, i.e., the retinal vessel analyzer, and also other software such as Integrative Vessel Analysis (IVAN), Singapore I Vessel Assessment (SIVA), and Vascular Assessment and Measurement Platform for Images of the Retina (VAMPIRE), has led to the accumulation of a formidable body of evidence regarding the prognostic value of retinal vessel analysis (RVA) for cardiovascular and cerebrovascular disease (including arterial hypertension in children). There is also the potential to monitor the response of retinal vessels to therapies such as physical activity or bariatric surgery. The dynamic vessel analyzer (DVA) remains a unique way of studying neurovascular coupling, helping to understand the pathogenesis of cerebrovascular and neurodegenerative conditions and also being complementary to techniques that measure macrovascular dysfunction. Beyond cardiovascular disease, retinal vessel analysis has shown associations with and prognostic value for neurological conditions, inflammation, kidney function, and respiratory disease. Artificial intelligence (AI) (represented by algorithms such as QUantitative Analysis of Retinal vessel Topology and siZe (QUARTZ), SIVA-DLS (SIVA-deep learning system), and many others) seems efficient in extracting information from fundus photographs, providing prognoses of various general conditions with unprecedented predictive value. The future challenges will be integrating RVA and other qualitative and quantitative risk factors in a unique, comprehensive prediction tool, certainly powered by AI, while building the much-needed acceptance for such an approach inside the medical community and reducing the "black box" effect, possibly by means of saliency maps.
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Affiliation(s)
- Ciprian Danielescu
- Department of Ophthalmology, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania;
| | - Marius Gabriel Dabija
- Department of Surgery II, Discipline of Neurosurgery, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania;
| | - Alin Horatiu Nedelcu
- Department of Morpho-Functional Sciences I, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania;
| | - Vasile Valeriu Lupu
- Department of Pediatrics, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania; (V.V.L.); (I.I.)
| | - Ancuta Lupu
- Department of Pediatrics, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania; (V.V.L.); (I.I.)
| | - Ileana Ioniuc
- Department of Pediatrics, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania; (V.V.L.); (I.I.)
| | | | - Vlad-Constantin Donica
- Doctoral School, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania; (V.-C.D.); (M.-L.A.)
| | - Maria-Luciana Anton
- Doctoral School, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania; (V.-C.D.); (M.-L.A.)
| | - Ovidiu Musat
- Department of Ophthalmology, University of Medicine and Pharmacy “Carol Davila”, 020021 Bucuresti, Romania;
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Tower J. Markers and mechanisms of death in Drosophila. FRONTIERS IN AGING 2023; 4:1292040. [PMID: 38149028 PMCID: PMC10749947 DOI: 10.3389/fragi.2023.1292040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 11/30/2023] [Indexed: 12/28/2023]
Abstract
Parameters correlated with age and mortality in Drosophila melanogaster include decreased negative geotaxis and centrophobism behaviors, decreased climbing and walking speed, and darkened pigments in oenocytes and eye. Cessation of egg laying predicts death within approximately 5 days. Endogenous green fluorescence in eye and body increases hours prior to death. Many flies exhibit erratic movement hours before death, often leading to falls. Loss of intestinal barrier integrity (IBI) is assayed by feeding blue dye ("Smurf" phenotype), and Smurf flies typically die within 0-48 h. Some studies report most flies exhibit Smurf, whereas multiple groups report most flies die without exhibiting Smurf. Transgenic reporters containing heat shock gene promoters and innate immune response gene promoters progressively increase expression with age, and partly predict remaining life span. Innate immune reporters increase with age in every fly, prior to any Smurf phenotype, in presence or absence of antibiotics. Many flies die on their side or supine (on their back) position. The data suggest three mechanisms for death of Drosophila. One is loss of IBI, as revealed by Smurf assay. The second is nervous system malfunction, leading to erratic behavior, locomotor malfunction, and falls. The aged fly is often unable to right itself after a fall to a side-ways or supine position, leading to inability to access the food and subsequent dehydration/starvation. Finally, some flies die upright without Smurf phenotype, suggesting a possible third mechanism. The frequency of these mechanisms varies between strains and culture conditions, which may affect efficacy of life span interventions.
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Affiliation(s)
- John Tower
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA, United States
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28
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Chikumba S, Hu Y, Luo J. Deep learning-based fundus image analysis for cardiovascular disease: a review. Ther Adv Chronic Dis 2023; 14:20406223231209895. [PMID: 38028950 PMCID: PMC10657535 DOI: 10.1177/20406223231209895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 10/03/2023] [Indexed: 12/01/2023] Open
Abstract
It is well established that the retina provides insights beyond the eye. Through observation of retinal microvascular changes, studies have shown that the retina contains information related to cardiovascular disease. Despite the tremendous efforts toward reducing the effects of cardiovascular diseases, they remain a global challenge and a significant public health concern. Conventionally, predicting the risk of cardiovascular disease involves the assessment of preclinical features, risk factors, or biomarkers. However, they are associated with cost implications, and tests to acquire predictive parameters are invasive. Artificial intelligence systems, particularly deep learning (DL) methods applied to fundus images have been generating significant interest as an adjunct assessment tool with the potential of enhancing efforts to prevent cardiovascular disease mortality. Risk factors such as age, gender, smoking status, hypertension, and diabetes can be predicted from fundus images using DL applications with comparable performance to human beings. A clinical change to incorporate DL systems for the analysis of fundus images as an equally good test over more expensive and invasive procedures may require conducting prospective clinical trials to mitigate all the possible ethical challenges and medicolegal implications. This review presents current evidence regarding the use of DL applications on fundus images to predict cardiovascular disease.
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Affiliation(s)
- Symon Chikumba
- Department of Ophthalmology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- Department of Optometry, Faculty of Healthy Sciences, Mzuzu University, Luwinga, Mzuzu, Malawi
| | - Yuqian Hu
- Department of Ophthalmology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Jing Luo
- Department of Ophthalmology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin RD, Changsha, Hunan, China
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29
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Li R, Chen W, Li M, Wang R, Zhao L, Lin Y, Chen X, Shang Y, Tu X, Lin D, Wu X, Lin Z, Xu A, Wang X, Wang D, Zhang X, Dongye M, Huang Y, Chen C, Zhu Y, Liu C, Hu Y, Zhao L, Ouyang H, Li M, Li X, Lin H. LensAge index as a deep learning-based biological age for self-monitoring the risks of age-related diseases and mortality. Nat Commun 2023; 14:7126. [PMID: 37932255 PMCID: PMC10628111 DOI: 10.1038/s41467-023-42934-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 10/25/2023] [Indexed: 11/08/2023] Open
Abstract
Age is closely related to human health and disease risks. However, chronologically defined age often disagrees with biological age, primarily due to genetic and environmental variables. Identifying effective indicators for biological age in clinical practice and self-monitoring is important but currently lacking. The human lens accumulates age-related changes that are amenable to rapid and objective assessment. Here, using lens photographs from 20 to 96-year-olds, we develop LensAge to reflect lens aging via deep learning. LensAge is closely correlated with chronological age of relatively healthy individuals (R2 > 0.80, mean absolute errors of 4.25 to 4.82 years). Among the general population, we calculate the LensAge index by contrasting LensAge and chronological age to reflect the aging rate relative to peers. The LensAge index effectively reveals the risks of age-related eye and systemic disease occurrence, as well as all-cause mortality. It outperforms chronological age in reflecting age-related disease risks (p < 0.001). More importantly, our models can conveniently work based on smartphone photographs, suggesting suitability for routine self-examination of aging status. Overall, our study demonstrates that the LensAge index may serve as an ideal quantitative indicator for clinically assessing and self-monitoring biological age in humans.
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Affiliation(s)
- Ruiyang Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Wenben Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Mingyuan Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Ruixin Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Lanqin Zhao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Yuanfan Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Xinwei Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Yuanjun Shang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Xueer Tu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Duoru Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Xiaohang Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Zhenzhe Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Andi Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Xun Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Dongni Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Xulin Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Meimei Dongye
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Yunjian Huang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Chuan Chen
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Yi Zhu
- Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Chunqiao Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Youjin Hu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Ling Zhao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Hong Ouyang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Miaoxin Li
- Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Sun Yat-sen University, Guangzhou, China
| | - Xuri Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
- Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
- Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Haikou, China.
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30
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Chen R, Zhang J, Shang X, Wang W, He M, Zhu Z. Central obesity and its association with retinal age gap: insights from the UK Biobank study. Int J Obes (Lond) 2023; 47:979-985. [PMID: 37491535 PMCID: PMC10511312 DOI: 10.1038/s41366-023-01345-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 07/02/2023] [Accepted: 07/06/2023] [Indexed: 07/27/2023]
Abstract
BACKGROUND Conflicting evidence exists on the association between ageing and obesity. Retinal age derived from fundus images has been validated as a novel biomarker of ageing. In this study, we aim to investigate the association between different anthropometric phenotypes based on body mass index (BMI) and waist circumference (WC) and the retinal age gap (retinal age minus chronological age). METHODS A total of 35,550 participants with BMI, WC and qualified retinal imaging data available were included to investigate the association between anthropometric groups and retinal ageing. Participants were stratified into 7 different body composition groups based on BMI and WC (Normal-weight/Normal WC, Overweight/Normal WC, Mild obesity/Normal WC, Normal-weight/High WC, Overweight/High WC, Mild obesity/High WC, and Severe obesity/High WC). Linear regression and logistic regression models were fitted to investigate the association between the seven anthropometric groups and retinal age gap as continuous and categorical outcomes, respectively. RESULTS A total of 35,550 participants (55.6% females) with a mean age 56.8 ± 8.04 years were included in the study. Individuals in the Overweight/High WC, Mild obesity/High WC and Severe obesity/High WC groups were associated with an increase in the retinal age gap, compared with those in the Normal Weight/Normal WC group (β = 0.264, 95% CI: 0.105-0.424, P =0.001; β = 0.226, 95% CI: 0.082-0.371, P = 0.002; β = 0.273, 95% CI: 0.081-0.465, P = 0.005; respectively) in fully adjusted models. Similar findings were noted in the association between the anthropometric groups and retinal ageing process as a categorical outcome. CONCLUSION A significant positive association exists between central obesity and accelerated ageing indexed by retinal age gaps, highlighting the significance of maintaining a healthy body shape.
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Affiliation(s)
- Ruiye Chen
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, VIC, Australia
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, VIC, Australia
| | - Junyao Zhang
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, VIC, Australia
| | - Xianwen Shang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, VIC, Australia
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, VIC, Australia
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
| | - Mingguang He
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China.
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, VIC, Australia.
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, VIC, Australia.
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
| | - Zhuoting Zhu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China.
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, VIC, Australia.
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, VIC, Australia.
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Shah M, de A Inácio MH, Lu C, Schiratti PR, Zheng SL, Clement A, de Marvao A, Bai W, King AP, Ware JS, Wilkins MR, Mielke J, Elci E, Kryukov I, McGurk KA, Bender C, Freitag DF, O'Regan DP. Environmental and genetic predictors of human cardiovascular ageing. Nat Commun 2023; 14:4941. [PMID: 37604819 PMCID: PMC10442405 DOI: 10.1038/s41467-023-40566-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 08/02/2023] [Indexed: 08/23/2023] Open
Abstract
Cardiovascular ageing is a process that begins early in life and leads to a progressive change in structure and decline in function due to accumulated damage across diverse cell types, tissues and organs contributing to multi-morbidity. Damaging biophysical, metabolic and immunological factors exceed endogenous repair mechanisms resulting in a pro-fibrotic state, cellular senescence and end-organ damage, however the genetic architecture of cardiovascular ageing is not known. Here we use machine learning approaches to quantify cardiovascular age from image-derived traits of vascular function, cardiac motion and myocardial fibrosis, as well as conduction traits from electrocardiograms, in 39,559 participants of UK Biobank. Cardiovascular ageing is found to be significantly associated with common or rare variants in genes regulating sarcomere homeostasis, myocardial immunomodulation, and tissue responses to biophysical stress. Ageing is accelerated by cardiometabolic risk factors and we also identify prescribed medications that are potential modifiers of ageing. Through large-scale modelling of ageing across multiple traits our results reveal insights into the mechanisms driving premature cardiovascular ageing and reveal potential molecular targets to attenuate age-related processes.
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Affiliation(s)
- Mit Shah
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Marco H de A Inácio
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Chang Lu
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | | | - Sean L Zheng
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Adam Clement
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Antonio de Marvao
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Wenjia Bai
- Department of Computing, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Andrew P King
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - James S Ware
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Martin R Wilkins
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Johanna Mielke
- Bayer AG, Research & Development, Pharmaceuticals, Wuppertal, Germany
| | - Eren Elci
- Bayer AG, Research & Development, Pharmaceuticals, Wuppertal, Germany
| | - Ivan Kryukov
- Bayer AG, Research & Development, Pharmaceuticals, Wuppertal, Germany
| | - Kathryn A McGurk
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Christian Bender
- Bayer AG, Research & Development, Pharmaceuticals, Wuppertal, Germany
| | - Daniel F Freitag
- Bayer AG, Research & Development, Pharmaceuticals, Wuppertal, Germany
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, UK.
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32
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Hu W, Yii FSL, Chen R, Zhang X, Shang X, Kiburg K, Woods E, Vingrys A, Zhang L, Zhu Z, He M. A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images. Transl Vis Sci Technol 2023; 12:14. [PMID: 37440249 PMCID: PMC10353749 DOI: 10.1167/tvst.12.7.14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 06/08/2023] [Indexed: 07/14/2023] Open
Abstract
Purpose The purpose of this study was to perform a systematic review and meta-analysis to synthesize evidence from studies using deep learning (DL) to predict cardiovascular disease (CVD) risk from retinal images. Methods A systematic literature search was performed in MEDLINE, Scopus, and Web of Science up to June 2022. We extracted data pertaining to predicted outcomes, model development, and validation and model performance metrics. Included studies were graded using the Quality Assessment of Diagnostic Accuracies Studies 2 tool. Model performance was pooled across eligible studies using a random-effects meta-analysis model. Results A total of 26 studies were included in the analysis. There were 42 CVD risk-related outcomes predicted from retinal images were identified, including 33 CVD risk factors, 4 cardiac imaging biomarkers, 2 CVD risk scores, the presence of CVD, and incident CVD. Three studies that aimed to predict the development of future CVD events reported an area under the receiver operating curve (AUROC) between 0.68 and 0.81. Models that used retinal images as input data had a pooled mean absolute error of 3.19 years (95% confidence interval [CI] = 2.95-3.43) for age prediction; a pooled AUROC of 0.96 (95% CI = 0.95-0.97) for gender classification; a pooled AUROC of 0.80 (95% CI = 0.73-0.86) for diabetes detection; and a pooled AUROC of 0.86 (95% CI = 0.81-0.92) for the detection of chronic kidney disease. We observed a high level of heterogeneity and variation in study designs. Conclusions Although DL models appear to have reasonably good performance when it comes to predicting CVD risk, further work is necessary to evaluate the real-world applicability and predictive accuracy. Translational Relevance DL-based CVD risk assessment from retinal images holds great promise to be translated to clinical practice as a novel approach for CVD risk assessment, given its simple, quick, and noninvasive nature.
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Affiliation(s)
- Wenyi Hu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Fabian S. L. Yii
- Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK
- Curle Ophthalmology Laboratory, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK
| | - Ruiye Chen
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Xinyu Zhang
- Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xianwen Shang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
| | - Katerina Kiburg
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
| | - Ekaterina Woods
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
| | - Algis Vingrys
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Optometry and Vision Sciences, The University of Melbourne, Melbourne, Australia
| | - Lei Zhang
- Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Zhuoting Zhu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
| | - Mingguang He
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
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33
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Ahadi S, Wilson KA, Babenko B, McLean CY, Bryant D, Pritchard O, Kumar A, Carrera EM, Lamy R, Stewart JM, Varadarajan A, Berndl M, Kapahi P, Bashir A. Longitudinal fundus imaging and its genome-wide association analysis provide evidence for a human retinal aging clock. eLife 2023; 12:e82364. [PMID: 36975205 PMCID: PMC10110236 DOI: 10.7554/elife.82364] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 03/22/2023] [Indexed: 03/29/2023] Open
Abstract
Biological age, distinct from an individual's chronological age, has been studied extensively through predictive aging clocks. However, these clocks have limited accuracy in short time-scales. Here we trained deep learning models on fundus images from the EyePACS dataset to predict individuals' chronological age. Our retinal aging clocking, 'eyeAge', predicted chronological age more accurately than other aging clocks (mean absolute error of 2.86 and 3.30 years on quality-filtered data from EyePACS and UK Biobank, respectively). Additionally, eyeAge was independent of blood marker-based measures of biological age, maintaining an all-cause mortality hazard ratio of 1.026 even when adjusted for phenotypic age. The individual-specific nature of eyeAge was reinforced via multiple GWAS hits in the UK Biobank cohort. The top GWAS locus was further validated via knockdown of the fly homolog, Alk, which slowed age-related decline in vision in flies. This study demonstrates the potential utility of a retinal aging clock for studying aging and age-related diseases and quantitatively measuring aging on very short time-scales, opening avenues for quick and actionable evaluation of gero-protective therapeutics.
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Affiliation(s)
- Sara Ahadi
- Google ResearchMountain ViewUnited States
| | | | | | | | | | | | - Ajay Kumar
- Department of Biophysics, Post Graduate Institute of Medical Education and ResearchChandigarhIndia
| | | | - Ricardo Lamy
- Department of Ophthalmology, Zuckerberg San Francisco General Hospital and Trauma CenterSan FranciscoUnited States
| | - Jay M Stewart
- Department of Ophthalmology, University of California, San FranciscoSan FranciscoUnited States
| | | | | | - Pankaj Kapahi
- Buck Institute for Research on AgingNovatoUnited States
| | - Ali Bashir
- Google ResearchMountain ViewUnited States
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34
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Arnould L, Meriaudeau F, Guenancia C, Germanese C, Delcourt C, Kawasaki R, Cheung CY, Creuzot-Garcher C, Grzybowski A. Using Artificial Intelligence to Analyse the Retinal Vascular Network: The Future of Cardiovascular Risk Assessment Based on Oculomics? A Narrative Review. Ophthalmol Ther 2023; 12:657-674. [PMID: 36562928 PMCID: PMC10011267 DOI: 10.1007/s40123-022-00641-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022] Open
Abstract
The healthcare burden of cardiovascular diseases remains a major issue worldwide. Understanding the underlying mechanisms and improving identification of people with a higher risk profile of systemic vascular disease through noninvasive examinations is crucial. In ophthalmology, retinal vascular network imaging is simple and noninvasive and can provide in vivo information of the microstructure and vascular health. For more than 10 years, different research teams have been working on developing software to enable automatic analysis of the retinal vascular network from different imaging techniques (retinal fundus photographs, OCT angiography, adaptive optics, etc.) and to provide a description of the geometric characteristics of its arterial and venous components. Thus, the structure of retinal vessels could be considered a witness of the systemic vascular status. A new approach called "oculomics" using retinal image datasets and artificial intelligence algorithms recently increased the interest in retinal microvascular biomarkers. Despite the large volume of associated research, the role of retinal biomarkers in the screening, monitoring, or prediction of systemic vascular disease remains uncertain. A PubMed search was conducted until August 2022 and yielded relevant peer-reviewed articles based on a set of inclusion criteria. This literature review is intended to summarize the state of the art in oculomics and cardiovascular disease research.
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Affiliation(s)
- Louis Arnould
- Ophthalmology Department, Dijon University Hospital, 14 Rue Paul Gaffarel, 21079, Dijon CEDEX, France. .,University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR U1219, 33000, Bordeaux, France.
| | - Fabrice Meriaudeau
- Laboratory ImViA, IFTIM, Université Bourgogne Franche-Comté, 21078, Dijon, France
| | - Charles Guenancia
- Pathophysiology and Epidemiology of Cerebro-Cardiovascular Diseases, (EA 7460), Faculty of Health Sciences, Université de Bourgogne Franche-Comté, Dijon, France.,Cardiology Department, Dijon University Hospital, Dijon, France
| | - Clément Germanese
- Ophthalmology Department, Dijon University Hospital, 14 Rue Paul Gaffarel, 21079, Dijon CEDEX, France
| | - Cécile Delcourt
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR U1219, 33000, Bordeaux, France
| | - Ryo Kawasaki
- Artificial Intelligence Center for Medical Research and Application, Osaka University Hospital, Osaka, Japan
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Catherine Creuzot-Garcher
- Ophthalmology Department, Dijon University Hospital, 14 Rue Paul Gaffarel, 21079, Dijon CEDEX, France.,Centre des Sciences du Goût et de l'Alimentation, AgroSup Dijon, CNRS, INRAE, Université Bourgogne Franche-Comté, Dijon, France
| | - Andrzej Grzybowski
- Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland.,Institute for Research in Ophthalmology, Poznan, Poland
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35
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Chan YK, Cheng CY, Sabanayagam C. Eyes as the windows into cardiovascular disease in the era of big data. Taiwan J Ophthalmol 2023; 13:151-167. [PMID: 37484607 PMCID: PMC10361436 DOI: 10.4103/tjo.tjo-d-23-00018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 04/11/2023] [Indexed: 07/25/2023] Open
Abstract
Cardiovascular disease (CVD) is a major cause of mortality and morbidity worldwide and imposes significant socioeconomic burdens, especially with late diagnoses. There is growing evidence of strong correlations between ocular images, which are information-dense, and CVD progression. The accelerating development of deep learning algorithms (DLAs) is a promising avenue for research into CVD biomarker discovery, early CVD diagnosis, and CVD prognostication. We review a selection of 17 recent DLAs on the less-explored realm of DL as applied to ocular images to produce CVD outcomes, potential challenges in their clinical deployment, and the path forward. The evidence for CVD manifestations in ocular images is well documented. Most of the reviewed DLAs analyze retinal fundus photographs to predict CV risk factors, in particular hypertension. DLAs can predict age, sex, smoking status, alcohol status, body mass index, mortality, myocardial infarction, stroke, chronic kidney disease, and hematological disease with significant accuracy. While the cardio-oculomics intersection is now burgeoning, very much remain to be explored. The increasing availability of big data, computational power, technological literacy, and acceptance all prime this subfield for rapid growth. We pinpoint the specific areas of improvement toward ubiquitous clinical deployment: increased generalizability, external validation, and universal benchmarking. DLAs capable of predicting CVD outcomes from ocular inputs are of great interest and promise to individualized precision medicine and efficiency in the provision of health care with yet undetermined real-world efficacy with impactful initial results.
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Affiliation(s)
- Yarn Kit Chan
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
| | - Ching-Yu Cheng
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Center for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Charumathi Sabanayagam
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
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36
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Chen R, Xu J, Shang X, Bulloch G, He M, Wang W, Zhu Z. Association between cardiovascular health metrics and retinal ageing. GeroScience 2023:10.1007/s11357-023-00743-3. [PMID: 36930331 PMCID: PMC10400488 DOI: 10.1007/s11357-023-00743-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/25/2023] [Indexed: 03/18/2023] Open
Abstract
The study aims to investigate associations between cardiovascular health (CVH) metrics and retinal ageing indexed by retinal age gap. A total of 26,354 participants from the UK Biobank study with available CVH metrics and qualified retinal imaging were included in the present analysis. CVH included 7 metrics (smoking, physical activity, diet, body mass index [BMI], total cholesterol, blood pressure [BP], blood glucose). These were summarized to classify the overall CVH as poor (0-7), intermediate (8-10) or ideal (11-14). Retinal age gap was defined as the difference between biological age predicted by fundus images and chronological age. Accelerated and non-accelerated retinal ageing was defined if retinal age gap was in the upper or lower 50% quantiles of the study population, respectively. Linear and logistic regression models estimated the association of overall CVH and each metric of CVH with retinal age gap respectively. Our results showed that in the fully adjusted model, each one-unit score increase in overall CVH was negatively associated with retinal age gap (odds ratio [OR] = 0.89, 95% confidence interval [CI]: 0.87-0.92, P < 0.001). Compared with poor overall CVH, people with intermediate and ideal overall CVH had significantly lower retinal age gap (OR = 0.76, 95%CI: 0.67-0.85, P < 0.001; OR = 0.58, 95%CI: 0.50-0.67, P < 0.001). Similar associations were found between overall CVH and accelerated retinal ageing. CVH metrics including smoking, BMI, BP, and blood glucose were also significantly associated with higher retinal age gap. Taken together, we found a significant and inverse dose-response association between CVH metrics and retinal age gap, indicating that maintaining healthy metrics especially smoking, BMI, BP, and blood glucose may be crucial to slow down biological ageing.
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Affiliation(s)
- Ruiye Chen
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China.,Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia.,Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
| | - Jinyi Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xianwen Shang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China.,Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia.,Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
| | - Gabriella Bulloch
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
| | - Mingguang He
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China. .,Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia. .,Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia. .,State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
| | - Zhuoting Zhu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China. .,Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, Australia. .,Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia.
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37
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Marquié M, García-Sánchez A, Alarcón-Martín E, Martínez J, Castilla-Martí M, Castilla-Martí L, Orellana A, Montrreal L, de Rojas I, García-González P, Puerta R, Olivé C, Cano A, Hernández I, Rosende-Roca M, Vargas L, Tartari JP, Esteban-De Antonio E, Bojaryn U, Ricciardi M, Ariton DM, Pytel V, Alegret M, Ortega G, Espinosa A, Pérez-Cordón A, Sanabria Á, Muñoz N, Lleonart N, Aguilera N, Tárraga L, Valero S, Ruiz A, Boada M. Macular vessel density in the superficial plexus is not associated to cerebrospinal fluid core biomarkers for Alzheimer's disease in individuals with mild cognitive impairment: The NORFACE cohort. Front Neurosci 2023; 17:1076177. [PMID: 36908784 PMCID: PMC9995931 DOI: 10.3389/fnins.2023.1076177] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 02/07/2023] [Indexed: 02/25/2023] Open
Abstract
Background Optical coherence tomography angiography (OCT-A) is a novel method in the dementia field that allows the detection of retinal vascular changes. The comparison of OCT-A measures with established Alzheimer's disease (AD)-related biomarkers is essential to validate the former as a marker of cerebrovascular impairment in the AD continuum. We aimed to investigate the association of macular vessel density (VD) in the superficial plexus quantified by OCT-A with the AT(N) classification based on cerebrospinal fluid (CSF) Aβ1-42, p181-tau and t-tau measurements in individuals with mild cognitive impairment (MCI). Materials and methods Clinical, demographic, ophthalmological, OCT-A and CSF core biomarkers for AD data from the Neuro-ophthalmology Research at Fundació ACE (NORFACE) project were analyzed. Differences in macular VD in four quadrants (superior, nasal, inferior, and temporal) among three AT(N) groups [Normal, Alzheimer and Suspected non-Alzheimer pathology (SNAP)] were assessed in a multivariate regression model, adjusted for age, APOE ε4 status, hypertension, diabetes mellitus, dyslipidemia, heart disease, chronic obstructive pulmonary disease and smoking habit, using the Normal AT(N) group as the reference category. Results The study cohort comprised 144 MCI participants: 66 Normal AT(N), 45 Alzheimer AT(N) and 33 SNAP AT(N). Regression analysis showed no significant association of the AT(N) groups with any of the regional macular VD measures (all, p > 0.16). The interaction between sex and AT(N) groups had no effect on differentiating VD. Lastly, CSF Aβ1-42, p181-tau and t-tau measures were not correlated to VD (all r < 0.13; p > 0.13). Discussion Our study showed that macular VD measures were not associated with the AT(N) classification based on CSF biomarkers in patients with MCI, and did not differ between AD and other underlying causes of cognitive decline in our cohort.
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Affiliation(s)
- Marta Marquié
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain.,Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Ainhoa García-Sánchez
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - Emilio Alarcón-Martín
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - Joan Martínez
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - Miguel Castilla-Martí
- Clínica Oftalmológica Dr. Castilla, Barcelona, Spain.,Vista Alpina Eye Clinic, Visp, Switzerland
| | - Luis Castilla-Martí
- Ph.D. Programme in Surgery and Morphological Sciences, Universitat Autònoma de Barcelona, Barcelona, Spain.,Hôpital Ophtalmique Jules-Gonin, Fondation Asile des Aveugles, University of Lausanne, Lausanne, Switzerland
| | - Adelina Orellana
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain.,Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Laura Montrreal
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - Itziar de Rojas
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain.,Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Pablo García-González
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain.,Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Raquel Puerta
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - Clàudia Olivé
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - Amanda Cano
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain.,Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Isabel Hernández
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - Maitée Rosende-Roca
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - Liliana Vargas
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - Juan Pablo Tartari
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | | | - Urszula Bojaryn
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - Mario Ricciardi
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - Diana M Ariton
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - Vanesa Pytel
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - Montserrat Alegret
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain.,Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Gemma Ortega
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain.,Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Ana Espinosa
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain.,Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Alba Pérez-Cordón
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - Ángela Sanabria
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain.,Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Nathalia Muñoz
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - Núria Lleonart
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - Núria Aguilera
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - Lluís Tárraga
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain.,Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Sergi Valero
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain.,Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Agustín Ruiz
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain.,Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Mercè Boada
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain.,Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
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Application of Deep Learning to Retinal-Image-Based Oculomics for Evaluation of Systemic Health: A Review. J Clin Med 2022; 12:jcm12010152. [PMID: 36614953 PMCID: PMC9821402 DOI: 10.3390/jcm12010152] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/17/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022] Open
Abstract
The retina is a window to the human body. Oculomics is the study of the correlations between ophthalmic biomarkers and systemic health or disease states. Deep learning (DL) is currently the cutting-edge machine learning technique for medical image analysis, and in recent years, DL techniques have been applied to analyze retinal images in oculomics studies. In this review, we summarized oculomics studies that used DL models to analyze retinal images-most of the published studies to date involved color fundus photographs, while others focused on optical coherence tomography images. These studies showed that some systemic variables, such as age, sex and cardiovascular disease events, could be consistently robustly predicted, while other variables, such as thyroid function and blood cell count, could not be. DL-based oculomics has demonstrated fascinating, "super-human" predictive capabilities in certain contexts, but it remains to be seen how these models will be incorporated into clinical care and whether management decisions influenced by these models will lead to improved clinical outcomes.
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Zhang S, Chen R, Wang Y, Hu W, Kiburg KV, Zhang J, Yang X, Yu H, He M, Wang W, Zhu Z. Association of Retinal Age Gap and Risk of Kidney Failure: A UK Biobank Study. Am J Kidney Dis 2022; 81:537-544.e1. [PMID: 36481699 DOI: 10.1053/j.ajkd.2022.09.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 09/25/2022] [Indexed: 12/12/2022]
Abstract
RATIONALE & OBJECTIVE The incidence of kidney failure is known to increase with age. We have previously developed and validated the use of retinal age based on fundus images as a biomarker of aging. However, the association of retinal age with kidney failure is not clear. We investigated the association of retinal age gap (the difference between retinal age and chronological age) with future risk of kidney failure. STUDY DESIGN Prospective cohort study. SETTING & PARTICIPANTS 11,052 UK Biobank study participants without any reported disease for characterizing retinal age in a deep learning algorithm. 35,864 other participants with retinal images and no kidney failure were followed to assess the association between retinal age gap and the risk of kidney failure. EXPOSURE Retinal age gap, defined as the difference between model-based retinal age and chronological age. OUTCOME Incident kidney failure. ANALYTICAL APPROACH A deep learning prediction model used to characterize retinal age based on retinal images and chronological age, and Cox proportional hazards regression models to investigate the association of retinal age gap with incident kidney failure. RESULTS After a median follow-up period of 11 (IQR, 10.89-11.14) years, 115 (0.32%) participants were diagnosed with incident kidney failure. Each 1-year greater retinal age gap at baseline was independently associated with a 10% increase in the risk of incident kidney failure (HR, 1.10 [95% CI, 1.03-1.17]; P=0.003). Participants with retinal age gaps in the fourth (highest) quartile had a significantly higher risk of incident kidney failure compared with those in the first quartile (HR, 2.77 [95% CI, 1.29-5.93]; P=0.009). LIMITATIONS Limited generalizability related to the composition of participants in the UK Biobank study. CONCLUSIONS Retinal age gap was significantly associated with incident kidney failure and may be a promising noninvasive predictive biomarker for incident kidney failure.
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Affiliation(s)
- Shiran Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, and Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, People's Republic of China
| | - Ruiye Chen
- Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, Australia; Department of Surgery, Ophthalmology, University of Melbourne, Melbourne, Australia
| | - Yan Wang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, People's Republic of China
| | - Wenyi Hu
- Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, Australia; Department of Surgery, Ophthalmology, University of Melbourne, Melbourne, Australia
| | - Katerina V Kiburg
- Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, Australia
| | - Junyao Zhang
- Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, Australia
| | - Xiaohong Yang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, People's Republic of China
| | - Honghua Yu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, People's Republic of China
| | - Mingguang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, and Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, People's Republic of China; Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, People's Republic of China; Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, Australia; Department of Surgery, Ophthalmology, University of Melbourne, Melbourne, Australia.
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, and Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, People's Republic of China
| | - Zhuoting Zhu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, People's Republic of China; Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, Australia; Department of Surgery, Ophthalmology, University of Melbourne, Melbourne, Australia.
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Hagio T, Murthy VL. Deep learning: Opening a third eye to myocardial perfusion imaging. J Nucl Cardiol 2022; 29:3311-3314. [PMID: 35554868 DOI: 10.1007/s12350-022-02959-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 03/09/2022] [Indexed: 01/18/2023]
Affiliation(s)
- Tomoe Hagio
- INVIA Medical Imaging Solutions, 3025 Boardwalk St, Suite 200, Ann Arbor, MI, 48108, USA.
| | - Venkatesh L Murthy
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
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Zhu Z, Hu W, Chen R, Xiong R, Wang W, Shang X, Chen Y, Kiburg K, Shi D, He S, Huang Y, Zhang X, Tang S, Zeng J, Yu H, Yang X, He M. Retinal age gap as a predictive biomarker of stroke risk. BMC Med 2022; 20:466. [PMID: 36447293 PMCID: PMC9710167 DOI: 10.1186/s12916-022-02620-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 10/20/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND The aim of this study is to investigate the association of retinal age gap with the risk of incident stroke and its predictive value for incident stroke. METHODS A total of 80,169 fundus images from 46,969 participants in the UK Biobank cohort met the image quality standard. A deep learning model was constructed based on 19,200 fundus images of 11,052 disease-free participants at baseline for age prediction. Retinal age gap (retinal age predicted based on the fundus image minus chronological age) was generated for the remaining 35,917 participants. Stroke events were determined by data linkage to hospital records on admissions and diagnoses, and national death registers, whichever occurred earliest. Cox proportional hazards regression models were used to estimate the effect of retinal age gap on risk of stroke. Logistic regression models were used to estimate the predictive value of retinal age and well-established risk factors in 10-year stroke risk. RESULTS A total of 35,304 participants without history of stroke at baseline were included. During a median follow-up of 5.83 years, 282 (0.80%) participants had stroke events. In the fully adjusted model, each one-year increase in the retinal age gap was associated with a 4% increase in the risk of stroke (hazard ratio [HR] = 1.04, 95% confidence interval [CI]: 1.00-1.08, P = 0.029). Compared to participants with retinal age gap in the first quintile, participants with retinal age gap in the fifth quintile had significantly higher risks of stroke events (HR = 2.37, 95% CI: 1.37-4.10, P = 0.002). The predictive capability of retinal age alone was comparable to the well-established risk factor-based model (AUC=0.676 vs AUC=0.661, p=0.511). CONCLUSIONS We found that retinal age gap was significantly associated with incident stroke, implying the potential of retinal age gap as a predictive biomarker of stroke risk.
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Affiliation(s)
- Zhuoting Zhu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China.,Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, Australia.,Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
| | - Wenyi Hu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China.,Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, Australia.,Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
| | - Ruiye Chen
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China.,Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, Australia.,Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
| | - Ruilin Xiong
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xianwen Shang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China.,Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, Australia.,Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
| | - Yifan Chen
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China.,John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Katerina Kiburg
- Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, Australia
| | - Danli Shi
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Shuang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yu Huang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Xueli Zhang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Shulin Tang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Jieshan Zeng
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Honghua Yu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Xiaohong Yang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China.
| | - Mingguang He
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China. .,Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, Australia. .,Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia. .,State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
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An evaluation of aging measures: from biomarkers to clocks. Biogerontology 2022; 24:303-328. [PMID: 36418661 DOI: 10.1007/s10522-022-09997-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 10/21/2022] [Indexed: 11/25/2022]
Abstract
With the increasing number of aged population and growing burden of healthy aging demands, a rational standard for evaluation aging is in urgent need. The advancement of medical testing technology and the prospering of artificial intelligence make it possible to evaluate the biological status of aging from a more comprehensive view. In this review, we introduced common aging biomarkers and concluded several famous aging clocks. Aging biomarkers reflect changes in the organism at a molecular or cellular level over time while aging clocks tend to be more of a generalization of the overall state of the organism. We expect to construct a framework for aging evaluation measurement from both micro and macro perspectives. Especially, population-specific aging clocks and multi-omics aging clocks may better fit the demands to evaluate aging in a comprehensive and multidimensional manner and make a detailed classification to represent different aging rates at tissue/organ levels. This framework will promisingly provide a crucial basis for disease diagnosis and intervention assessment in geroscience.
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Nguyen TX, Ran AR, Hu X, Yang D, Jiang M, Dou Q, Cheung CY. Federated Learning in Ocular Imaging: Current Progress and Future Direction. Diagnostics (Basel) 2022; 12:2835. [PMID: 36428895 PMCID: PMC9689273 DOI: 10.3390/diagnostics12112835] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/11/2022] [Accepted: 11/14/2022] [Indexed: 11/18/2022] Open
Abstract
Advances in artificial intelligence deep learning (DL) have made tremendous impacts on the field of ocular imaging over the last few years. Specifically, DL has been utilised to detect and classify various ocular diseases on retinal photographs, optical coherence tomography (OCT) images, and OCT-angiography images. In order to achieve good robustness and generalisability of model performance, DL training strategies traditionally require extensive and diverse training datasets from various sites to be transferred and pooled into a "centralised location". However, such a data transferring process could raise practical concerns related to data security and patient privacy. Federated learning (FL) is a distributed collaborative learning paradigm which enables the coordination of multiple collaborators without the need for sharing confidential data. This distributed training approach has great potential to ensure data privacy among different institutions and reduce the potential risk of data leakage from data pooling or centralisation. This review article aims to introduce the concept of FL, provide current evidence of FL in ocular imaging, and discuss potential challenges as well as future applications.
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Affiliation(s)
- Truong X. Nguyen
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - An Ran Ran
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Xiaoyan Hu
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Dawei Yang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Meirui Jiang
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Qi Dou
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Carol Y. Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
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Blasiak J, Sobczuk P, Pawlowska E, Kaarniranta K. Interplay between aging and other factors of the pathogenesis of age-related macular degeneration. Ageing Res Rev 2022; 81:101735. [PMID: 36113764 DOI: 10.1016/j.arr.2022.101735] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 08/03/2022] [Accepted: 09/12/2022] [Indexed: 01/31/2023]
Abstract
Age-related macular degeneration (AMD) is a complex eye disease with the retina as the target tissue and aging as per definition the most serious risk factor. However, the retina contains over 60 kinds of cells that form different structures, including the neuroretina and retinal pigment epithelium (RPE) which can age at different rates. Other established or putative AMD risk factors can differentially affect the neuroretina and RPE and can differently interplay with aging of these structures. The occurrence of β-amyloid plaques and increased levels of cholesterol in AMD retinas suggest that AMD may be a syndrome of accelerated brain aging. Therefore, the question about the real meaning of age in AMD is justified. In this review we present and update information on how aging may interplay with some aspects of AMD pathogenesis, such as oxidative stress, amyloid beta formation, circadian rhythm, metabolic aging and cellular senescence. Also, we show how this interplay can be specific for photoreceptors, microglia cells and RPE cells as well as in Bruch's membrane and the choroid. Therefore, the process of aging may differentially affect different retinal structures. As an accurate quantification of biological aging is important for risk stratification and early intervention for age-related diseases, the determination how photoreceptors, microglial and RPE cells age in AMD may be helpful for a precise diagnosis and treatment of this largely untreatable disease.
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Affiliation(s)
- Janusz Blasiak
- Department of Molecular Genetics, University of Lodz, Pomorska 141/143, 90-236, Lodz, Poland.
| | - Piotr Sobczuk
- Emergency Medicine and Disaster Medicine Department, Medical University of Lodz, Pomorska 251, 92-209 Lodz, Poland; Department of Orthopaedics and Traumatology, Polish Mothers' Memorial Hospital - Research Institute, Rzgowska 281, 93-338 Lodz, Poland
| | - Elzbieta Pawlowska
- Department of Pediatric Dentistry, Medical University of Lodz, Pomorska 251, 92-216 Lodz, Poland
| | - Kai Kaarniranta
- Department of Ophthalmology, Institute of Clinical Medicine, University of Eastern Finland, P.O. Box 1627, FI-70211, Kuopio, Finland; Department of Ophthalmology, Kuopio University Hospital, KYS, P.O. Box 100, FI-70029 Finland
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Ferro Desideri L, Rutigliani C, Corazza P, Nastasi A, Roda M, Nicolo M, Traverso CE, Vagge A. The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases. JOURNAL OF OPTOMETRY 2022; 15 Suppl 1:S50-S57. [PMID: 36216736 PMCID: PMC9732476 DOI: 10.1016/j.optom.2022.08.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/14/2022] [Accepted: 08/16/2022] [Indexed: 06/16/2023]
Abstract
In recent years, the role of artificial intelligence (AI) and deep learning (DL) models is attracting increasing global interest in the field of ophthalmology. DL models are considered the current state-of-art among the AI technologies. In fact, DL systems have the capability to recognize, quantify and describe pathological clinical features. Their role is currently being investigated for the early diagnosis and management of several retinal diseases and glaucoma. The application of DL models to fundus photographs, visual fields and optical coherence tomography (OCT) imaging has provided promising results in the early detection of diabetic retinopathy (DR), wet age-related macular degeneration (w-AMD), retinopathy of prematurity (ROP) and glaucoma. In this review we analyze the current evidence of AI applied to these ocular diseases, as well as discuss the possible future developments and potential clinical implications, without neglecting the present limitations and challenges in order to adopt AI and DL models as powerful tools in the everyday routine clinical practice.
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Affiliation(s)
- Lorenzo Ferro Desideri
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy.
| | | | - Paolo Corazza
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy
| | | | - Matilde Roda
- Ophthalmology Unit, Department of Experimental, Diagnostic and Specialty Medicine (DIMES), Alma Mater Studiorum University of Bologna and S.Orsola-Malpighi Teaching Hospital, Bologna, Italy
| | - Massimo Nicolo
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy
| | - Carlo Enrico Traverso
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy
| | - Aldo Vagge
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy
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Hu W, Wang W, Wang Y, Chen Y, Shang X, Liao H, Huang Y, Bulloch G, Zhang S, Kiburg K, Zhang X, Tang S, Yu H, Yang X, He M, Zhu Z. Retinal age gap as a predictive biomarker of future risk of Parkinson's disease. Age Ageing 2022; 51:6555657. [PMID: 35352798 PMCID: PMC8966015 DOI: 10.1093/ageing/afac062] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Indexed: 12/21/2022] Open
Abstract
Introduction retinal age derived from fundus images using deep learning has been verified as a novel biomarker of ageing. We aim to investigate the association between retinal age gap (retinal age–chronological age) and incident Parkinson’s disease (PD). Methods a deep learning (DL) model trained on 19,200 fundus images of 11,052 chronic disease-free participants was used to predict retinal age. Retinal age gap was generated by the trained DL model for the remaining 35,834 participants free of PD at the baseline assessment. Cox proportional hazards regression models were utilised to investigate the association between retinal age gap and incident PD. Multivariable logistic model was applied for prediction of 5-year PD risk and area under the receiver operator characteristic curves (AUC) was used to estimate the predictive value. Results a total of 35,834 participants (56.7 ± 8.04 years, 55.7% female) free of PD at baseline were included in the present analysis. After adjustment of confounding factors, 1-year increase in retinal age gap was associated with a 10% increase in risk of PD (hazard ratio [HR] = 1.10, 95% confidence interval [CI]: 1.01–1.20, P = 0.023). Compared with the lowest quartile of the retinal age gap, the risk of PD was significantly increased in the third and fourth quartiles (HR = 2.66, 95% CI: 1.13–6.22, P = 0.024; HR = 4.86, 95% CI: 1.59–14.8, P = 0.005, respectively). The predictive value of retinal age and established risk factors for 5-year PD risk were comparable (AUC = 0.708 and 0.717, P = 0.821). Conclusion retinal age gap demonstrated a potential for identifying individuals at a high risk of developing future PD.
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Affiliation(s)
- Wenyi Hu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
- Ophthalmology, Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yueye Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yifan Chen
- John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Xianwen Shang
- Ophthalmology, Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia
| | - Huan Liao
- Neural Regeneration Group, Institute of Reconstructive Neurobiology, University of Bonn, Bonn, Germany
| | - Yu Huang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Gabriella Bulloch
- Ophthalmology, Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia
| | - Shiran Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Katerina Kiburg
- Ophthalmology, Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia
| | - Xueli Zhang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Shulin Tang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Honghua Yu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Xiaohong Yang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Mingguang He
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
- Ophthalmology, Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Zhuoting Zhu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
- Ophthalmology, Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia
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