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Quer G, Topol EJ. The potential for large language models to transform cardiovascular medicine. Lancet Digit Health 2024; 6:e767-e771. [PMID: 39214760 DOI: 10.1016/s2589-7500(24)00151-1] [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: 03/26/2024] [Revised: 07/04/2024] [Accepted: 07/04/2024] [Indexed: 09/04/2024]
Abstract
Cardiovascular diseases persist as the leading cause of death globally and their early detection and prediction remain a major challenge. Artificial intelligence (AI) tools can help meet this challenge as they have considerable potential for early diagnosis and prediction of occurrence of these diseases. Deep neural networks can improve the accuracy of medical image interpretation and their outputs can provide rich information that otherwise would not be detected by cardiologists. With recent advances in transformer models, multimodal AI, and large language models, the ability to integrate electronic health record data with images, genomics, biosensors, and other data has the potential to improve diagnosis and partition patients who are at high risk for primary preventive strategies. Although much emphasis has been placed on AI supporting clinicians, AI can also serve patients and provide immediate help with diagnosis, such as that of arrhythmia, and is being studied for automated self-imaging. Potential risks, such as loss of data privacy or potential diagnostic errors, should be addressed before use in clinical practice. This Series paper explores opportunities and limitations of AI models for cardiovascular medicine, and aims to identify specific barriers to and solutions in the application of AI models, facilitating their integration into health-care systems.
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Affiliation(s)
- Giorgio Quer
- Scripps Research Translational Institute, La Jolla, CA, USA
| | - Eric J Topol
- Scripps Research Translational Institute, La Jolla, CA, USA.
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2
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Ghenciu LA, Dima M, Stoicescu ER, Iacob R, Boru C, Hațegan OA. Retinal Imaging-Based Oculomics: Artificial Intelligence as a Tool in the Diagnosis of Cardiovascular and Metabolic Diseases. Biomedicines 2024; 12:2150. [PMID: 39335664 PMCID: PMC11430496 DOI: 10.3390/biomedicines12092150] [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: 08/27/2024] [Revised: 09/19/2024] [Accepted: 09/21/2024] [Indexed: 09/30/2024] Open
Abstract
Cardiovascular diseases (CVDs) are a major cause of mortality globally, emphasizing the need for early detection and effective risk assessment to improve patient outcomes. Advances in oculomics, which utilize the relationship between retinal microvascular changes and systemic vascular health, offer a promising non-invasive approach to assessing CVD risk. Retinal fundus imaging and optical coherence tomography/angiography (OCT/OCTA) provides critical information for early diagnosis, with retinal vascular parameters such as vessel caliber, tortuosity, and branching patterns identified as key biomarkers. Given the large volume of data generated during routine eye exams, there is a growing need for automated tools to aid in diagnosis and risk prediction. The study demonstrates that AI-driven analysis of retinal images can accurately predict cardiovascular risk factors, cardiovascular events, and metabolic diseases, surpassing traditional diagnostic methods in some cases. These models achieved area under the curve (AUC) values ranging from 0.71 to 0.87, sensitivity between 71% and 89%, and specificity between 40% and 70%, surpassing traditional diagnostic methods in some cases. This approach highlights the potential of retinal imaging as a key component in personalized medicine, enabling more precise risk assessment and earlier intervention. It not only aids in detecting vascular abnormalities that may precede cardiovascular events but also offers a scalable, non-invasive, and cost-effective solution for widespread screening. However, the article also emphasizes the need for further research to standardize imaging protocols and validate the clinical utility of these biomarkers across different populations. By integrating oculomics into routine clinical practice, healthcare providers could significantly enhance early detection and management of systemic diseases, ultimately improving patient outcomes. Fundus image analysis thus represents a valuable tool in the future of precision medicine and cardiovascular health management.
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Affiliation(s)
- Laura Andreea Ghenciu
- Department of Functional Sciences, 'Victor Babes' University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, 300041 Timisoara, Romania
- Center for Translational Research and Systems Medicine, 'Victor Babes' University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, 300041 Timisoara, Romania
| | - Mirabela Dima
- Department of Neonatology, 'Victor Babes' University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, 300041 Timisoara, Romania
| | - Emil Robert Stoicescu
- Field of Applied Engineering Sciences, Specialization Statistical Methods and Techniques in Health and Clinical Research, Faculty of Mechanics, 'Politehnica' University Timisoara, Mihai Viteazul Boulevard No. 1, 300222 Timisoara, Romania
- Department of Radiology and Medical Imaging, 'Victor Babes' University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, 300041 Timisoara, Romania
- Research Center for Pharmaco-Toxicological Evaluations, 'Victor Babes' University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, 300041 Timisoara, Romania
| | - Roxana Iacob
- Field of Applied Engineering Sciences, Specialization Statistical Methods and Techniques in Health and Clinical Research, Faculty of Mechanics, 'Politehnica' University Timisoara, Mihai Viteazul Boulevard No. 1, 300222 Timisoara, Romania
- Doctoral School, "Victor Babes" University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square 2, 300041 Timisoara, Romania
- Department of Anatomy and Embriology, 'Victor Babes' University of Medicine and Pharmacy Timisoara, 300041 Timisoara, Romania
| | - Casiana Boru
- Discipline of Anatomy and Embriology, Medicine Faculty, "Vasile Goldis" Western University of Arad, Revolution Boulevard 94, 310025 Arad, Romania
| | - Ovidiu Alin Hațegan
- Discipline of Anatomy and Embriology, Medicine Faculty, "Vasile Goldis" Western University of Arad, Revolution Boulevard 94, 310025 Arad, Romania
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3
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Baldwin G, Tracy J, Vingopoulos F, Munsell M, Bennett C, Rodriguez JM, Choi H, Garg I, Tandias R, Wescott H, Finn MJ, Razavi P, Bannerman A, Zeng R, Vavvas DG, Husain D, Kim LA, Patel NA, Miller JB. Contrast Sensitivity Better Reflects Wide-Field Swept-Source Optical Coherence Tomography Angiography Vascular Metrics Among Healthy Eyes Compared to Visual Acuity. Ophthalmic Surg Lasers Imaging Retina 2024; 55:494-502. [PMID: 38917397 DOI: 10.3928/23258160-20240411-01] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
BACKGROUND AND OBJECTIVE To evaluate the association between widefield swept-source optical coherence tomography angiography (WF SS-OCTA) and visual function in healthy eyes. PATIENTS AND METHODS Fifty-seven eyes of 45 patients were evaluated with visual acuity (VA), contrast sensitivity (CS), and WF SS-OCTA (3 × 3, 6 × 6, and 12 × 12 mm images) on the same day. Mixed-effects multivariable regression analyses were performed. RESULTS Contrast sensitivity metrics, including CS between 6 to 18 cycles per degree (cpd) and area under the logarithm CS function, were significantly associated with vessel density (VD) and vessel skeletonized density (VSD), whereas VA was not. The largest effect size was between CS at 18 cpd and VD (β = 0.41, P = 0.007) and VSD (β = 0.42, P = 0.006) on 12 × 12 mm images. CONCLUSIONS Reduced VSD and VD on WF SSOCTA was significantly associated with decreased CS, whereas VA was not. These results suggest CS could serve as a screening tool for early stage retinal and neurologic disorders. [Ophthalmic Surg Lasers Imaging Retina 2024;55:494-502.].
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4
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Messica S, Presil D, Hoch Y, Lev T, Hadad A, Katz O, Owens DR. Enhancing stroke risk and prognostic timeframe assessment with deep learning and a broad range of retinal biomarkers. Artif Intell Med 2024; 154:102927. [PMID: 38991398 DOI: 10.1016/j.artmed.2024.102927] [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: 01/22/2024] [Revised: 06/15/2024] [Accepted: 06/25/2024] [Indexed: 07/13/2024]
Abstract
Stroke stands as a major global health issue, causing high death and disability rates and significant social and economic burdens. The effectiveness of existing stroke risk assessment methods is questionable due to their use of inconsistent and varying biomarkers, which may lead to unpredictable risk evaluations. This study introduces an automatic deep learning-based system for predicting stroke risk (both ischemic and hemorrhagic) and estimating the time frame of its occurrence, utilizing a comprehensive set of known retinal biomarkers from fundus images. Our system, tested on the UK Biobank and DRSSW datasets, achieved AUROC scores of 0.83 (95% CI: 0.79-0.85) and 0.93 (95% CI: 0.9-0.95), respectively. These results not only highlight our system's advantage over established benchmarks but also underscore the predictive power of retinal biomarkers in assessing stroke risk and the unique effectiveness of each biomarker. Additionally, the correlation between retinal biomarkers and cardiovascular diseases broadens the potential application of our system, making it a versatile tool for predicting a wide range of cardiovascular conditions.
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Affiliation(s)
| | - Dan Presil
- NEC Israeli Research Center, Herzliya, Israel
| | - Yaacov Hoch
- NEC Israeli Research Center, Herzliya, Israel
| | - Tsvi Lev
- NEC Israeli Research Center, Herzliya, Israel
| | - Aviel Hadad
- Ophthalmology Department, Soroka University Medical Center, Be'er Sheva, South District, Israel
| | - Or Katz
- NEC Israeli Research Center, Herzliya, Israel
| | - David R Owens
- Swansea University Medical School, Swansea, Wales, UK
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5
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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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6
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Lendzioszek M, Bryl A, Poppe E, Zorena K, Mrugacz M. Retinal Vein Occlusion-Background Knowledge and Foreground Knowledge Prospects-A Review. J Clin Med 2024; 13:3950. [PMID: 38999513 PMCID: PMC11242360 DOI: 10.3390/jcm13133950] [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: 05/28/2024] [Revised: 06/28/2024] [Accepted: 07/03/2024] [Indexed: 07/14/2024] Open
Abstract
Thrombosis of retinal veins is one of the most common retinal vascular diseases that may lead to vascular blindness. The latest epidemiological data leave no illusions that the burden on the healthcare system, as impacted by patients with this diagnosis, will increase worldwide. This obliges scientists to search for new therapeutic and diagnostic options. In the 21st century, there has been tremendous progress in retinal imaging techniques, which has facilitated a better understanding of the mechanisms related to the development of retinal vein occlusion (RVO) and its complications, and consequently has enabled the introduction of new treatment methods. Moreover, artificial intelligence (AI) is likely to assist in selecting the best treatment option for patients in the near future. The aim of this comprehensive review is to re-evaluate the old but still relevant data on the RVO and confront them with new studies. The paper will provide a detailed overview of diagnosis, current treatment, prevention, and future therapeutic possibilities regarding RVO, as well as clarifying the mechanism of macular edema in this disease entity.
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Affiliation(s)
- Maja Lendzioszek
- Department of Ophthalmology, Voivodship Hospital, 18-400 Lomza, Poland
| | - Anna Bryl
- Department of Ophthalmology and Eye Rehabilitation, Medical University of Bialystok, 15-089 Bialystok, Poland
| | - Ewa Poppe
- Department of Ophthalmology, Voivodship Hospital, 18-400 Lomza, Poland
| | - Katarzyna Zorena
- Department of Immunobiology and Environment Microbiology, Medical University of Gdansk, Dębinki 7, 80-211 Gdansk, Poland
| | - Malgorzata Mrugacz
- Department of Ophthalmology and Eye Rehabilitation, Medical University of Bialystok, 15-089 Bialystok, Poland
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Gavidia M, Zhu H, Montanari AN, Fuentes J, Cheng C, Dubner S, Chames M, Maison-Blanche P, Rahman MM, Sassi R, Badilini F, Jiang Y, Zhang S, Zhang HT, Du H, Teng B, Yuan Y, Wan G, Tang Z, He X, Yang X, Goncalves J. Early warning of atrial fibrillation using deep learning. PATTERNS (NEW YORK, N.Y.) 2024; 5:100970. [PMID: 39005489 PMCID: PMC11240177 DOI: 10.1016/j.patter.2024.100970] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 02/21/2024] [Accepted: 03/25/2024] [Indexed: 07/16/2024]
Abstract
Atrial fibrillation (AF), the most prevalent cardiac rhythm disorder, significantly increases hospitalization and health risks. Reverting from AF to sinus rhythm (SR) often requires intensive interventions. This study presents a deep-learning model capable of predicting the transition from SR to AF on average 30.8 min before the onset appears, with an accuracy of 83% and an F1 score of 85% on the test data. This performance was obtained from R-to-R interval signals, which can be accessible from wearable technology. Our model, entitled Warning of Atrial Fibrillation (WARN), consists of a deep convolutional neural network trained and validated on 24-h Holter electrocardiogram data from 280 patients, with 70 additional patients used for testing and further evaluation on 33 patients from two external centers. The low computational cost of WARN makes it ideal for integration into wearable technology, allowing for continuous heart monitoring and early AF detection, which can potentially reduce emergency interventions and improve patient outcomes.
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Affiliation(s)
- Marino Gavidia
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4367 Belvaux, Luxembourg
| | - Hongling Zhu
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Arthur N. Montanari
- Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208, USA
| | - Jesús Fuentes
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4367 Belvaux, Luxembourg
| | - Cheng Cheng
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Sergio Dubner
- Clinica y Maternidad Suizo Argentina, Buenos Aires 1461, Argentina
| | - Martin Chames
- Centro Integral Cardiovascular, Gualeguaychú, Entre Ríos, Argentina
| | | | | | - Roberto Sassi
- Computer Science Department, University of Milan, 20133 Milan, Italy
| | - Fabio Badilini
- Department of Physiologic Nursing, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Yinuo Jiang
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Shengjun Zhang
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hai-Tao Zhang
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hao Du
- Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Basi Teng
- Department of Plant Sciences, Cambridge University, CB2 3EA Cambridge, UK
| | - Ye Yuan
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Guohua Wan
- Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200052, China
| | - Zhouping Tang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xin He
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xiaoyun Yang
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jorge Goncalves
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4367 Belvaux, Luxembourg
- Department of Plant Sciences, Cambridge University, CB2 3EA Cambridge, UK
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8
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Patterson EJ, Bounds AD, Wagner SK, Kadri-Langford R, Taylor R, Daly D. Oculomics: A Crusade Against the Four Horsemen of Chronic Disease. Ophthalmol Ther 2024; 13:1427-1451. [PMID: 38630354 PMCID: PMC11109082 DOI: 10.1007/s40123-024-00942-x] [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: 02/02/2024] [Accepted: 03/25/2024] [Indexed: 05/22/2024] Open
Abstract
Chronic, non-communicable diseases present a major barrier to living a long and healthy life. In many cases, early diagnosis can facilitate prevention, monitoring, and treatment efforts, improving patient outcomes. There is therefore a critical need to make screening techniques as accessible, unintimidating, and cost-effective as possible. The association between ocular biomarkers and systemic health and disease (oculomics) presents an attractive opportunity for detection of systemic diseases, as ophthalmic techniques are often relatively low-cost, fast, and non-invasive. In this review, we highlight the key associations between structural biomarkers in the eye and the four globally leading causes of morbidity and mortality: cardiovascular disease, cancer, neurodegenerative disease, and metabolic disease. We observe that neurodegenerative disease is a particularly promising target for oculomics, with biomarkers detected in multiple ocular structures. Cardiovascular disease biomarkers are present in the choroid, retinal vasculature, and retinal nerve fiber layer, and metabolic disease biomarkers are present in the eyelid, tear fluid, lens, and retinal vasculature. In contrast, only the tear fluid emerged as a promising ocular target for the detection of cancer. The retina is a rich source of oculomics data, the analysis of which has been enhanced by artificial intelligence-based tools. Although not all biomarkers are disease-specific, limiting their current diagnostic utility, future oculomics research will likely benefit from combining data from various structures to improve specificity, as well as active design, development, and optimization of instruments that target specific disease signatures, thus facilitating differential diagnoses.
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Affiliation(s)
| | | | - Siegfried K Wagner
- Moorfields Eye Hospital NHS Trust, 162 City Road, London, EC1V 2PD, UK
- UCL Institute of Ophthalmology, University College London, 11-43 Bath Street, London, EC1V 9EL, UK
| | | | - Robin Taylor
- Occuity, The Blade, Abbey Square, Reading, Berkshire, RG1 3BE, UK
| | - Dan Daly
- Occuity, The Blade, Abbey Square, Reading, Berkshire, RG1 3BE, UK
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9
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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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10
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Girach Z, Sarian A, Maldonado-García C, Ravikumar N, Sergouniotis PI, Rothwell PM, Frangi AF, Julian TH. Retinal imaging for the assessment of stroke risk: a systematic review. J Neurol 2024; 271:2285-2297. [PMID: 38430271 PMCID: PMC11055692 DOI: 10.1007/s00415-023-12171-6] [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: 10/15/2023] [Revised: 12/21/2023] [Accepted: 12/23/2023] [Indexed: 03/03/2024]
Abstract
BACKGROUND Stroke is a leading cause of morbidity and mortality. Retinal imaging allows non-invasive assessment of the microvasculature. Consequently, retinal imaging is a technology which is garnering increasing attention as a means of assessing cardiovascular health and stroke risk. METHODS A biomedical literature search was performed to identify prospective studies that assess the role of retinal imaging derived biomarkers as indicators of stroke risk. RESULTS Twenty-four studies were included in this systematic review. The available evidence suggests that wider retinal venules, lower fractal dimension, increased arteriolar tortuosity, presence of retinopathy, and presence of retinal emboli are associated with increased likelihood of stroke. There is weaker evidence to suggest that narrower arterioles and the presence of individual retinopathy traits such as microaneurysms and arteriovenous nicking indicate increased stroke risk. Our review identified three models utilizing artificial intelligence algorithms for the analysis of retinal images to predict stroke. Two of these focused on fundus photographs, whilst one also utilized optical coherence tomography (OCT) technology images. The constructed models performed similarly to conventional risk scores but did not significantly exceed their performance. Only two studies identified in this review used OCT imaging, despite the higher dimensionality of this data. CONCLUSION Whilst there is strong evidence that retinal imaging features can be used to indicate stroke risk, there is currently no predictive model which significantly outperforms conventional risk scores. To develop clinically useful tools, future research should focus on utilization of deep learning algorithms, validation in external cohorts, and analysis of OCT images.
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Affiliation(s)
- Zain Girach
- Sheffield Medical School, University of Sheffield, Beech Hill Rd, Broomhall, Sheffield, UK
| | - Arni Sarian
- Manchester Royal Infirmary, Manchester University NHS Foundation Trust, Oxford Rd, Manchester, UK
| | - Cynthia Maldonado-García
- Centre for Computational Imaging and Simulation Technologies in Biomedicine, School of Computing, University of Leeds, Leeds, UK
| | - Nishant Ravikumar
- Centre for Computational Imaging and Simulation Technologies in Biomedicine, School of Computing, University of Leeds, Leeds, UK
| | - Panagiotis I Sergouniotis
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
- Manchester Centre for Genomic Medicine, Saint Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK
- Manchester Royal Eye Hospital, Manchester University NHS Foundation Trust, Oxford Rd, Manchester, UK
| | - Peter M Rothwell
- Wolfson Centre for the Prevention of Stroke and Dementia, University of Oxford, Oxford, UK
| | - Alejandro F Frangi
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- School of Computer Science, Faculty of Science and Engineering, University of Manchester, Kilburn Building, Manchester, UK
- Christabel Pankhurst Institute, The University of Manchester, Manchester, UK
| | - Thomas H Julian
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK.
- Manchester Centre for Genomic Medicine, Saint Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK.
- Manchester Royal Eye Hospital, Manchester University NHS Foundation Trust, Oxford Rd, Manchester, UK.
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11
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Prasad DK, Manjunath MP, Kulkarni MS, Kullambettu S, Srinivasan V, Chakravarthi M, Ramesh A. A Multi-Stage Approach for Cardiovascular Risk Assessment from Retinal Images Using an Amalgamation of Deep Learning and Computer Vision Techniques. Diagnostics (Basel) 2024; 14:928. [PMID: 38732342 PMCID: PMC11083022 DOI: 10.3390/diagnostics14090928] [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: 03/19/2024] [Revised: 04/10/2024] [Accepted: 04/22/2024] [Indexed: 05/13/2024] Open
Abstract
Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Early detection and effective risk assessment are crucial for implementing preventive measures and improving patient outcomes for CVDs. This work presents a novel approach to CVD risk assessment using fundus images, leveraging the inherent connection between retinal microvascular changes and systemic vascular health. This study aims to develop a predictive model for the early detection of CVDs by evaluating retinal vascular parameters. This methodology integrates both handcrafted features derived through mathematical computation and retinal vascular patterns extracted by artificial intelligence (AI) models. By combining these approaches, we seek to enhance the accuracy and reliability of CVD risk prediction in individuals. The methodology integrates state-of-the-art computer vision algorithms and AI techniques in a multi-stage architecture to extract relevant features from retinal fundus images. These features encompass a range of vascular parameters, including vessel caliber, tortuosity, and branching patterns. Additionally, a deep learning (DL)-based binary classification model is incorporated to enhance predictive accuracy. A dataset comprising fundus images and comprehensive metadata from the clinical trials conducted is utilized for training and validation. The proposed approach demonstrates promising results in the early prediction of CVD risk factors. The interpretability of the approach is enhanced through visualization techniques that highlight the regions of interest within the fundus images that are contributing to the risk predictions. Furthermore, the validation conducted in the clinical trials and the performance analysis of the proposed approach shows the potential to provide early and accurate predictions. The proposed system not only aids in risk stratification but also serves as a valuable tool for identifying vascular abnormalities that may precede overt cardiovascular events. The approach has achieved an accuracy of 85% and the findings of this study underscore the feasibility and efficacy of leveraging fundus images for cardiovascular risk assessment. As a non-invasive and cost-effective modality, fundus image analysis presents a scalable solution for population-wide screening programs. This research contributes to the evolving landscape of precision medicine by providing an innovative tool for proactive cardiovascular health management. Future work will focus on refining the solution's robustness, exploring additional risk factors, and validating its performance in additional and diverse clinical settings.
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Affiliation(s)
- Deepthi K. Prasad
- Research and Development, Image Processing and Analysis, Forus Health Private Ltd., Bengaluru 560070, India; (M.P.M.); (M.S.K.); (S.K.); (V.S.)
| | - Madhura Prakash Manjunath
- Research and Development, Image Processing and Analysis, Forus Health Private Ltd., Bengaluru 560070, India; (M.P.M.); (M.S.K.); (S.K.); (V.S.)
| | - Meghna S. Kulkarni
- Research and Development, Image Processing and Analysis, Forus Health Private Ltd., Bengaluru 560070, India; (M.P.M.); (M.S.K.); (S.K.); (V.S.)
| | - Spoorthi Kullambettu
- Research and Development, Image Processing and Analysis, Forus Health Private Ltd., Bengaluru 560070, India; (M.P.M.); (M.S.K.); (S.K.); (V.S.)
| | - Venkatakrishnan Srinivasan
- Research and Development, Image Processing and Analysis, Forus Health Private Ltd., Bengaluru 560070, India; (M.P.M.); (M.S.K.); (S.K.); (V.S.)
| | | | - Anusha Ramesh
- Department of OBGyn, St. John’s Medical College, Bengaluru 560034, India;
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12
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Evans W, Meslin EM, Kai J, Qureshi N. Precision Medicine-Are We There Yet? A Narrative Review of Precision Medicine's Applicability in Primary Care. J Pers Med 2024; 14:418. [PMID: 38673045 PMCID: PMC11051552 DOI: 10.3390/jpm14040418] [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: 03/06/2024] [Revised: 03/27/2024] [Accepted: 04/06/2024] [Indexed: 04/28/2024] Open
Abstract
Precision medicine (PM), also termed stratified, individualised, targeted, or personalised medicine, embraces a rapidly expanding area of research, knowledge, and practice. It brings together two emerging health technologies to deliver better individualised care: the many "-omics" arising from increased capacity to understand the human genome and "big data" and data analytics, including artificial intelligence (AI). PM has the potential to transform an individual's health, moving from population-based disease prevention to more personalised management. There is however a tension between the two, with a real risk that this will exacerbate health inequalities and divert funds and attention from basic healthcare requirements leading to worse health outcomes for many. All areas of medicine should consider how this will affect their practice, with PM now strongly encouraged and supported by government initiatives and research funding. In this review, we discuss examples of PM in current practice and its emerging applications in primary care, such as clinical prediction tools that incorporate genomic markers and pharmacogenomic testing. We look towards potential future applications and consider some key questions for PM, including evidence of its real-world impact, its affordability, the risk of exacerbating health inequalities, and the computational and storage challenges of applying PM technologies at scale.
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Affiliation(s)
- William Evans
- Primary Care Stratified Medicine (PRISM), Division of Primary Care, University of Nottingham, Nottingham NG7 2RD, UK; (J.K.); (N.Q.)
| | - Eric M. Meslin
- PHG Foundation, Cambridge University, Cambridge CB1 8RN, UK;
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Joe Kai
- Primary Care Stratified Medicine (PRISM), Division of Primary Care, University of Nottingham, Nottingham NG7 2RD, UK; (J.K.); (N.Q.)
| | - Nadeem Qureshi
- Primary Care Stratified Medicine (PRISM), Division of Primary Care, University of Nottingham, Nottingham NG7 2RD, UK; (J.K.); (N.Q.)
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13
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Zhang J, Luo X, Li D, Peng Y, Gao G, Lei L, Gao M, Lu L, Xu Y, Yu T, Lin S, Ma Y, Yao C, Zou H. Evaluating imaging repeatability of fully self-service fundus photography within a community-based eye disease screening setting. Biomed Eng Online 2024; 23:32. [PMID: 38475784 DOI: 10.1186/s12938-024-01222-2] [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: 11/07/2023] [Accepted: 02/23/2024] [Indexed: 03/14/2024] Open
Abstract
PURPOSE This study aimed to investigate the imaging repeatability of self-service fundus photography compared to traditional fundus photography performed by experienced operators. DESIGN Prospective cross-sectional study. METHODS In a community-based eye diseases screening site, we recruited 65 eyes (65 participants) from the resident population of Shanghai, China. All participants were devoid of cataract or any other conditions that could potentially compromise the quality of fundus imaging. Participants were categorized into fully self-service fundus photography or traditional fundus photography group. Image quantitative analysis software was used to extract clinically relevant indicators from the fundus images. Finally, a statistical analysis was performed to depict the imaging repeatability of fully self-service fundus photography. RESULTS There was no statistical difference in the absolute differences, or the extents of variation of the indicators between the two groups. The extents of variation of all the measurement indicators, with the exception of the optic cup area, were below 10% in both groups. The Bland-Altman plots and multivariate analysis results were consistent with results mentioned above. CONCLUSIONS The image repeatability of fully self-service fundus photography is comparable to that of traditional fundus photography performed by professionals, demonstrating promise in large-scale eye disease screening programs.
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Affiliation(s)
- Juzhao Zhang
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Department of Ophthalmology, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xuan Luo
- Songjiang Disease Control and Prevention Center, Shanghai, China
| | - Deshang Li
- Sijing Community Health Service Center, Shanghai, China
| | - Yajun Peng
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Guiling Gao
- Songjiang Disease Control and Prevention Center, Shanghai, China
| | - Liangwen Lei
- Sijing Community Health Service Center, Shanghai, China
| | - Meng Gao
- Sijing Community Health Service Center, Shanghai, China
| | - Lina Lu
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Yi Xu
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Tao Yu
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Senlin Lin
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China.
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China.
| | - Yingyan Ma
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China.
- National Clinical Research Center for Eye Diseases, Shanghai, China.
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China.
- Department of Ophthalmology, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Chunxia Yao
- Songjiang Disease Control and Prevention Center, Shanghai, China.
| | - Haidong Zou
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China.
- National Clinical Research Center for Eye Diseases, Shanghai, China.
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China.
- Department of Ophthalmology, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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14
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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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15
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Chen X, Xia Y, Dall'Armellina E, Ravikumar N, Frangi AF. Joint shape/texture representation learning for cardiovascular disease diagnosis from magnetic resonance imaging. EUROPEAN HEART JOURNAL. IMAGING METHODS AND PRACTICE 2024; 2:qyae042. [PMID: 39045211 PMCID: PMC11195696 DOI: 10.1093/ehjimp/qyae042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 04/09/2024] [Indexed: 07/25/2024]
Abstract
Aims Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide. Cardiac image and mesh are two primary modalities to present the shape and structure of the heart and have been demonstrated to be efficient in CVD prediction and diagnosis. However, previous research has been generally focussed on a single modality (image or mesh), and few of them have tried to jointly consider the image and mesh representations of heart. To obtain efficient and explainable biomarkers for CVD prediction and diagnosis, it is needed to jointly consider both representations. Methods and results We design a novel multi-channel variational auto-encoder, mesh-image variational auto-encoder, to learn joint representation of paired mesh and image. After training, the shape-aware image representation (SAIR) can be learned directly from the raw images and applied for further CVD prediction and diagnosis. We demonstrate our method on data from UK Biobank study and two other datasets via extensive experiments. In acute myocardial infarction prediction, SAIR achieves 81.43% accuracy, significantly higher than traditional biomarkers like metadata and clinical indices (left ventricle and right ventricle clinical indices of cardiac function like chamber volume, mass, and ejection fraction). Conclusion Our mesh-image variational auto-encoder provides a novel approach for 3D cardiac mesh reconstruction from images. The extraction of SAIR is fast and without need of segmentation masks, and its focussing can be visualized in the corresponding cardiac meshes. SAIR archives better performance than traditional biomarkers and can be applied as an efficient supplement to them, which is of significant potential in CVD analysis.
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Affiliation(s)
- Xiang Chen
- School of Computing, University of Leeds, Woodhouse, LS2 9JT Leeds, UK
| | - Yan Xia
- School of Computing, University of Leeds, Woodhouse, LS2 9JT Leeds, UK
| | | | - Nishant Ravikumar
- School of Computing, University of Leeds, Woodhouse, LS2 9JT Leeds, UK
| | - Alejandro F Frangi
- Christabel Pankhurst Institute, The University of Manchester, Oxford Rd, M13 9PL Manchester, UK
- Department of Computer Science, School of Engineering, The University of Manchester, Oxford Rd, M13 9PL Manchester, UK
- Division of Informatics, Imaging, and Data Sciences, School of Health Sciences, The University of Manchester, Oxford Rd, M13 9PL Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Oxford Rd, M13 9PL Manchester, UK
- Medical Imaging Research Center (MIRC), University Hospital Gasthuisberg, UZ Herestraat 49 - bus 7003, 3000 Leuven, Belgium
- Department of Cardiovascular Sciences, KU Leuven, UZ Herestraat 49 - box 911, 3000 Leuven, Belgium
- Department of Electrical Engineering, KU Leuven, Kasteelpark Arenberg 10 postbus 2440, 3001 Leuven, Belgium
- Alan Turing Institute, British Library, 96 Euston Rd., NW1 2DB London, UK
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16
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Colcombe J, Mundae R, Kaiser A, Bijon J, Modi Y. Retinal Findings and Cardiovascular Risk: Prognostic Conditions, Novel Biomarkers, and Emerging Image Analysis Techniques. J Pers Med 2023; 13:1564. [PMID: 38003879 PMCID: PMC10672409 DOI: 10.3390/jpm13111564] [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: 10/10/2023] [Revised: 10/26/2023] [Accepted: 10/26/2023] [Indexed: 11/26/2023] Open
Abstract
Many retinal diseases and imaging findings have pathophysiologic underpinnings in the function of the cardiovascular system. Myriad retinal conditions, new imaging biomarkers, and novel image analysis techniques have been investigated for their association with future cardiovascular risk or utility in cardiovascular risk prognostication. An intensive literature search was performed to identify relevant articles indexed in PubMed, Scopus, and Google Scholar for a targeted narrative review. This review investigates the literature on specific retinal disease states, such as retinal arterial and venous occlusions and cotton wool spots, that portend significantly increased risk of future cardiovascular events, such as stroke or myocardial infarction, and the implications for personalized patient counseling. Furthermore, conditions diagnosed primarily through retinal bioimaging, such as paracentral acute middle maculopathy and the newly discovered entity known as a retinal ischemic perivascular lesion, may be associated with future incident cardiovascular morbidity and are also discussed. As ever-more-sophisticated imaging biomarkers and analysis techniques are developed, the review concludes with a focused analysis of optical coherence tomography and optical coherence tomography angiography biomarkers under investigation for potential value in prognostication and personalized therapy in cardiovascular disease.
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Affiliation(s)
- Joseph Colcombe
- Department of Ophthalmology, NYU Langone Medical Center, New York, NY 10016, USA; (J.C.); (R.M.)
| | - Rusdeep Mundae
- Department of Ophthalmology, NYU Langone Medical Center, New York, NY 10016, USA; (J.C.); (R.M.)
| | - Alexis Kaiser
- College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jacques Bijon
- Vitreous Retina Macula Consultants of New York, New York, NY 10022, USA;
| | - Yasha Modi
- Department of Ophthalmology, NYU Langone Medical Center, New York, NY 10016, USA; (J.C.); (R.M.)
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17
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Zhou Y, Chia MA, Wagner SK, Ayhan MS, Williamson DJ, Struyven RR, Liu T, Xu M, Lozano MG, Woodward-Court P, Kihara Y, Altmann A, Lee AY, Topol EJ, Denniston AK, Alexander DC, Keane PA. A foundation model for generalizable disease detection from retinal images. Nature 2023; 622:156-163. [PMID: 37704728 PMCID: PMC10550819 DOI: 10.1038/s41586-023-06555-x] [Citation(s) in RCA: 80] [Impact Index Per Article: 80.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 08/18/2023] [Indexed: 09/15/2023]
Abstract
Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders1. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications2. Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications. Specifically, RETFound is trained on 1.6 million unlabelled retinal images by means of self-supervised learning and then adapted to disease detection tasks with explicit labels. We show that adapted RETFound consistently outperforms several comparison models in the diagnosis and prognosis of sight-threatening eye diseases, as well as incident prediction of complex systemic disorders such as heart failure and myocardial infarction with fewer labelled data. RETFound provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable broad clinical AI applications from retinal imaging.
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Affiliation(s)
- Yukun Zhou
- Centre for Medical Image Computing, University College London, London, UK.
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK.
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
| | - Mark A Chia
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Siegfried K Wagner
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Murat S Ayhan
- Centre for Medical Image Computing, University College London, London, UK
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Dominic J Williamson
- Centre for Medical Image Computing, University College London, London, UK
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Robbert R Struyven
- Centre for Medical Image Computing, University College London, London, UK
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Timing Liu
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Moucheng Xu
- Centre for Medical Image Computing, University College London, London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Mateo G Lozano
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Department of Computer Science, University of Coruña, A Coruña, Spain
| | - Peter Woodward-Court
- Centre for Medical Image Computing, University College London, London, UK
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Yuka Kihara
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- Roger and Angie Karalis Johnson Retina Center, University of Washington, Seattle, WA, USA
| | - Andre Altmann
- Centre for Medical Image Computing, University College London, London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- Roger and Angie Karalis Johnson Retina Center, University of Washington, Seattle, WA, USA
| | - Eric J Topol
- Department of Molecular Medicine, Scripps Research, La Jolla, CA, USA
| | - Alastair K Denniston
- Academic Unit of Ophthalmology, University of Birmingham, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, University College London, London, UK
- Department of Computer Science, University College London, London, UK
| | - Pearse A Keane
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK.
- Institute of Ophthalmology, University College London, London, UK.
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18
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Bennett J, van Dinther M, Voorter P, Backes W, Barnes J, Barkhof F, Captur G, Hughes AD, Sudre C, Treibel TA. Assessment of Microvascular Disease in Heart and Brain by MRI: Application in Heart Failure with Preserved Ejection Fraction and Cerebral Small Vessel Disease. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1596. [PMID: 37763715 PMCID: PMC10534635 DOI: 10.3390/medicina59091596] [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: 06/09/2023] [Revised: 08/18/2023] [Accepted: 08/29/2023] [Indexed: 09/29/2023]
Abstract
The objective of this review is to investigate the commonalities of microvascular (small vessel) disease in heart failure with preserved ejection fraction (HFpEF) and cerebral small vessel disease (CSVD). Furthermore, the review aims to evaluate the current magnetic resonance imaging (MRI) diagnostic techniques for both conditions. By comparing the two conditions, this review seeks to identify potential opportunities to improve the understanding of both HFpEF and CSVD.
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Affiliation(s)
- Jonathan Bennett
- Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK
- Department of Cardiology, Barts Heart Centre, London EC1A 7BE, UK
| | - Maud van Dinther
- Department of Neurology, Maastricht University Medical Center, 6229 HX Maastricht, The Netherlands
- School for Cardiovascular Diseases, Faculty of Health, Medicine and Life Sciences, Maastricht University, 6211 LX Maastricht, The Netherlands
| | - Paulien Voorter
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, 6229 HX Maastricht, The Netherlands
- School for Mental Health & Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Walter Backes
- School for Cardiovascular Diseases, Faculty of Health, Medicine and Life Sciences, Maastricht University, 6211 LX Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, 6229 HX Maastricht, The Netherlands
- School for Mental Health & Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Josephine Barnes
- Dementia Research Centre, UCL Queens Square Institute of Neurology, University College London, London WC1E 6BT, UK
| | - Frederick Barkhof
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, Vrije University, P.O. Box 7057, 1007 MB Amsterdam, The Netherlands
- Queen Square Institute of Neurology, University College London, London WC1E 6BT, UK
- Centre for Medical Image Computing, University College London, London WC1E 6BT, UK
| | - Gabriella Captur
- Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK
- Medical Research Council Unit for Lifelong Health and Ageing, Department of Population Science and Experimental Medicine, University College London, London WC1E 6BT, UK
- Centre for Inherited Heart Muscle Conditions, Cardiology Department, The Royal Free Hospital, London NW3 2QG, UK
| | - Alun D. Hughes
- Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK
- Medical Research Council Unit for Lifelong Health and Ageing, Department of Population Science and Experimental Medicine, University College London, London WC1E 6BT, UK
| | - Carole Sudre
- Dementia Research Centre, UCL Queens Square Institute of Neurology, University College London, London WC1E 6BT, UK
- Centre for Medical Image Computing, University College London, London WC1E 6BT, UK
- Medical Research Council Unit for Lifelong Health and Ageing, Department of Population Science and Experimental Medicine, University College London, London WC1E 6BT, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London WC2R 2LS, UK
| | - Thomas A. Treibel
- Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK
- Department of Cardiology, Barts Heart Centre, London EC1A 7BE, UK
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19
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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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20
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Li H, Cao J, Grzybowski A, Jin K, Lou L, Ye J. Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images. Healthcare (Basel) 2023; 11:1739. [PMID: 37372857 PMCID: PMC10298137 DOI: 10.3390/healthcare11121739] [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: 04/15/2023] [Revised: 06/07/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
The advent of artificial intelligence (AI), especially the state-of-the-art deep learning frameworks, has begun a silent revolution in all medical subfields, including ophthalmology. Due to their specific microvascular and neural structures, the eyes are anatomically associated with the rest of the body. Hence, ocular image-based AI technology may be a useful alternative or additional screening strategy for systemic diseases, especially where resources are scarce. This review summarizes the current applications of AI related to the prediction of systemic diseases from multimodal ocular images, including cardiovascular diseases, dementia, chronic kidney diseases, and anemia. Finally, we also discuss the current predicaments and future directions of these applications.
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Affiliation(s)
- Huimin Li
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China; (H.L.); (J.C.); (K.J.)
| | - Jing Cao
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China; (H.L.); (J.C.); (K.J.)
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, 60-836 Poznan, Poland;
| | - Kai Jin
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China; (H.L.); (J.C.); (K.J.)
| | - Lixia Lou
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China; (H.L.); (J.C.); (K.J.)
| | - Juan Ye
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China; (H.L.); (J.C.); (K.J.)
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21
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Villaplana-Velasco A, Pigeyre M, Engelmann J, Rawlik K, Canela-Xandri O, Tochel C, Lona-Durazo F, Mookiah MRK, Doney A, Parra EJ, Trucco E, MacGillivray T, Rannikmae K, Tenesa A, Pairo-Castineira E, Bernabeu MO. Fine-mapping of retinal vascular complexity loci identifies Notch regulation as a shared mechanism with myocardial infarction outcomes. Commun Biol 2023; 6:523. [PMID: 37188768 PMCID: PMC10185685 DOI: 10.1038/s42003-023-04836-9] [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: 08/18/2022] [Accepted: 04/12/2023] [Indexed: 05/17/2023] Open
Abstract
There is increasing evidence that the complexity of the retinal vasculature measured as fractal dimension, Df, might offer earlier insights into the progression of coronary artery disease (CAD) before traditional biomarkers can be detected. This association could be partly explained by a common genetic basis; however, the genetic component of Df is poorly understood. We present a genome-wide association study (GWAS) of 38,000 individuals with white British ancestry from the UK Biobank aimed to comprehensively study the genetic component of Df and analyse its relationship with CAD. We replicated 5 Df loci and found 4 additional loci with suggestive significance (P < 1e-05) to contribute to Df variation, which previously were reported in retinal tortuosity and complexity, hypertension, and CAD studies. Significant negative genetic correlation estimates support the inverse relationship between Df and CAD, and between Df and myocardial infarction (MI), one of CAD's fatal outcomes. Fine-mapping of Df loci revealed Notch signalling regulatory variants supporting a shared mechanism with MI outcomes. We developed a predictive model for MI incident cases, recorded over a 10-year period following clinical and ophthalmic evaluation, combining clinical information, Df, and a CAD polygenic risk score. Internal cross-validation demonstrated a considerable improvement in the area under the curve (AUC) of our predictive model (AUC = 0.770 ± 0.001) when comparing with an established risk model, SCORE, (AUC = 0.741 ± 0.002) and extensions thereof leveraging the PRS (AUC = 0.728 ± 0.001). This evidences that Df provides risk information beyond demographic, lifestyle, and genetic risk factors. Our findings shed new light on the genetic basis of Df, unveiling a common control with MI, and highlighting the benefits of its application in individualised MI risk prediction.
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Affiliation(s)
- Ana Villaplana-Velasco
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, Scotland, UK
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Marie Pigeyre
- Population Health Research Institute (PHRI), Department of Medicine, Faculty of Health Sciences, McMaster University, McMaster University, Hamilton, Ontario, Canada
| | - Justin Engelmann
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Konrad Rawlik
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Oriol Canela-Xandri
- MRC Human Genetics Unit, IGC, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Claire Tochel
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | | | | | - Alex Doney
- VAMPIRE project, Computing, School of Science and Engineering, University of Dundee, Dundee, Scotland, UK
| | - Esteban J Parra
- University of Toronto at Mississauga, Mississauga, Ontario, Canada
| | - Emanuele Trucco
- VAMPIRE project, Computing, School of Science and Engineering, University of Dundee, Dundee, Scotland, UK
| | - Tom MacGillivray
- VAMPIRE project, Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Kristiina Rannikmae
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Albert Tenesa
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, Scotland, UK
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
- MRC Human Genetics Unit, IGC, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Erola Pairo-Castineira
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Miguel O Bernabeu
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK.
- The Bayes Centre, The University of Edinburgh, Edinburgh, Scotland, UK.
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22
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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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23
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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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24
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Wen J, Liu D, Wu Q, Zhao L, Iao WC, Lin H. Retinal image‐based artificial intelligence in detecting and predicting kidney diseases: Current advances and future perspectives. VIEW 2023. [DOI: 10.1002/viw.20220070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2023] Open
Affiliation(s)
- Jingyi Wen
- State Key Laboratory of OphthalmologyZhongshan Ophthalmic CenterSun Yat‐sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Disease GuangzhouChina
| | - Dong Liu
- State Key Laboratory of OphthalmologyZhongshan Ophthalmic CenterSun Yat‐sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Disease GuangzhouChina
| | - Qianni Wu
- State Key Laboratory of OphthalmologyZhongshan Ophthalmic CenterSun Yat‐sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Disease GuangzhouChina
| | - Lanqin Zhao
- State Key Laboratory of OphthalmologyZhongshan Ophthalmic CenterSun Yat‐sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Disease GuangzhouChina
| | - Wai Cheng Iao
- State Key Laboratory of OphthalmologyZhongshan Ophthalmic CenterSun Yat‐sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Disease GuangzhouChina
| | - Haotian Lin
- State Key Laboratory of OphthalmologyZhongshan Ophthalmic CenterSun Yat‐sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Disease GuangzhouChina
- Center for Precision Medicine and Department of Genetics and Biomedical Informatics Zhongshan School of Medicine Sun Yat‐sen University Guangzhou China
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25
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Zeitz O, Sivaprasad S, Joussen AM, Grzybowski A. Systems medicine and artificial intelligence in retinal disease. Graefes Arch Clin Exp Ophthalmol 2023; 261:627-628. [PMID: 36278984 PMCID: PMC9988796 DOI: 10.1007/s00417-022-05868-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 10/02/2022] [Accepted: 10/10/2022] [Indexed: 11/28/2022] Open
Affiliation(s)
- Oliver Zeitz
- Department of Ophthalmology, Charité Universitätsmedizin Berlin, Augenklinik, Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Sobha Sivaprasad
- Institute of Ophthalmology, NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital, University College London, London, UK
| | - Antonia M Joussen
- Department of Ophthalmology, Charité Universitätsmedizin Berlin, Augenklinik, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Andrzej Grzybowski
- Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland
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26
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Häner NU, Dysli C, Munk MR. Imaging in retinal vascular disease: A review. Clin Exp Ophthalmol 2023; 51:217-228. [PMID: 36597823 DOI: 10.1111/ceo.14203] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/06/2022] [Accepted: 12/09/2022] [Indexed: 01/05/2023]
Abstract
Retinal vascular diseases represent a broad field of ocular pathologies. Retinal imaging is an important tool for diagnosis, prognosis and follow up of retinal vascular diseases. It includes a wide variety of imaging techniques ranging from colour fundus photography and optical coherence tomography to dynamic diagnostic options such as fluorescein angiography, and optical coherence tomography angiography. The newest developments in respective imaging techniques include widefield imaging to assess the retinal periphery, which is of especial interest in retinal vascular diseases. Automatic image analysis and artificial intelligence may support the image analysis and may prove valuable for prognostic purposes. This review provides a broad overview of the imaging techniques that have been used in the past, today and maybe in the future to stage and monitor retinal vascular disease with focus on the main disease entities including diabetic retinopathy, retinal vein occlusion, and retinal artery occlusion.
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Affiliation(s)
- Nathanael U Häner
- Department of Ophthalmology, Inselspital University Hospital, Bern, Switzerland
| | - Chantal Dysli
- Department of Ophthalmology, Inselspital University Hospital, Bern, Switzerland
| | - Marion R Munk
- Department of Ophthalmology, Inselspital University Hospital, Bern, Switzerland
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27
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Barriada RG, Masip D. An Overview of Deep-Learning-Based Methods for Cardiovascular Risk Assessment with Retinal Images. Diagnostics (Basel) 2022; 13:diagnostics13010068. [PMID: 36611360 PMCID: PMC9818382 DOI: 10.3390/diagnostics13010068] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/19/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Cardiovascular diseases (CVDs) are one of the most prevalent causes of premature death. Early detection is crucial to prevent and address CVDs in a timely manner. Recent advances in oculomics show that retina fundus imaging (RFI) can carry relevant information for the early diagnosis of several systemic diseases. There is a large corpus of RFI systematically acquired for diagnosing eye-related diseases that could be used for CVDs prevention. Nevertheless, public health systems cannot afford to dedicate expert physicians to only deal with this data, posing the need for automated diagnosis tools that can raise alarms for patients at risk. Artificial Intelligence (AI) and, particularly, deep learning models, became a strong alternative to provide computerized pre-diagnosis for patient risk retrieval. This paper provides a novel review of the major achievements of the recent state-of-the-art DL approaches to automated CVDs diagnosis. This overview gathers commonly used datasets, pre-processing techniques, evaluation metrics and deep learning approaches used in 30 different studies. Based on the reviewed articles, this work proposes a classification taxonomy depending on the prediction target and summarizes future research challenges that have to be tackled to progress in this line.
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28
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Rudnicka AR, Welikala R, Barman S, Foster PJ, Luben R, Hayat S, Khaw KT, Whincup P, Strachan D, Owen CG. Artificial intelligence-enabled retinal vasculometry for prediction of circulatory mortality, myocardial infarction and stroke. Br J Ophthalmol 2022; 106:1722-1729. [PMID: 36195457 PMCID: PMC9685715 DOI: 10.1136/bjo-2022-321842] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/03/2022] [Indexed: 02/02/2023]
Abstract
AIMS We examine whether inclusion of artificial intelligence (AI)-enabled retinal vasculometry (RV) improves existing risk algorithms for incident stroke, myocardial infarction (MI) and circulatory mortality. METHODS AI-enabled retinal vessel image analysis processed images from 88 052 UK Biobank (UKB) participants (aged 40-69 years at image capture) and 7411 European Prospective Investigation into Cancer (EPIC)-Norfolk participants (aged 48-92). Retinal arteriolar and venular width, tortuosity and area were extracted. Prediction models were developed in UKB using multivariable Cox proportional hazards regression for circulatory mortality, incident stroke and MI, and externally validated in EPIC-Norfolk. Model performance was assessed using optimism adjusted calibration, C-statistics and R2 statistics. Performance of Framingham risk scores (FRS) for incident stroke and incident MI, with addition of RV to FRS, were compared with a simpler model based on RV, age, smoking status and medical history (antihypertensive/cholesterol lowering medication, diabetes, prevalent stroke/MI). RESULTS UKB prognostic models were developed on 65 144 participants (mean age 56.8; median follow-up 7.7 years) and validated in 5862 EPIC-Norfolk participants (67.6, 9.1 years, respectively). Prediction models for circulatory mortality in men and women had optimism adjusted C-statistics and R2 statistics between 0.75-0.77 and 0.33-0.44, respectively. For incident stroke and MI, addition of RV to FRS did not improve model performance in either cohort. However, the simpler RV model performed equally or better than FRS. CONCLUSION RV offers an alternative predictive biomarker to traditional risk-scores for vascular health, without the need for blood sampling or blood pressure measurement. Further work is needed to examine RV in population screening to triage individuals at high-risk.
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Affiliation(s)
| | - Roshan Welikala
- Faculty of Science, Engineering and Computing, Kingston University, Kingston-Upon-Thames, UK
| | - Sarah Barman
- Faculty of Science, Engineering and Computing, Kingston University, Kingston-Upon-Thames, UK
| | - Paul J Foster
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, University College London, London, UK
| | - Robert Luben
- MRC Epidemiology Unit, Cambridge University, Cambridge, UK
| | - Shabina Hayat
- Department of Psychiatry, Cambridge Public Health, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Kay-Tee Khaw
- MRC Epidemiology Unit, Cambridge University, Cambridge, UK
| | - Peter Whincup
- Population Health Research Institute, St George's University of London, London, UK
| | - David Strachan
- Population Health Research Institute, St George's University of London, London, UK
| | - Christopher G Owen
- Population Health Research Institute, St George's University of London, London, UK
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29
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Sherif NA, Chew EY, Chiang MF, Hribar M, Gao J, Goetz KE. Artificial intelligence at the national eye institute. Curr Opin Ophthalmol 2022; 33:579-584. [PMID: 36206110 PMCID: PMC9555870 DOI: 10.1097/icu.0000000000000889] [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] [Indexed: 11/27/2022]
Abstract
PURPOSE OF REVIEW This review highlights the artificial intelligence, machine learning, and deep learning initiatives supported by the National Institutes of Health (NIH) and the National Eye Institute (NEI) and calls attention to activities and goals defined in the NEI Strategic Plan as well as opportunities for future activities and breakthroughs in ophthalmology. RECENT FINDINGS Ophthalmology is at the forefront of artificial intelligence-based innovations in biomedical research that may lead to improvement in early detection and surveillance of ocular disease, prediction of progression, and improved quality of life. Technological advances have ushered in an era where unprecedented amounts of information can be linked that enable scientific discovery. However, there remains an unmet need to collect, harmonize, and share data in a machine actionable manner. Similarly, there is a need to ensure that efforts promote health and research equity by expanding diversity in the data and workforce. SUMMARY The NIH/NEI has supported the development artificial intelligence-based innovations to advance biomedical research. The NIH/NEI has defined activities to achieve these goals in the NIH Strategic Plan for Data Science and the NEI Strategic Plan and have spearheaded initiatives to facilitate research in these areas.
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Affiliation(s)
- Noha A Sherif
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Emily Y Chew
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Michael F Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | | | - James Gao
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Kerry E Goetz
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
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Wong DYL, Lam MC, Ran A, Cheung CY. Artificial intelligence in retinal imaging for cardiovascular disease prediction: current trends and future directions. Curr Opin Ophthalmol 2022; 33:440-446. [PMID: 35916571 DOI: 10.1097/icu.0000000000000886] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Retinal microvasculature assessment has shown promise to enhance cardiovascular disease (CVD) risk stratification. Integrating artificial intelligence into retinal microvasculature analysis may increase the screening capacity of CVD risks compared with risk score calculation through blood-taking. This review summarizes recent advancements in artificial intelligence based retinal photograph analysis for CVD prediction, and suggests challenges and future prospects for translation into a clinical setting. RECENT FINDINGS Artificial intelligence based retinal microvasculature analyses potentially predict CVD risk factors (e.g. blood pressure, diabetes), direct CVD events (e.g. CVD mortality), retinal features (e.g. retinal vessel calibre) and CVD biomarkers (e.g. coronary artery calcium score). However, challenges such as handling photographs with concurrent retinal diseases, limited diverse data from other populations or clinical settings, insufficient interpretability and generalizability, concerns on cost-effectiveness and social acceptance may impede the dissemination of these artificial intelligence algorithms into clinical practice. SUMMARY Artificial intelligence based retinal microvasculature analysis may supplement existing CVD risk stratification approach. Although technical and socioeconomic challenges remain, we envision artificial intelligence based microvasculature analysis to have major clinical and research impacts in the future, through screening for high-risk individuals especially in less-developed areas and identifying new retinal biomarkers for CVD research.
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Affiliation(s)
- Dragon Y L Wong
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
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Chen JH, Dhaliwal G, Yang D. Decoding Artificial Intelligence to Achieve Diagnostic Excellence: Learning From Experts, Examples, and Experience. JAMA 2022; 328:709-710. [PMID: 35913752 DOI: 10.1001/jama.2022.13735] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Affiliation(s)
- Jonathan H Chen
- Stanford Center for Biomedical Informatics Research, Division of Hospital Medicine, Stanford University, Stanford, California
| | - Gurpreet Dhaliwal
- Department of Medicine, University of California, San Francisco
- Medical Service, San Francisco Veteran Affairs Medical Center, San Francisco, California
| | - Daniel Yang
- Gordon and Betty Moore Foundation, Palo Alto, California
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Peng Q, Tseng RMWW, Tham YC, Cheng CY, Rim TH. Detection of Systemic Diseases From Ocular Images Using Artificial Intelligence: A Systematic Review. Asia Pac J Ophthalmol (Phila) 2022; 11:126-139. [PMID: 35533332 DOI: 10.1097/apo.0000000000000515] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
PURPOSE Despite the huge investment in health care, there is still a lack of precise and easily accessible screening systems. With proven associations to many systemic diseases, the eye could potentially provide a credible perspective as a novel screening tool. This systematic review aims to summarize the current applications of ocular image-based artificial intelligence on the detection of systemic diseases and suggest future trends for systemic disease screening. METHODS A systematic search was conducted on September 1, 2021, using 3 databases-PubMed, Google Scholar, and Web of Science library. Date restrictions were not imposed and search terms covering ocular images, systemic diseases, and artificial intelligence aspects were used. RESULTS Thirty-three papers were included in this systematic review. A spectrum of target diseases was observed, and this included but was not limited to cardio-cerebrovascular diseases, central nervous system diseases, renal dysfunctions, and hepatological diseases. Additionally, one- third of the papers included risk factor predictions for the respective systemic diseases. CONCLUSIONS Ocular image - based artificial intelligence possesses potential diagnostic power to screen various systemic diseases and has also demonstrated the ability to detect Alzheimer and chronic kidney diseases at early stages. Further research is needed to validate these models for real-world implementation.
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Affiliation(s)
- Qingsheng Peng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Clinical and Translational Sciences Program, Duke-NUS Medical School, Singapore
| | | | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore
| | - Tyler Hyungtaek Rim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
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