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Gaire BP, Koronyo Y, Fuchs DT, Shi H, Rentsendorj A, Danziger R, Vit JP, Mirzaei N, Doustar J, Sheyn J, Hampel H, Vergallo A, Davis MR, Jallow O, Baldacci F, Verdooner SR, Barron E, Mirzaei M, Gupta VK, Graham SL, Tayebi M, Carare RO, Sadun AA, Miller CA, Dumitrascu OM, Lahiri S, Gao L, Black KL, Koronyo-Hamaoui M. Alzheimer's disease pathophysiology in the Retina. Prog Retin Eye Res 2024; 101:101273. [PMID: 38759947 DOI: 10.1016/j.preteyeres.2024.101273] [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: 02/11/2023] [Revised: 04/23/2024] [Accepted: 05/10/2024] [Indexed: 05/19/2024]
Abstract
The retina is an emerging CNS target for potential noninvasive diagnosis and tracking of Alzheimer's disease (AD). Studies have identified the pathological hallmarks of AD, including amyloid β-protein (Aβ) deposits and abnormal tau protein isoforms, in the retinas of AD patients and animal models. Moreover, structural and functional vascular abnormalities such as reduced blood flow, vascular Aβ deposition, and blood-retinal barrier damage, along with inflammation and neurodegeneration, have been described in retinas of patients with mild cognitive impairment and AD dementia. Histological, biochemical, and clinical studies have demonstrated that the nature and severity of AD pathologies in the retina and brain correspond. Proteomics analysis revealed a similar pattern of dysregulated proteins and biological pathways in the retina and brain of AD patients, with enhanced inflammatory and neurodegenerative processes, impaired oxidative-phosphorylation, and mitochondrial dysfunction. Notably, investigational imaging technologies can now detect AD-specific amyloid deposits, as well as vasculopathy and neurodegeneration in the retina of living AD patients, suggesting alterations at different disease stages and links to brain pathology. Current and exploratory ophthalmic imaging modalities, such as optical coherence tomography (OCT), OCT-angiography, confocal scanning laser ophthalmoscopy, and hyperspectral imaging, may offer promise in the clinical assessment of AD. However, further research is needed to deepen our understanding of AD's impact on the retina and its progression. To advance this field, future studies require replication in larger and diverse cohorts with confirmed AD biomarkers and standardized retinal imaging techniques. This will validate potential retinal biomarkers for AD, aiding in early screening and monitoring.
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Affiliation(s)
- Bhakta Prasad Gaire
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Yosef Koronyo
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Dieu-Trang Fuchs
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Haoshen Shi
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Altan Rentsendorj
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ron Danziger
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jean-Philippe Vit
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Nazanin Mirzaei
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jonah Doustar
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Julia Sheyn
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Harald Hampel
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Andrea Vergallo
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Miyah R Davis
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ousman Jallow
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Filippo Baldacci
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France; Department of Clinical and Experimental Medicine, Neurology Unit, University of Pisa, Pisa, Italy
| | | | - Ernesto Barron
- Department of Ophthalmology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA; Doheny Eye Institute, Los Angeles, CA, USA
| | - Mehdi Mirzaei
- Department of Clinical Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
| | - Vivek K Gupta
- Department of Clinical Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
| | - Stuart L Graham
- Department of Clinical Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia; Department of Clinical Medicine, Macquarie University, Sydney, NSW, Australia
| | - Mourad Tayebi
- School of Medicine, Western Sydney University, Campbelltown, NSW, Australia
| | - Roxana O Carare
- Department of Clinical Neuroanatomy, University of Southampton, Southampton, UK
| | - Alfredo A Sadun
- Department of Ophthalmology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA; Doheny Eye Institute, Los Angeles, CA, USA
| | - Carol A Miller
- Department of Pathology Program in Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Shouri Lahiri
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Liang Gao
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Keith L Black
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Maya Koronyo-Hamaoui
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Biomedical Sciences, Division of Applied Cell Biology and Physiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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Jin Z, Chen X, Jiang C, Feng X, Zou D, Lu Y, Li J, Ren Q, Zhou C. Predicting the cognitive impairment with multimodal ophthalmic imaging and artificial neural network for community screening. Br J Ophthalmol 2024:bjo-2023-323283. [PMID: 38697799 DOI: 10.1136/bjo-2023-323283] [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: 01/28/2023] [Accepted: 04/18/2024] [Indexed: 05/05/2024]
Abstract
BACKGROUND/AIMS To investigate the comprehensive prediction ability for cognitive impairment in a general elder population using the combination of the multimodal ophthalmic imaging and artificial neural networks. METHODS Patients with cognitive impairment and cognitively healthy individuals were recruited. All subjects underwent medical history, blood pressure measurement, the Montreal Cognitive Assessment, medical optometry, intraocular pressure and custom-built multimodal ophthalmic imaging, which integrated pupillary light reaction, multispectral imaging, laser speckle contrast imaging and retinal oximetry. Multidimensional parameters were analysed by Student's t-test. Logistic regression analysis and back-propagation neural network (BPNN) were used to identify the predictive capability for cognitive impairment. RESULTS This study included 104 cognitive impairment patients (61.5% female; mean (SD) age, 68.3 (9.4) years), and 94 cognitively healthy age-matched and sex-matched subjects (56.4% female; mean (SD) age, 65.9 (7.6) years). The variation of most parameters including decreased pupil constriction amplitude (CA), relative CA, average constriction velocity, venous diameter, venous blood flow and increased centred retinal reflectance in 548 nm (RC548) in cognitive impairment was consistent with previous studies while the reduced flow acceleration index and oxygen metabolism were reported for the first time. Compared with the logistic regression model, BPNN had better predictive performance (accuracy: 0.91 vs 0.69; sensitivity: 93.3% vs 61.70%; specificity: 90.0% vs 68.66%). CONCLUSIONS This study demonstrates retinal spectral signature alteration, neurodegeneration and angiopathy occur concurrently in cognitive impairment. The combination of multimodal ophthalmic imaging and BPNN can be a useful tool for predicting cognitive impairment with high performance for community screening.
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Affiliation(s)
- Zi Jin
- Department of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
- Department of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Xuhui Chen
- Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Chunxia Jiang
- Department of Ophthalmology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Ximeng Feng
- Department of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
- Department of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- Department of Biomedical Engineering, Peking University, Beijing, China
- Institute of Medical Technology, Peking University Health Science Centre, Beijing, China
| | - Da Zou
- Department of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
- Department of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- Department of Biomedical Engineering, Peking University, Beijing, China
- Institute of Medical Technology, Peking University Health Science Centre, Beijing, China
| | - Yanye Lu
- Department of Biomedical Engineering, Peking University, Beijing, China
- Institute of Medical Technology, Peking University Health Science Centre, Beijing, China
| | - Jinying Li
- Department of Ophthalmology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Qiushi Ren
- Department of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
- Department of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- Department of Biomedical Engineering, Peking University, Beijing, China
- Institute of Medical Technology, Peking University Health Science Centre, Beijing, China
| | - Chuanqing Zhou
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China
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Yousefzadeh N, Tran C, Ramirez-Zamora A, Chen J, Fang R, Thai MT. Neuron-level explainable AI for Alzheimer's Disease assessment from fundus images. Sci Rep 2024; 14:7710. [PMID: 38565579 PMCID: PMC10987553 DOI: 10.1038/s41598-024-58121-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 03/26/2024] [Indexed: 04/04/2024] Open
Abstract
Alzheimer's Disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia. Early diagnosis is critical for patients to benefit from potential intervention and treatment. The retina has emerged as a plausible diagnostic site for AD detection owing to its anatomical connection with the brain. However, existing AI models for this purpose have yet to provide a rational explanation behind their decisions and have not been able to infer the stage of the disease's progression. Along this direction, we propose a novel model-agnostic explainable-AI framework, called Granula ̲ r Neuron-le v ̲ el Expl a ̲ iner (LAVA), an interpretation prototype that probes into intermediate layers of the Convolutional Neural Network (CNN) models to directly assess the continuum of AD from the retinal imaging without the need for longitudinal or clinical evaluations. This innovative approach aims to validate retinal vasculature as a biomarker and diagnostic modality for evaluating Alzheimer's Disease. Leveraged UK Biobank cognitive tests and vascular morphological features demonstrate significant promise and effectiveness of LAVA in identifying AD stages across the progression continuum.
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Affiliation(s)
- Nooshin Yousefzadeh
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, USA
| | - Charlie Tran
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA
| | | | - Jinghua Chen
- Department of Ophthalmology, University of Florida, Gainesville, FL, USA
| | - Ruogu Fang
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA.
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
- Center for Cognitive Aging and Memory, University of Florida, Gainesville, FL, USA.
| | - My T Thai
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, USA.
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Koff WC, Anandkumar A, Poland GA. AI and the future of vaccine development. Vaccine 2024; 42:1407-1408. [PMID: 38296704 DOI: 10.1016/j.vaccine.2024.01.059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2024]
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Hasan MM, Phu J, Sowmya A, Meijering E, Kalloniatis M. Artificial intelligence in the diagnosis of glaucoma and neurodegenerative diseases. Clin Exp Optom 2024; 107:130-146. [PMID: 37674264 DOI: 10.1080/08164622.2023.2235346] [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: 02/23/2023] [Accepted: 07/07/2023] [Indexed: 09/08/2023] Open
Abstract
Artificial Intelligence is a rapidly expanding field within computer science that encompasses the emulation of human intelligence by machines. Machine learning and deep learning - two primary data-driven pattern analysis approaches under the umbrella of artificial intelligence - has created considerable interest in the last few decades. The evolution of technology has resulted in a substantial amount of artificial intelligence research on ophthalmic and neurodegenerative disease diagnosis using retinal images. Various artificial intelligence-based techniques have been used for diagnostic purposes, including traditional machine learning, deep learning, and their combinations. Presented here is a review of the literature covering the last 10 years on this topic, discussing the use of artificial intelligence in analysing data from different modalities and their combinations for the diagnosis of glaucoma and neurodegenerative diseases. The performance of published artificial intelligence methods varies due to several factors, yet the results suggest that such methods can potentially facilitate clinical diagnosis. Generally, the accuracy of artificial intelligence-assisted diagnosis ranges from 67-98%, and the area under the sensitivity-specificity curve (AUC) ranges from 0.71-0.98, which outperforms typical human performance of 71.5% accuracy and 0.86 area under the curve. This indicates that artificial intelligence-based tools can provide clinicians with useful information that would assist in providing improved diagnosis. The review suggests that there is room for improvement of existing artificial intelligence-based models using retinal imaging modalities before they are incorporated into clinical practice.
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Affiliation(s)
- Md Mahmudul Hasan
- School of Computer Science and Engineering, University of New South Wales, Kensington, New South Wales, Australia
| | - Jack Phu
- School of Optometry and Vision Science, University of New South Wales, Kensington, Australia
- Centre for Eye Health, University of New South Wales, Sydney, New South Wales, Australia
- School of Medicine (Optometry), Deakin University, Waurn Ponds, Victoria, Australia
| | - Arcot Sowmya
- School of Computer Science and Engineering, University of New South Wales, Kensington, New South Wales, Australia
| | - Erik Meijering
- School of Computer Science and Engineering, University of New South Wales, Kensington, New South Wales, Australia
| | - Michael Kalloniatis
- School of Optometry and Vision Science, University of New South Wales, Kensington, Australia
- School of Medicine (Optometry), Deakin University, Waurn Ponds, Victoria, Australia
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Ashayeri H, Jafarizadeh A, Yousefi M, Farhadi F, Javadzadeh A. Retinal imaging and Alzheimer's disease: a future powered by Artificial Intelligence. Graefes Arch Clin Exp Ophthalmol 2024:10.1007/s00417-024-06394-0. [PMID: 38358524 DOI: 10.1007/s00417-024-06394-0] [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: 08/03/2023] [Revised: 01/22/2024] [Accepted: 01/30/2024] [Indexed: 02/16/2024] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative condition that primarily affects brain tissue. Because the retina and brain share the same embryonic origin, visual deficits have been reported in AD patients. Artificial Intelligence (AI) has recently received a lot of attention due to its immense power to process and detect image hallmarks and make clinical decisions (like diagnosis) based on images. Since retinal changes have been reported in AD patients, AI is being proposed to process images to predict, diagnose, and prognosis AD. As a result, the purpose of this review was to discuss the use of AI trained on retinal images of AD patients. According to previous research, AD patients experience retinal thickness and retinal vessel density changes, which can occasionally occur before the onset of the disease's clinical symptoms. AI and machine vision can detect and use these changes in the domains of disease prediction, diagnosis, and prognosis. As a result, not only have unique algorithms been developed for this condition, but also databases such as the Retinal OCTA Segmentation dataset (ROSE) have been constructed for this purpose. The achievement of high accuracy, sensitivity, and specificity in the classification of retinal images between AD and healthy groups is one of the major breakthroughs in using AI based on retinal images for AD. It is fascinating that researchers could pinpoint individuals with a positive family history of AD based on the properties of their eyes. In conclusion, the growing application of AI in medicine promises its future position in processing different aspects of patients with AD, but we need cohort studies to determine whether it can help to follow up with healthy persons at risk of AD for a quicker diagnosis or assess the prognosis of patients with AD.
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Affiliation(s)
- Hamidreza Ashayeri
- Neuroscience Research Center (NSRC), Tabriz University of Medical Sciences, Tabriz, Iran
| | - Ali Jafarizadeh
- Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Milad Yousefi
- Faculty of Mathematics, Statistics and Computer Sciences, University of Tabriz, Tabriz, Iran
| | - Fereshteh Farhadi
- Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Alireza Javadzadeh
- Department of Ophthalmology, Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz, Iran.
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Tran C, Shen K, Liu K, Ashok A, Ramirez-Zamora A, Chen J, Li Y, Fang R. Deep learning predicts prevalent and incident Parkinson's disease from UK Biobank fundus imaging. Sci Rep 2024; 14:3637. [PMID: 38351326 PMCID: PMC10864361 DOI: 10.1038/s41598-024-54251-1] [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: 05/02/2023] [Accepted: 02/10/2024] [Indexed: 02/16/2024] Open
Abstract
Parkinson's disease is the world's fastest-growing neurological disorder. Research to elucidate the mechanisms of Parkinson's disease and automate diagnostics would greatly improve the treatment of patients with Parkinson's disease. Current diagnostic methods are expensive and have limited availability. Considering the insidious and preclinical onset and progression of the disease, a desirable screening should be diagnostically accurate even before the onset of symptoms to allow medical interventions. We highlight retinal fundus imaging, often termed a window to the brain, as a diagnostic screening modality for Parkinson's disease. We conducted a systematic evaluation of conventional machine learning and deep learning techniques to classify Parkinson's disease from UK Biobank fundus imaging. Our results suggest Parkinson's disease individuals can be differentiated from age and gender-matched healthy subjects with 68% accuracy. This accuracy is maintained when predicting either prevalent or incident Parkinson's disease. Explainability and trustworthiness are enhanced by visual attribution maps of localized biomarkers and quantified metrics of model robustness to data perturbations.
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Affiliation(s)
- Charlie Tran
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA
| | - Kai Shen
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA
| | - Kang Liu
- Department of Physics, University of Florida, Gainesville, FL, 32661, USA
| | - Akshay Ashok
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, 32611, USA
| | | | - Jinghua Chen
- Department of Ophthalmology, University of Florida, Gainesville, FL, 32661, USA
| | - Yulin Li
- Department of Biostatistics, University of Florida, Gainesville, FL, 32661, USA
| | - Ruogu Fang
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA.
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, 1275 Center Drive, PO Box 116131, Gainesville, FL, 32611-6131, USA.
- Center for Cognitive Aging and Memory, University of Florida, Gainesville, FL, 32611, USA.
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Bahr T, Vu TA, Tuttle JJ, Iezzi R. Deep Learning and Machine Learning Algorithms for Retinal Image Analysis in Neurodegenerative Disease: Systematic Review of Datasets and Models. Transl Vis Sci Technol 2024; 13:16. [PMID: 38381447 PMCID: PMC10893898 DOI: 10.1167/tvst.13.2.16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 11/26/2023] [Indexed: 02/22/2024] Open
Abstract
Purpose Retinal images contain rich biomarker information for neurodegenerative disease. Recently, deep learning models have been used for automated neurodegenerative disease diagnosis and risk prediction using retinal images with good results. Methods In this review, we systematically report studies with datasets of retinal images from patients with neurodegenerative diseases, including Alzheimer's disease, Huntington's disease, Parkinson's disease, amyotrophic lateral sclerosis, and others. We also review and characterize the models in the current literature which have been used for classification, regression, or segmentation problems using retinal images in patients with neurodegenerative diseases. Results Our review found several existing datasets and models with various imaging modalities primarily in patients with Alzheimer's disease, with most datasets on the order of tens to a few hundred images. We found limited data available for the other neurodegenerative diseases. Although cross-sectional imaging data for Alzheimer's disease is becoming more abundant, datasets with longitudinal imaging of any disease are lacking. Conclusions The use of bilateral and multimodal imaging together with metadata seems to improve model performance, thus multimodal bilateral image datasets with patient metadata are needed. We identified several deep learning tools that have been useful in this context including feature extraction algorithms specifically for retinal images, retinal image preprocessing techniques, transfer learning, feature fusion, and attention mapping. Importantly, we also consider the limitations common to these models in real-world clinical applications. Translational Relevance This systematic review evaluates the deep learning models and retinal features relevant in the evaluation of retinal images of patients with neurodegenerative disease.
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Affiliation(s)
- Tyler Bahr
- Mayo Clinic, Department of Ophthalmology, Rochester, MN, USA
| | - Truong A. Vu
- University of the Incarnate Word, School of Osteopathic Medicine, San Antonio, TX, USA
| | - Jared J. Tuttle
- University of Texas Health Science Center at San Antonio, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
| | - Raymond Iezzi
- Mayo Clinic, Department of Ophthalmology, Rochester, MN, USA
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9
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Shi XH, Ju L, Dong L, Zhang RH, Shao L, Yan YN, Wang YX, Fu XF, Chen YZ, Ge ZY, Wei WB. Deep Learning Models for the Screening of Cognitive Impairment Using Multimodal Fundus Images. Ophthalmol Retina 2024:S2468-6530(24)00045-9. [PMID: 38280426 DOI: 10.1016/j.oret.2024.01.019] [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: 07/31/2023] [Revised: 01/03/2024] [Accepted: 01/19/2024] [Indexed: 01/29/2024]
Abstract
OBJECTIVE We aimed to develop a deep learning system capable of identifying subjects with cognitive impairment quickly and easily based on multimodal ocular images. DESIGN Cross sectional study. SUBJECTS Participants of Beijing Eye Study 2011 and patients attending Beijing Tongren Eye Center and Beijing Tongren Hospital Physical Examination Center. METHODS We trained and validated a deep learning algorithm to assess cognitive impairment using retrospectively collected data from the Beijing Eye Study 2011. Cognitive impairment was defined as a Mini-Mental State Examination score < 24. Based on fundus photographs and OCT images, we developed 5 models based on the following sets of images: macula-centered fundus photographs, optic disc-centered fundus photographs, fundus photographs of both fields, OCT images, and fundus photographs of both fields with OCT (multimodal). The performance of the models was evaluated and compared in an external validation data set, which was collected from patients attending Beijing Tongren Eye Center and Beijing Tongren Hospital Physical Examination Center. MAIN OUTCOME MEASURES Area under the curve (AUC). RESULTS A total of 9424 retinal photographs and 4712 OCT images were used to develop the model. The external validation sets from each center included 1180 fundus photographs and 590 OCT images. Model comparison revealed that the multimodal performed best, achieving an AUC of 0.820 in the internal validation set, 0.786 in external validation set 1, and 0.784 in external validation set 2. We evaluated the performance of the multi-model in different sexes and different age groups; there were no significant differences. The heatmap analysis showed that signals around the optic disc in fundus photographs and the retina and choroid around the macular and optic disc regions in OCT images were used by the multimodal to identify participants with cognitive impairment. CONCLUSIONS Fundus photographs and OCT can provide valuable information on cognitive function. Multimodal models provide richer information compared with single-mode models. Deep learning algorithms based on multimodal retinal images may be capable of screening cognitive impairment. This technique has potential value for broader implementation in community-based screening or clinic settings. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Xu Han Shi
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Lie Ju
- Beijing Airdoc Technology Co., Ltd., Beijing, China; Augmented Intelligence and Multimodal Analytics (AIM) for Health Lab, Faculty of Information Technology, Monash University, Clayton, Australia; Faculty of Engineering, Monash University, Clayton, Australia
| | - Li Dong
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Rui Heng Zhang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Lei Shao
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Yan Ni Yan
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Ya Xing Wang
- Beijing Ophthalmology and Visual Science Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Institute of Ophthalmology, Capital Medical University, Beijing, China
| | - Xue Fei Fu
- Beijing Airdoc Technology Co., Ltd., Beijing, China
| | | | - Zong Yuan Ge
- Beijing Airdoc Technology Co., Ltd., Beijing, China; Augmented Intelligence and Multimodal Analytics (AIM) for Health Lab, Faculty of Information Technology, Monash University, Clayton, Australia; Faculty of Engineering, Monash University, Clayton, Australia
| | - Wen Bin Wei
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
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Yoon JM, Lim CY, Noh H, Nam SW, Jun SY, Kim MJ, Song MY, Jang H, Kim HJ, Seo SW, Na DL, Chung MJ, Ham DI, Kim K. Enhancing foveal avascular zone analysis for Alzheimer's diagnosis with AI segmentation and machine learning using multiple radiomic features. Sci Rep 2024; 14:1841. [PMID: 38253722 PMCID: PMC10810355 DOI: 10.1038/s41598-024-51612-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 01/07/2024] [Indexed: 01/24/2024] Open
Abstract
We propose a hybrid technique that employs artificial intelligence (AI)-based segmentation and machine learning classification using multiple features extracted from the foveal avascular zone (FAZ)-a retinal biomarker for Alzheimer's disease-to improve the disease diagnostic performance. Imaging data of optical coherence tomography angiography from 37 patients with Alzheimer's disease and 48 healthy controls were investigated. The presence or absence of brain amyloids was confirmed using amyloid positron emission tomography. In the superficial capillary plexus of the angiography scans, the FAZ was automatically segmented using an AI method to extract multiple biomarkers (area, solidity, compactness, roundness, and eccentricity), which were paired with clinical data (age and sex) as common correction variables. We used a light-gradient boosting machine (a light-gradient boosting machine is a machine learning algorithm based on trees utilizing gradient boosting) to diagnose Alzheimer's disease by integrating the corresponding multiple radiomic biomarkers. Fivefold cross-validation was applied for analysis, and the diagnostic performance for Alzheimer's disease was determined by the area under the curve. The proposed hybrid technique achieved an area under the curve of [Formula: see text]%, outperforming the existing single-feature (area) criteria by over 13%. Furthermore, in the holdout test set, the proposed technique exhibited a 14% improvement compared to single features, achieving an area under the curve of 72.0± 4.8%. Based on these facts, we have demonstrated the effectiveness of our technology in achieving significant performance improvements in FAZ-based Alzheimer's diagnosis research through the use of multiple radiomic biomarkers (area, solidity, compactness, roundness, and eccentricity).
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Affiliation(s)
- Je Moon Yoon
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
| | - Chae Yeon Lim
- Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul, 06351, Republic of Korea
| | - Hoon Noh
- Hangil Eye Hospital, 35 Bupyeong-daero, Bupyeong-gu, Incheon, 21388, Republic of Korea
| | - Seung Wan Nam
- Hangil Eye Hospital, 35 Bupyeong-daero, Bupyeong-gu, Incheon, 21388, Republic of Korea
- Department of Ophthalmology, Catholic Kwandong University College of Medicine, 35 Bupyeong-daero, Bupyeong-gu, Incheon, 21388, Republic of Korea
| | - Sung Yeon Jun
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
| | - Min Ji Kim
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
| | - Mi Yeon Song
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
| | - Hyemin Jang
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hee Jin Kim
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
| | - Sang Won Seo
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Duk L Na
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
- Happymind Clinic, Seoul, Republic of Korea
| | - Myung Jin Chung
- Department of Data Convergence and Future Medicine, Sungkyunkwan University School of Medicine, Suwon, 16419, Republic of Korea
- Department of Radiology and AI Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
| | - Don-Il Ham
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea.
| | - Kyungsu Kim
- Medical AI Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, 06351, Republic of Korea.
- Department of Data Convergence and Future Medicine, Sungkyunkwan University School of Medicine, Suwon, 16419, Republic of Korea.
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11
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Alber J, Bouwman F, den Haan J, Rissman RA, De Groef L, Koronyo‐Hamaoui M, Lengyel I, Thal DR. Retina pathology as a target for biomarkers for Alzheimer's disease: Current status, ophthalmopathological background, challenges, and future directions. Alzheimers Dement 2024; 20:728-740. [PMID: 37917365 PMCID: PMC10917008 DOI: 10.1002/alz.13529] [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: 08/07/2023] [Revised: 09/30/2023] [Accepted: 10/05/2023] [Indexed: 11/04/2023]
Abstract
There is emerging evidence that amyloid beta protein (Aβ) and tau-related lesions in the retina are associated with Alzheimer's disease (AD). Aβ and hyperphosphorylated (p)-tau deposits have been described in the retina and were associated with small amyloid spots visualized by in vivo imaging techniques as well as degeneration of the retina. These changes correlate with brain amyloid deposition as determined by histological quantification, positron emission tomography (PET) or clinical diagnosis of AD. However, the literature is not coherent on these histopathological and in vivo imaging findings. One important reason for this is the variability in the methods and the interpretation of findings across different studies. In this perspective, we indicate the critical methodological deviations among different groups and suggest a roadmap moving forward on how to harmonize (i) histopathologic examination of retinal tissue; (ii) in vivo imaging among different methods, devices, and interpretation algorithms; and (iii) inclusion/exclusion criteria for studies aiming at retinal biomarker validation.
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Affiliation(s)
- Jessica Alber
- George and Anne Ryan Institute for Neuroscience, Department of Biomedical and Pharmaceutical SciencesUniversity of Rhode IslandKingstonRhode IslandUSA
- Butler Hospital Memory & Aging ProgramProvidenceRhode IslandUSA
| | - Femke Bouwman
- Amsterdam UMC, location VUmcAlzheimer Center, Department of NeurologyAmsterdamThe Netherlands
| | - Jurre den Haan
- Amsterdam UMC, location VUmcAlzheimer Center, Department of NeurologyAmsterdamThe Netherlands
| | - Robert A. Rissman
- Alzheimer's Therapeutic Research InstituteKeck School of Medicine of the University of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Lies De Groef
- Cellular Communication and Neurodegeneration Research Group, Animal Physiology and Neurobiology Division, Department of BiologyLeuven Brain InstituteKU LeuvenLeuvenBelgium
| | - Maya Koronyo‐Hamaoui
- Departments of Neurosurgery, Neurology, and Biomedical SciencesMaxine Dunitz Neurosurgical Research Institute, Cedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| | - Imre Lengyel
- The Wellcome‐Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry and Biomedical ScienceQueen's University BelfastBelfastUK
| | - Dietmar Rudolf Thal
- Laboratory of NeuropathologyDepartment of Imaging and Pathology, and Leuven Brain Institute, KU LeuvenLeuvenBelgium
- Department of PathologyUZ LeuvenLeuvenBelgium
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12
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Wisely CE, Richardson A, Henao R, Robbins CB, Ma JP, Wang D, Johnson KG, Liu AJ, Grewal DS, Fekrat S. A Convolutional Neural Network Using Multimodal Retinal Imaging for Differentiation of Mild Cognitive Impairment from Normal Cognition. OPHTHALMOLOGY SCIENCE 2024; 4:100355. [PMID: 37877003 PMCID: PMC10591009 DOI: 10.1016/j.xops.2023.100355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 06/08/2023] [Accepted: 06/14/2023] [Indexed: 10/26/2023]
Abstract
Purpose To develop a machine learning tool capable of differentiating eyes of subjects with normal cognition from those with mild cognitive impairment (MCI) using OCT and OCT angiography (OCTA). Design Evaluation of a diagnostic technology. Participants Subjects with normal cognition were compared to subjects with MCI. Methods A multimodal convolutional neural network (CNN) was built to predict likelihood of MCI from ganglion cell-inner plexiform layer (GC-IPL) thickness maps, OCTA images, and quantitative data including patient characteristics. Main Outcome Measures Area under the receiver operating characteristic curve (AUC) and summaries of the confusion matrix (sensitivity and specificity) were used as performance metrics for the prediction outputs of the CNN. Results Images from 236 eyes of 129 cognitively normal subjects and 154 eyes of 80 MCI subjects were used for training, validating, and testing the CNN. When applied to the independent test set using inputs including GC-IPL thickness maps, OCTA images, and quantitative OCT and OCTA data, the AUC value for the CNN was 0.809 (95% confidence interval [CI]: 0.681-0.937). This model achieved a sensitivity of 79% and specificity of 83%. The AUC value for GC-IPL thickness maps alone was 0.681 (95% CI: 0.529-0.832), for OCTA images alone was 0.625 (95% CI: 0.466-0.784) and for both GC-IPL maps and OCTA images was 0.693 (95% CI: 0.543-0.843). Models using quantitative data alone were also tested, with a model using quantitative data derived from images, 0.960 (95% CI: 0.902-1.00), outperforming a model using demographic data alone, 0.580 (95% CI: 0.417-0.742). Conclusions This novel CNN was able to identify an MCI diagnosis using an independent test set comprised of OCT and OCTA images and quantitative data. The GC-IPL thickness maps provided more useful decision support than the OCTA images. The addition of quantitative data inputs also provided significant decision support to the CNN to identify individuals with MCI. Quantitative imaging metrics provided superior decision support than demographic data. Financial Disclosures Proprietary or commercial disclosure may be found after the references.
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Affiliation(s)
- C. Ellis Wisely
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina
- iMIND Study Group, Duke University School of Medicine, Durham, North Carolina
| | - Alexander Richardson
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina
- iMIND Study Group, Duke University School of Medicine, Durham, North Carolina
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
| | - Ricardo Henao
- iMIND Study Group, Duke University School of Medicine, Durham, North Carolina
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
| | - Cason B. Robbins
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina
- iMIND Study Group, Duke University School of Medicine, Durham, North Carolina
| | - Justin P. Ma
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina
- iMIND Study Group, Duke University School of Medicine, Durham, North Carolina
| | - Dong Wang
- iMIND Study Group, Duke University School of Medicine, Durham, North Carolina
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
| | - Kim G. Johnson
- Department of Neurology, Duke University School of Medicine, Durham, North Carolina
| | - Andy J. Liu
- Department of Neurology, Duke University School of Medicine, Durham, North Carolina
| | - Dilraj S. Grewal
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina
- iMIND Study Group, Duke University School of Medicine, Durham, North Carolina
| | - Sharon Fekrat
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina
- iMIND Study Group, Duke University School of Medicine, Durham, North Carolina
- Department of Neurology, Duke University School of Medicine, Durham, North Carolina
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13
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Hanif A, Prajna NV, Lalitha P, NaPier E, Parker M, Steinkamp P, Keenan JD, Campbell JP, Song X, Redd TK. Assessing the Impact of Image Quality on Deep Learning Classification of Infectious Keratitis. OPHTHALMOLOGY SCIENCE 2023; 3:100331. [PMID: 37920421 PMCID: PMC10618822 DOI: 10.1016/j.xops.2023.100331] [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: 01/21/2023] [Revised: 04/13/2023] [Accepted: 05/08/2023] [Indexed: 11/04/2023]
Abstract
Objective To investigate the impact of corneal photograph quality on convolutional neural network (CNN) predictions. Design A CNN trained to classify bacterial and fungal keratitis was evaluated using photographs of ulcers labeled according to 5 corneal image quality parameters: eccentric gaze direction, abnormal eyelid position, over/under-exposure, inadequate focus, and malpositioned light reflection. Participants All eligible subjects with culture and stain-proven bacterial and/or fungal ulcers presenting to Aravind Eye Hospital in Madurai, India, between January 1, 2021 and December 31, 2021. Methods Convolutional neural network classification performance was compared for each quality parameter, and gradient class activation heatmaps were generated to visualize regions of highest influence on CNN predictions. Main Outcome Measures Area under the receiver operating characteristic and precision recall curves were calculated to quantify model performance. Bootstrapped confidence intervals were used for statistical comparisons. Logistic loss was calculated to measure individual prediction accuracy. Results Individual presence of either light reflection or eyelids obscuring the corneal surface was associated with significantly higher CNN performance. No other quality parameter significantly influenced CNN performance. Qualitative review of gradient class activation heatmaps generally revealed the infiltrate as having the highest diagnostic relevance. Conclusions The CNN demonstrated expert-level performance regardless of image quality. Future studies may investigate use of smartphone cameras and image sets with greater variance in image quality to further explore the influence of these parameters on model performance. Financial Disclosures Proprietary or commercial disclosure may be found after the references.
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Affiliation(s)
- Adam Hanif
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | | | | | - Erin NaPier
- John A. Burns School of Medicine, University of Hawai'i, Honolulu, Hawaii
| | - Maria Parker
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Peter Steinkamp
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Jeremy D. Keenan
- Francis I. Proctor Foundation, University of California, San Francisco, San Francisco, California
| | - J. Peter Campbell
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Xubo Song
- Department of Medical Informatics and Clinical Epidemiology and Program of Computer Science and Electrical Engineering, Oregon Health & Science University, Portland, Oregon
| | - Travis K. Redd
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
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14
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Li D, Ran AR, Cheung CY, Prince JL. Deep learning in optical coherence tomography: Where are the gaps? Clin Exp Ophthalmol 2023; 51:853-863. [PMID: 37245525 PMCID: PMC10825778 DOI: 10.1111/ceo.14258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 04/24/2023] [Accepted: 05/03/2023] [Indexed: 05/30/2023]
Abstract
Optical coherence tomography (OCT) is a non-invasive optical imaging modality, which provides rapid, high-resolution and cross-sectional morphology of macular area and optic nerve head for diagnosis and managing of different eye diseases. However, interpreting OCT images requires experts in both OCT images and eye diseases since many factors such as artefacts and concomitant diseases can affect the accuracy of quantitative measurements made by post-processing algorithms. Currently, there is a growing interest in applying deep learning (DL) methods to analyse OCT images automatically. This review summarises the trends in DL-based OCT image analysis in ophthalmology, discusses the current gaps, and provides potential research directions. DL in OCT analysis shows promising performance in several tasks: (1) layers and features segmentation and quantification; (2) disease classification; (3) disease progression and prognosis; and (4) referral triage level prediction. Different studies and trends in the development of DL-based OCT image analysis are described and the following challenges are identified and described: (1) public OCT data are scarce and scattered; (2) models show performance discrepancies in real-world settings; (3) models lack of transparency; (4) there is a lack of societal acceptance and regulatory standards; and (5) OCT is still not widely available in underprivileged areas. More work is needed to tackle the challenges and gaps, before DL is further applied in OCT image analysis for clinical use.
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Affiliation(s)
- Dawei Li
- College of Future Technology, Peking University, Beijing, China
| | - An Ran Ran
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Carol Y. Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jerry L. Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA
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15
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Gao H, Zhao S, Zheng G, Wang X, Zhao R, Pan Z, Li H, Lu F, Shen M. Using a dual-stream attention neural network to characterize mild cognitive impairment based on retinal images. Comput Biol Med 2023; 166:107411. [PMID: 37738896 DOI: 10.1016/j.compbiomed.2023.107411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/08/2023] [Accepted: 08/27/2023] [Indexed: 09/24/2023]
Abstract
Mild cognitive impairment (MCI) is a critical transitional stage between normal cognition and dementia, for which early detection is crucial for timely intervention. Retinal imaging has been shown as a promising potential biomarker for MCI. This study aimed to develop a dual-stream attention neural network to classify individuals with MCI based on multi-modal retinal images. Our approach incorporated a cross-modality fusion technique, a variable scale dense residual model, and a multi-classifier mechanism within the dual-stream network. The model utilized a residual module to extract image features and employed a multi-level feature aggregation method to capture complex context information. Self-attention and cross-attention modules were utilized at each convolutional layer to fuse features from optical coherence tomography (OCT) and fundus modalities, resulting in multiple output losses. The neural network was applied to classify individuals with MCI, Alzheimer's disease, and control participants with normal cognition. Through fine-tuning the pre-trained model, we classified community-dwelling participants into two groups based on cognitive impairment test scores. To identify retinal imaging biomarkers associated with accurate prediction, we used the Gradient-weighted Class Activation Mapping technique. The proposed method achieved high precision rates of 84.96% and 80.90% in classifying MCI and positive test scores for cognitive impairment, respectively. Notably, changes in the optic nerve head on fundus photographs or OCT images among patients with MCI were not used to discriminate patients from the control group. These findings demonstrate the potential of our approach in identifying individuals with MCI and emphasize the significance of retinal imaging for early detection of cognitive impairment.
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Affiliation(s)
- Hebei Gao
- School of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou, 325035, China; Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Eye Hospital, Wenzhou Medical University, Wenzhou, 325000, China
| | - Shuaiye Zhao
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China
| | - Gu Zheng
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Eye Hospital, Wenzhou Medical University, Wenzhou, 325000, China
| | - Xinmin Wang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Eye Hospital, Wenzhou Medical University, Wenzhou, 325000, China
| | - Runyi Zhao
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Eye Hospital, Wenzhou Medical University, Wenzhou, 325000, China
| | - Zhigeng Pan
- School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Hong Li
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Fan Lu
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Eye Hospital, Wenzhou Medical University, Wenzhou, 325000, China.
| | - Meixiao Shen
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Eye Hospital, Wenzhou Medical University, Wenzhou, 325000, China.
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16
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Chaitanuwong P, Singhanetr P, Chainakul M, Arjkongharn N, Ruamviboonsuk P, Grzybowski A. Potential Ocular Biomarkers for Early Detection of Alzheimer's Disease and Their Roles in Artificial Intelligence Studies. Neurol Ther 2023; 12:1517-1532. [PMID: 37468682 PMCID: PMC10444735 DOI: 10.1007/s40120-023-00526-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 07/03/2023] [Indexed: 07/21/2023] Open
Abstract
Alzheimer's disease (AD) is the leading cause of dementia worldwide. Early detection is believed to be essential to disease management because it enables physicians to initiate treatment in patients with early-stage AD (early AD), with the possibility of stopping the disease or slowing disease progression, preserving function and ultimately reducing disease burden. The purpose of this study was to review prior research on the use of eye biomarkers and artificial intelligence (AI) for detecting AD and early AD. The PubMed database was searched to identify studies for review. Ocular biomarkers in AD research and AI research on AD were reviewed and summarized. According to numerous studies, there is a high likelihood that ocular biomarkers can be used to detect early AD: tears, corneal nerves, retina, visual function and, in particular, eye movement tracking have been identified as ocular biomarkers with the potential to detect early AD. However, there is currently no ocular biomarker that can be used to definitely detect early AD. A few studies that used AI with ocular biomarkers to detect AD reported promising results, demonstrating that using AI with ocular biomarkers through multimodal imaging could improve the accuracy of identifying AD patients. This strategy may become a screening tool for detecting early AD in older patients prior to the onset of AD symptoms.
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Affiliation(s)
- Pareena Chaitanuwong
- Ophthalmology Department, Rajavithi Hospital, Ministry of Public Health, Bangkok, Thailand
- Department of Ophthalmology, Faculty of Medicine, Rangsit University, Bangkok, Thailand
| | - Panisa Singhanetr
- Mettapracharak Eye Institute, Mettapracharak (Wat Rai Khing) Hospital, Nakhon Pathom, Thailand
| | - Methaphon Chainakul
- Ophthalmology Department, Rajavithi Hospital, Ministry of Public Health, Bangkok, Thailand
- Department of Ophthalmology, Faculty of Medicine, Rangsit University, Bangkok, Thailand
| | - Niracha Arjkongharn
- Ophthalmology Department, Rajavithi Hospital, Ministry of Public Health, Bangkok, Thailand
- Department of Ophthalmology, Faculty of Medicine, Rangsit University, Bangkok, Thailand
| | - Paisan Ruamviboonsuk
- Ophthalmology Department, Rajavithi Hospital, Ministry of Public Health, Bangkok, Thailand
- Department of Ophthalmology, Faculty of Medicine, Rangsit University, Bangkok, Thailand
| | - Andrzej Grzybowski
- Institute of Research in Ophthalmology, Foundation for Ophthalmology Development, Mickiewicza 24/3B, 60-836, Poznan, Poland.
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17
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Elsabagh AA, Elhadary M, Elsayed B, Elshoeibi AM, Ferih K, Kaddoura R, Alkindi S, Alshurafa A, Alrasheed M, Alzayed A, Al-Abdulmalek A, Altooq JA, Yassin M. Artificial intelligence in sickle disease. Blood Rev 2023; 61:101102. [PMID: 37355428 DOI: 10.1016/j.blre.2023.101102] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/12/2023] [Accepted: 06/01/2023] [Indexed: 06/26/2023]
Abstract
Artificial intelligence (AI) is rapidly becoming an established arm in medical sciences and clinical practice in numerous medical fields. Its implications have been rising and are being widely used in research, diagnostics, and treatment options for many pathologies, including sickle cell disease (SCD). AI has started new ways to improve risk stratification and diagnosing SCD complications early, allowing rapid intervention and reallocation of resources to high-risk patients. We reviewed the literature for established and new AI applications that may enhance management of SCD through advancements in diagnosing SCD and its complications, risk stratification, and the effect of AI in establishing an individualized approach in managing SCD patients in the future. Aim: to review the benefits and drawbacks of resources utilizing AI in clinical practice for improving the management for SCD cases.
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Affiliation(s)
| | | | - Basel Elsayed
- College of Medicine, QU Health, Qatar University, Doha, Qatar
| | | | - Khaled Ferih
- College of Medicine, QU Health, Qatar University, Doha, Qatar
| | - Rasha Kaddoura
- Pharmacy Department, Heart Hospital, Hamad Medical Corporation (HMC), Doha, Qatar
| | - Salam Alkindi
- Professor of Hematology, Sultan Qaboos University, Oman
| | - Awni Alshurafa
- Department of Hematology, Hamad Medical Corporation (HMC), Doha, Qatar
| | - Mona Alrasheed
- Hematology Unit, Department of Medicine, Aladnan Hospital, Ministry of Health, Kuwait
| | | | | | | | - Mohamed Yassin
- Hematology Section, Medical Oncology, National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation (HMC), Doha, Qatar.
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18
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Kao CC, Hsieh HM, Chang YC, Chu HC, Yang YH, Sheu SJ. Optical Coherence Tomography Assessment of Macular Thickness in Alzheimer's Dementia with Different Neuropsychological Severities. J Pers Med 2023; 13:1118. [PMID: 37511731 PMCID: PMC10381874 DOI: 10.3390/jpm13071118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 07/03/2023] [Accepted: 07/08/2023] [Indexed: 07/30/2023] Open
Abstract
This retrospective case-control study aimed to investigate associations between disease severity of Alzheimer's dementia (AD) and macular thickness. Data of patients with AD who were under medication (n = 192) between 2013 and 2020, as well as an age- and sex-matched control group (n = 200) with normal cognitive function, were included. AD patients were divided into subgroups according to scores of the Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR). Macular thickness was analyzed via the Early Treatment Diabetic Retinopathy Study (ETDRS) grid map. AD patients had significant reductions in full macula layers, including inner circle, outer inferior area, and outer nasal area of the macula. Similar retinal thinning was noted in ganglion cells and inner plexiform layers. Advanced AD patients (MMSE score < 18 or CDR ≥ 1) showed more advanced reduction of macular thickness than the AD group (CDR = 0.5 or MMSE ≥ 18), indicating that severe cognitive impairment was associated with thinner macular thickness. Advanced AD is associated with significant macula thinning in full retina and inner plexiform layers, especially at the inner circle of the macula. Macular thickness may be a useful biomarker of AD disease severity. Retinal imaging may be a non-invasive, low-cost surrogate for AD.
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Affiliation(s)
- Chia-Chen Kao
- Department of Ophthalmology, Kaohsiung Medical University Hospital, Kaohsiung 80756, Taiwan
- Department of Ophthalmology, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Hui-Min Hsieh
- Department of Public Health, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung 80756, Taiwan
- Department of Community Medicine, Kaohsiung Medical University Hospital, Kaohsiung 80756, Taiwan
- Center for Big Data Research, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Research Center for Precision Environmental Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Yo-Chen Chang
- Department of Ophthalmology, Kaohsiung Medical University Hospital, Kaohsiung 80756, Taiwan
- Department of Ophthalmology, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Department of Ophthalmology, Kaohsiung Municipal Siaogang Hospital, Kaohsiung 812, Taiwan
| | - Hui-Chen Chu
- Department of Ophthalmology, Kaohsiung Medical University Hospital, Kaohsiung 80756, Taiwan
| | - Yuan-Han Yang
- Department of Neurology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Neuroscience Research Center, Kaohsiung Medical University, Kaohsiung 812, Taiwan
- Department of Neurology, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung Medical University Hospital, Kaohsiung 80756, Taiwan
- Post-Baccalaureate Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Shwu-Jiuan Sheu
- Department of Ophthalmology, Kaohsiung Medical University Hospital, Kaohsiung 80756, Taiwan
- Department of Ophthalmology, Kaohsiung Medical University, Kaohsiung 807, Taiwan
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Luengnaruemitchai G, Kaewmahanin W, Munthuli A, Phienphanich P, Puangarom S, Sangchocanonta S, Jariyakosol S, Hirunwiwatkul P, Tantibundhit C. Alzheimer's Together with Mild Cognitive Impairment Screening Using Polar Transformation of Middle Zone of Fundus Images Based Deep Learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083188 DOI: 10.1109/embc40787.2023.10340463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) are considered an increasing major health problem in elderlies. However, current clinical methods of Alzheimer's detection are expensive and difficult to access, making the detection inconvenient and unsuitable for developing countries such as Thailand. Thus, we developed a method of AD together with MCI screening by fine-tuning a pre-trained Densely Connected Convolutional Network (DenseNet-121) model using the middle zone of polar transformed fundus image. The polar transformation in the middle zone of the fundus is a key factor helping the model to extract features more effectively and that enhances the model accuracy. The dataset was divided into 2 groups: normal and abnormal (AD and MCI). This method can classify between normal and abnormal patients with 96% accuracy, 99% sensitivity, 90% specificity, 95% precision, and 97% F1 score. Parts of both MCI and AD input images that most impact the classification score visualized by Grad-CAM++ focus in superior and inferior retinal quadrants.Clinical relevance- The parts of both MCI and AD input images that have the most impact the classification score (visualized by Grad-CAM++) are superior and inferior retinal quadrants. Polar transformation of the middle zone of retinal fundus images is a key factor that enhances the classification accuracy.
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20
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Lee T, Rivera A, Brune M, Kundu A, Haystead A, Winslow L, Kundu R, Wisely CE, Robbins CB, Henao R, Grewal DS, Fekrat S. Convolutional Neural Network-Based Automated Quality Assessment of OCT and OCT Angiography Image Maps in Individuals With Neurodegenerative Disease. Transl Vis Sci Technol 2023; 12:30. [PMID: 37389540 PMCID: PMC10318591 DOI: 10.1167/tvst.12.6.30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 06/04/2023] [Indexed: 07/01/2023] Open
Abstract
Purpose To train and test convolutional neural networks (CNNs) to automate quality assessment of optical coherence tomography (OCT) and OCT angiography (OCTA) images in patients with neurodegenerative disease. Methods Patients with neurodegenerative disease were enrolled in the Duke Eye Multimodal Imaging in Neurodegenerative Disease Study. Image inputs were ganglion cell-inner plexiform layer (GC-IPL) thickness maps and fovea-centered 6-mm × 6-mm OCTA scans of the superficial capillary plexus (SCP). Two trained graders manually labeled all images for quality (good versus poor). Interrater reliability (IRR) of manual quality assessment was calculated for a subset of each image type. Images were split into train, validation, and test sets in a 70%/15%/15% split. An AlexNet-based CNN was trained using these labels and evaluated with area under the receiver operating characteristic (AUC) and summaries of the confusion matrix. Results A total of 1465 GC-IPL thickness maps (1217 good and 248 poor quality) and 2689 OCTA scans of the SCP (1797 good and 892 poor quality) served as model inputs. The IRR of quality assessment agreement by two graders was 97% and 90% for the GC-IPL maps and OCTA scans, respectively. The AlexNet-based CNNs trained to assess quality of the GC-IPL images and OCTA scans achieved AUCs of 0.990 and 0.832, respectively. Conclusions CNNs can be trained to accurately differentiate good- from poor-quality GC-IPL thickness maps and OCTA scans of the macular SCP. Translational Relevance Since good-quality retinal images are critical for the accurate assessment of microvasculature and structure, incorporating an automated image quality sorter may obviate the need for manual image review.
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Affiliation(s)
- Terry Lee
- iMIND Study Group, Duke University School of Medicine, Durham, NC, USA
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
| | - Alexandra Rivera
- iMIND Study Group, Duke University School of Medicine, Durham, NC, USA
- Pratt School of Engineering, Duke University, Durham, NC, USA
| | - Matthew Brune
- iMIND Study Group, Duke University School of Medicine, Durham, NC, USA
- Pratt School of Engineering, Duke University, Durham, NC, USA
| | - Anita Kundu
- iMIND Study Group, Duke University School of Medicine, Durham, NC, USA
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
| | - Alice Haystead
- iMIND Study Group, Duke University School of Medicine, Durham, NC, USA
- Pratt School of Engineering, Duke University, Durham, NC, USA
| | - Lauren Winslow
- iMIND Study Group, Duke University School of Medicine, Durham, NC, USA
- Pratt School of Engineering, Duke University, Durham, NC, USA
| | - Raj Kundu
- iMIND Study Group, Duke University School of Medicine, Durham, NC, USA
- Pratt School of Engineering, Duke University, Durham, NC, USA
| | - C. Ellis Wisely
- iMIND Study Group, Duke University School of Medicine, Durham, NC, USA
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
| | - Cason B. Robbins
- iMIND Study Group, Duke University School of Medicine, Durham, NC, USA
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
| | - Ricardo Henao
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA
| | - Dilraj S. Grewal
- iMIND Study Group, Duke University School of Medicine, Durham, NC, USA
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
| | - Sharon Fekrat
- iMIND Study Group, Duke University School of Medicine, Durham, NC, USA
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
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21
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Tan Y, Sun X. Ocular images-based artificial intelligence on systemic diseases. Biomed Eng Online 2023; 22:49. [PMID: 37208715 DOI: 10.1186/s12938-023-01110-1] [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: 02/14/2023] [Accepted: 05/02/2023] [Indexed: 05/21/2023] Open
Abstract
PURPOSE To provide a summary of the research advances on ocular images-based artificial intelligence on systemic diseases. METHODS Narrative literature review. RESULTS Ocular images-based artificial intelligence has been used in a variety of systemic diseases, including endocrine, cardiovascular, neurological, renal, autoimmune, and hematological diseases, and many others. However, the studies are still at an early stage. The majority of studies have used AI only for diseases diagnosis, and the specific mechanisms linking systemic diseases to ocular images are still unclear. In addition, there are many limitations to the research, such as the number of images, the interpretability of artificial intelligence, rare diseases, and ethical and legal issues. CONCLUSION While ocular images-based artificial intelligence is widely used, the relationship between the eye and the whole body should be more clearly elucidated.
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Affiliation(s)
- Yuhe Tan
- Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Xufang Sun
- Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China.
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22
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Kim BJ, Grossman M, Aleman TS, Song D, Cousins KAQ, McMillan CT, Saludades A, Yu Y, Lee EB, Wolk D, Van Deerlin VM, Shaw LM, Ying GS, Irwin DJ. Retinal photoreceptor layer thickness has disease specificity and distinguishes predicted FTLD-Tau from biomarker-determined Alzheimer's disease. Neurobiol Aging 2023; 125:74-82. [PMID: 36857870 PMCID: PMC10038934 DOI: 10.1016/j.neurobiolaging.2023.01.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 01/23/2023] [Accepted: 01/25/2023] [Indexed: 02/04/2023]
Abstract
While Alzheimer's disease (AD) is associated with inner retina thinning (retinal nerve fiber layer and ganglion cell layer), we have observed photoreceptor outer nuclear layer (ONL) thinning in patients with frontotemporal lobar degeneration tauopathy (FTLD-Tau) compared to normal controls. We hypothesized that ONL thinning may distinguish FTLD-Tau from patients with biomarker evidence of AD neuropathologic change (ADNC) and will correlate with FTLD-Tau disease severity. Predicted FTLD-Tau (pFTLD-Tau; n = 21; 33 eyes) and predicted ADNC (pADNC; n = 24; 46 eyes) patients were consecutively enrolled, underwent optical coherence tomography macula imaging, and disease was categorized (pFTLD-Tau vs. pADNC) with cerebrospinal fluid biomarkers, genetic testing, and autopsy data when available. Adjusting for age, sex, and race, pFTLD-Tau patients had a thinner ONL compared to pADNC, while retinal nerve fiber layer and ganglion cell layer were not significantly different. Reduced ONL thickness correlated with worse performance on Folstein Mini-Mental State Examination and clinical dementia rating plus frontotemporal dementia sum of boxes for pFTLD-Tau but not pADNC. Photoreceptor ONL thickness may serve as an important noninvasive diagnostic marker that distinguishes FTLD-Tau from AD neuropathologic change.
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Affiliation(s)
- Benjamin J Kim
- Department of Ophthalmology, Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Murray Grossman
- Department of Neurology, Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Tomas S Aleman
- Department of Ophthalmology, Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Delu Song
- Department of Ophthalmology, Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Katheryn A Q Cousins
- Department of Neurology, Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Corey T McMillan
- Department of Neurology, Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Adrienne Saludades
- Department of Ophthalmology, Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yinxi Yu
- Department of Ophthalmology, Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, Translational Neuropathology Research Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Disease Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David Wolk
- Department of Neurology, Penn Alzheimer's Disease Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Vivianna M Van Deerlin
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gui-Shuang Ying
- Department of Ophthalmology, Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David J Irwin
- Department of Neurology, Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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23
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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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Sathianvichitr K, Lamoureux O, Nakada S, Tang Z, Schmetterer L, Chen C, Cheung CY, Najjar RP, Milea D. Through the eyes into the brain, using artificial intelligence. ANNALS OF THE ACADEMY OF MEDICINE, SINGAPORE 2023. [DOI: 10.47102/annals-acadmedsg.2022369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
Introduction: Detection of neurological conditions is of high importance in the current context of increasingly ageing populations. Imaging of the retina and the optic nerve head represents a unique opportunity to detect brain diseases, but requires specific human expertise. We review the current outcomes of artificial intelligence (AI) methods applied to retinal imaging for the detection of neurological and neuro-ophthalmic conditions.
Method: Current and emerging concepts related to the detection of neurological conditions, using AI-based investigations of the retina in patients with brain disease were examined and summarised.
Results: Papilloedema due to intracranial hypertension can be accurately identified with deep learning on standard retinal imaging at a human expert level. Emerging studies suggest that patients with Alzheimer’s disease can be discriminated from cognitively normal individuals, using AI applied to retinal images.
Conclusion: Recent AI-based systems dedicated to scalable retinal imaging have opened new perspectives for the detection of brain conditions directly or indirectly affecting retinal structures. However, further validation and implementation studies are required to better understand their potential value in clinical practice.
Keywords: Alzheimer’s disease, deep learning, dementia, optic neuropathy, papilloedema
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Affiliation(s)
| | - Oriana Lamoureux
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | | | - Zhiqun Tang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | | | - Christopher Chen
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Carol Y Cheung
- The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Raymond P Najjar
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Dan Milea
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
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25
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Hui HYH, Ran AR, Dai JJ, Cheung CY. Deep Reinforcement Learning-Based Retinal Imaging in Alzheimer's Disease: Potential and Perspectives. J Alzheimers Dis 2023; 94:39-50. [PMID: 37212112 DOI: 10.3233/jad-230055] [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] [Indexed: 05/23/2023]
Abstract
Alzheimer's disease (AD) remains a global health challenge in the 21st century due to its increasing prevalence as the major cause of dementia. State-of-the-art artificial intelligence (AI)-based tests could potentially improve population-based strategies to detect and manage AD. Current retinal imaging demonstrates immense potential as a non-invasive screening measure for AD, by studying qualitative and quantitative changes in the neuronal and vascular structures of the retina that are often associated with degenerative changes in the brain. On the other hand, the tremendous success of AI, especially deep learning, in recent years has encouraged its incorporation with retinal imaging for predicting systemic diseases. Further development in deep reinforcement learning (DRL), defined as a subfield of machine learning that combines deep learning and reinforcement learning, also prompts the question of how it can work hand in hand with retinal imaging as a viable tool for automated prediction of AD. This review aims to discuss potential applications of DRL in using retinal imaging to study AD, and their synergistic application to unlock other possibilities, such as AD detection and prediction of AD progression. Challenges and future directions, such as the use of inverse DRL in defining reward function, lack of standardization in retinal imaging, and data availability, will also be addressed to bridge gaps for its transition into clinical use.
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Affiliation(s)
- Herbert Y H Hui
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - An Ran Ran
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Jia Jia Dai
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
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26
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Application of Deep Learning to Retinal-Image-Based Oculomics for Evaluation of Systemic Health: A Review. J Clin Med 2022; 12:jcm12010152. [PMID: 36614953 PMCID: PMC9821402 DOI: 10.3390/jcm12010152] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/17/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022] Open
Abstract
The retina is a window to the human body. Oculomics is the study of the correlations between ophthalmic biomarkers and systemic health or disease states. Deep learning (DL) is currently the cutting-edge machine learning technique for medical image analysis, and in recent years, DL techniques have been applied to analyze retinal images in oculomics studies. In this review, we summarized oculomics studies that used DL models to analyze retinal images-most of the published studies to date involved color fundus photographs, while others focused on optical coherence tomography images. These studies showed that some systemic variables, such as age, sex and cardiovascular disease events, could be consistently robustly predicted, while other variables, such as thyroid function and blood cell count, could not be. DL-based oculomics has demonstrated fascinating, "super-human" predictive capabilities in certain contexts, but it remains to be seen how these models will be incorporated into clinical care and whether management decisions influenced by these models will lead to improved clinical outcomes.
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27
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Xia X, Qin Q, Peng Y, Wang M, Yin Y, Tang Y. Retinal Examinations Provides Early Warning of Alzheimer's Disease. J Alzheimers Dis 2022; 90:1341-1357. [PMID: 36245377 DOI: 10.3233/jad-220596] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Patients with Alzheimer's disease have difficulty maintaining independent living abilities as the disease progresses, causing an increased burden of care on family caregivers and the healthcare system and related financial strain. This patient group is expected to continue to expand as life expectancy climbs. Current diagnostics for Alzheimer's disease are complex, unaffordable, and invasive without regard to diagnosis quality at early stages, which urgently calls for more technical improvements for diagnosis specificity. Optical coherence tomography or tomographic angiography has been shown to identify retinal thickness loss and lower vascular density present earlier than symptom onset in these patients. The retina is an extension of the central nervous system and shares anatomic and functional similarities with the brain. Ophthalmological examinations can be an efficient tool to offer a window into cerebral pathology with the merit of easy operation. In this review, we summarized the latest observations on retinal pathology in Alzheimer's disease and discussed the feasibility of retinal imaging in diagnostic prediction, as well as limitations in current retinal examinations for Alzheimer's disease diagnosis.
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Affiliation(s)
- Xinyi Xia
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Qi Qin
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Yankun Peng
- Department of Ophthalmology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Meng Wang
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yunsi Yin
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yi Tang
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
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28
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Marshall CR, Uchegbu I. Artificial intelligence for detection of Alzheimer's disease: demonstration of real-world value is required to bridge the translational gap. Lancet Digit Health 2022; 4:e768-e769. [PMID: 36192348 DOI: 10.1016/s2589-7500(22)00190-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 09/23/2022] [Indexed: 06/16/2023]
Affiliation(s)
- Charles R Marshall
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK; Neurology Department, Barts Health NHS Trust, London, UK.
| | - Ijeoma Uchegbu
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK; Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Queen Mary University of London, London, UK
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29
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Cheung CY, Ran AR, Wang S, Chan VTT, Sham K, Hilal S, Venketasubramanian N, Cheng CY, Sabanayagam C, Tham YC, Schmetterer L, McKay GJ, Williams MA, Wong A, Au LWC, Lu Z, Yam JC, Tham CC, Chen JJ, Dumitrascu OM, Heng PA, Kwok TCY, Mok VCT, Milea D, Chen CLH, Wong TY. A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study. Lancet Digit Health 2022; 4:e806-e815. [PMID: 36192349 DOI: 10.1016/s2589-7500(22)00169-8] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 08/12/2022] [Accepted: 08/19/2022] [Indexed: 10/14/2022]
Abstract
BACKGROUND There is no simple model to screen for Alzheimer's disease, partly because the diagnosis of Alzheimer's disease itself is complex-typically involving expensive and sometimes invasive tests not commonly available outside highly specialised clinical settings. We aimed to develop a deep learning algorithm that could use retinal photographs alone, which is the most common method of non-invasive imaging the retina to detect Alzheimer's disease-dementia. METHODS In this retrospective, multicentre case-control study, we trained, validated, and tested a deep learning algorithm to detect Alzheimer's disease-dementia from retinal photographs using retrospectively collected data from 11 studies that recruited patients with Alzheimer's disease-dementia and people without disease from different countries. Our main aim was to develop a bilateral model to detect Alzheimer's disease-dementia from retinal photographs alone. We designed and internally validated the bilateral deep learning model using retinal photographs from six studies. We used the EfficientNet-b2 network as the backbone of the model to extract features from the images. Integrated features from four retinal photographs (optic nerve head-centred and macula-centred fields from both eyes) for each individual were used to develop supervised deep learning models and equip the network with unsupervised domain adaptation technique, to address dataset discrepancy between the different studies. We tested the trained model using five other studies, three of which used PET as a biomarker of significant amyloid β burden (testing the deep learning model between amyloid β positive vs amyloid β negative). FINDINGS 12 949 retinal photographs from 648 patients with Alzheimer's disease and 3240 people without the disease were used to train, validate, and test the deep learning model. In the internal validation dataset, the deep learning model had 83·6% (SD 2·5) accuracy, 93·2% (SD 2·2) sensitivity, 82·0% (SD 3·1) specificity, and an area under the receiver operating characteristic curve (AUROC) of 0·93 (0·01) for detecting Alzheimer's disease-dementia. In the testing datasets, the bilateral deep learning model had accuracies ranging from 79·6% (SD 15·5) to 92·1% (11·4) and AUROCs ranging from 0·73 (SD 0·24) to 0·91 (0·10). In the datasets with data on PET, the model was able to differentiate between participants who were amyloid β positive and those who were amyloid β negative: accuracies ranged from 80·6 (SD 13·4%) to 89·3 (13·7%) and AUROC ranged from 0·68 (SD 0·24) to 0·86 (0·16). In subgroup analyses, the discriminative performance of the model was improved in patients with eye disease (accuracy 89·6% [SD 12·5%]) versus those without eye disease (71·7% [11·6%]) and patients with diabetes (81·9% [SD 20·3%]) versus those without the disease (72·4% [11·7%]). INTERPRETATION A retinal photograph-based deep learning algorithm can detect Alzheimer's disease with good accuracy, showing its potential for screening Alzheimer's disease in a community setting. FUNDING BrightFocus Foundation.
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Affiliation(s)
- Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, the Chinese University of Hong Kong, Hong Kong Special Administrative Region, China.
| | - An Ran Ran
- Department of Ophthalmology and Visual Sciences, the Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Shujun Wang
- Department of Computer Science and Engineering, the Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Victor T T Chan
- Department of Ophthalmology and Visual Sciences, the Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Department of Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong Special Administrative Region, China
| | - Kaiser Sham
- Department of Ophthalmology and Visual Sciences, the Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Saima Hilal
- Memory Aging &Cognition Centre, National University Health System, Singapore; Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | | | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-National University of Singapore Medical School, Singapore
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-National University of Singapore Medical School, Singapore
| | - Yih Chung Tham
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-National University of Singapore Medical School, Singapore
| | - Leopold Schmetterer
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Singapore Eye Research Institute, Advanced Ocular Engineering and School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore
| | - Gareth J McKay
- Centre for Public Health, Royal Victoria Hospital, Queen's University Belfast, Belfast, UK
| | | | - Adrian Wong
- Gerald Choa Neuroscience Institute, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Division of Neurology, Department of Medicine and Therapeutics, the Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Lisa W C Au
- Gerald Choa Neuroscience Institute, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Division of Neurology, Department of Medicine and Therapeutics, the Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Zhihui Lu
- Jockey Club Centre for Osteoporosis Care and Control, the Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Department of Medicine and Therapeutics, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Jason C Yam
- Department of Ophthalmology and Visual Sciences, the Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Clement C Tham
- Department of Ophthalmology and Visual Sciences, the Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - John J Chen
- Department of Ophthalmology and Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Oana M Dumitrascu
- Department of Neurology and Department of Ophthalmology, Division of Cerebrovascular Diseases, Mayo Clinic College of Medicine and Science, Scottsdale, AZ, USA
| | - Pheng-Ann Heng
- Department of Computer Science and Engineering, the Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Timothy C Y Kwok
- Jockey Club Centre for Osteoporosis Care and Control, the Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Department of Medicine and Therapeutics, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Vincent C T Mok
- Gerald Choa Neuroscience Institute, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Division of Neurology, Department of Medicine and Therapeutics, the Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Dan Milea
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-National University of Singapore Medical School, Singapore
| | - Christopher Li-Hsian Chen
- Memory Aging &Cognition Centre, National University Health System, Singapore; Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-National University of Singapore Medical School, Singapore; Tsinghua Medicine, Tsinghua University, Beijing, China
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Jeon M, Park H, Kim HJ, Morley M, Cho H. k-SALSA: k-anonymous synthetic averaging of retinal images via local style alignment. COMPUTER VISION - ECCV ... : ... EUROPEAN CONFERENCE ON COMPUTER VISION : PROCEEDINGS. EUROPEAN CONFERENCE ON COMPUTER VISION 2022; 13681:661-678. [PMID: 37525827 PMCID: PMC10388376 DOI: 10.1007/978-3-031-19803-8_39] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
The application of modern machine learning to retinal image analyses offers valuable insights into a broad range of human health conditions beyond ophthalmic diseases. Additionally, data sharing is key to fully realizing the potential of machine learning models by providing a rich and diverse collection of training data. However, the personallyidentifying nature of retinal images, encompassing the unique vascular structure of each individual, often prevents this data from being shared openly. While prior works have explored image de-identification strategies based on synthetic averaging of images in other domains (e.g. facial images), existing techniques face difficulty in preserving both privacy and clinical utility in retinal images, as we demonstrate in our work. We therefore introduce k-SALSA, a generative adversarial network (GAN)-based framework for synthesizing retinal fundus images that summarize a given private dataset while satisfying the privacy notion of k-anonymity. k-SALSA brings together state-of-the-art techniques for training and inverting GANs to achieve practical performance on retinal images. Furthermore, k-SALSA leverages a new technique, called local style alignment, to generate a synthetic average that maximizes the retention of fine-grain visual patterns in the source images, thus improving the clinical utility of the generated images. On two benchmark datasets of diabetic retinopathy (EyePACS and APTOS), we demonstrate our improvement upon existing methods with respect to image fidelity, classification performance, and mitigation of membership inference attacks. Our work represents a step toward broader sharing of retinal images for scientific collaboration. Code is available at https://github.com/hcholab/k-salsa.
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Affiliation(s)
- Minkyu Jeon
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Korea University, Seoul, Republic of Korea
| | | | | | - Michael Morley
- Harvard Medical School, Boston, MA, USA
- Ophthalmic Consultants of Boston, Boston, MA, USA
| | - Hyunghoon Cho
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Patil AD, Biousse V, Newman NJ. Artificial intelligence in ophthalmology: an insight into neurodegenerative disease. Curr Opin Ophthalmol 2022; 33:432-439. [PMID: 35819902 DOI: 10.1097/icu.0000000000000877] [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: 11/26/2022]
Abstract
PURPOSE OF REVIEW The aging world population accounts for the increasing prevalence of neurodegenerative diseases such as Alzheimer's and Parkinson's which carry a significant health and economic burden. There is therefore a need for sensitive and specific noninvasive biomarkers for early diagnosis and monitoring. Advances in retinal and optic nerve multimodal imaging as well as the development of artificial intelligence deep learning systems (AI-DLS) have heralded a number of promising advances of which ophthalmologists are at the forefront. RECENT FINDINGS The association among retinal vascular, nerve fiber layer, and macular findings in neurodegenerative disease is well established. In order to optimize the use of these ophthalmic parameters as biomarkers, validated AI-DLS are required to ensure clinical efficacy and reliability. Varied image acquisition methods and protocols as well as variability in neurogenerative disease diagnosis compromise the robustness of ground truths that are paramount to developing high-quality training datasets. SUMMARY In order to produce effective AI-DLS for the diagnosis and monitoring of neurodegenerative disease, multicenter international collaboration is required to prospectively produce large inclusive datasets, acquired through standardized methods and protocols. With a uniform approach, the efficacy of resultant clinical applications will be maximized.
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Affiliation(s)
| | | | - Nancy J Newman
- Department of Ophthalmology
- Department of Neurology
- Department of Neurological Surgery, Emory University School of Medicine, Atlanta, Georgia, USA
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Bhambra N, Antaki F, Malt FE, Xu A, Duval R. Deep learning for ultra-widefield imaging: a scoping review. Graefes Arch Clin Exp Ophthalmol 2022; 260:3737-3778. [PMID: 35857087 DOI: 10.1007/s00417-022-05741-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 05/16/2022] [Accepted: 06/22/2022] [Indexed: 11/04/2022] Open
Abstract
PURPOSE This article is a scoping review of published and peer-reviewed articles using deep-learning (DL) applied to ultra-widefield (UWF) imaging. This study provides an overview of the published uses of DL and UWF imaging for the detection of ophthalmic and systemic diseases, generative image synthesis, quality assessment of images, and segmentation and localization of ophthalmic image features. METHODS A literature search was performed up to August 31st, 2021 using PubMed, Embase, Cochrane Library, and Google Scholar. The inclusion criteria were as follows: (1) deep learning, (2) ultra-widefield imaging. The exclusion criteria were as follows: (1) articles published in any language other than English, (2) articles not peer-reviewed (usually preprints), (3) no full-text availability, (4) articles using machine learning algorithms other than deep learning. No study design was excluded from consideration. RESULTS A total of 36 studies were included. Twenty-three studies discussed ophthalmic disease detection and classification, 5 discussed segmentation and localization of ultra-widefield images (UWFIs), 3 discussed generative image synthesis, 3 discussed ophthalmic image quality assessment, and 2 discussed detecting systemic diseases via UWF imaging. CONCLUSION The application of DL to UWF imaging has demonstrated significant effectiveness in the diagnosis and detection of ophthalmic diseases including diabetic retinopathy, retinal detachment, and glaucoma. DL has also been applied in the generation of synthetic ophthalmic images. This scoping review highlights and discusses the current uses of DL with UWF imaging, and the future of DL applications in this field.
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Affiliation(s)
- Nishaant Bhambra
- Faculty of Medicine, McGill University, Montréal, Québec, Canada
| | - Fares Antaki
- Department of Ophthalmology, Université de Montréal, Montréal, Québec, Canada.,Centre Universitaire d'Ophtalmologie (CUO), Hôpital Maisonneuve-Rosemont, CIUSSS de L'Est-de-L'Île-de-Montréal, 5415 Assumption Blvd, Montréal, Québec, H1T 2M4, Canada
| | - Farida El Malt
- Faculty of Medicine, McGill University, Montréal, Québec, Canada
| | - AnQi Xu
- Faculty of Medicine, Université de Montréal, Montréal, Québec, Canada
| | - Renaud Duval
- Department of Ophthalmology, Université de Montréal, Montréal, Québec, Canada. .,Centre Universitaire d'Ophtalmologie (CUO), Hôpital Maisonneuve-Rosemont, CIUSSS de L'Est-de-L'Île-de-Montréal, 5415 Assumption Blvd, Montréal, Québec, H1T 2M4, Canada.
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Khan NC, Perera C, Dow ER, Chen KM, Mahajan VB, Mruthyunjaya P, Do DV, Leng T, Myung D. Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models. Diagnostics (Basel) 2022; 12:diagnostics12071714. [PMID: 35885619 PMCID: PMC9322827 DOI: 10.3390/diagnostics12071714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 06/23/2022] [Accepted: 06/24/2022] [Indexed: 12/02/2022] Open
Abstract
While color fundus photos are used in routine clinical practice to diagnose ophthalmic conditions, evidence suggests that ocular imaging contains valuable information regarding the systemic health features of patients. These features can be identified through computer vision techniques including deep learning (DL) artificial intelligence (AI) models. We aim to construct a DL model that can predict systemic features from fundus images and to determine the optimal method of model construction for this task. Data were collected from a cohort of patients undergoing diabetic retinopathy screening between March 2020 and March 2021. Two models were created for each of 12 systemic health features based on the DenseNet201 architecture: one utilizing transfer learning with images from ImageNet and another from 35,126 fundus images. Here, 1277 fundus images were used to train the AI models. Area under the receiver operating characteristics curve (AUROC) scores were used to compare the model performance. Models utilizing the ImageNet transfer learning data were superior to those using retinal images for transfer learning (mean AUROC 0.78 vs. 0.65, p-value < 0.001). Models using ImageNet pretraining were able to predict systemic features including ethnicity (AUROC 0.93), age > 70 (AUROC 0.90), gender (AUROC 0.85), ACE inhibitor (AUROC 0.82), and ARB medication use (AUROC 0.78). We conclude that fundus images contain valuable information about the systemic characteristics of a patient. To optimize DL model performance, we recommend that even domain specific models consider using transfer learning from more generalized image sets to improve accuracy.
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Affiliation(s)
- Nergis C. Khan
- Byers Eye Institute at Stanford, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA 94305, USA; (N.C.K.); (C.P.); (E.R.D.); (K.M.C.); (V.B.M.); (P.M.); (D.V.D.); (T.L.)
| | - Chandrashan Perera
- Byers Eye Institute at Stanford, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA 94305, USA; (N.C.K.); (C.P.); (E.R.D.); (K.M.C.); (V.B.M.); (P.M.); (D.V.D.); (T.L.)
- Department of Ophthalmology, Fremantle Hospital, Perth, WA 6004, Australia
| | - Eliot R. Dow
- Byers Eye Institute at Stanford, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA 94305, USA; (N.C.K.); (C.P.); (E.R.D.); (K.M.C.); (V.B.M.); (P.M.); (D.V.D.); (T.L.)
| | - Karen M. Chen
- Byers Eye Institute at Stanford, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA 94305, USA; (N.C.K.); (C.P.); (E.R.D.); (K.M.C.); (V.B.M.); (P.M.); (D.V.D.); (T.L.)
| | - Vinit B. Mahajan
- Byers Eye Institute at Stanford, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA 94305, USA; (N.C.K.); (C.P.); (E.R.D.); (K.M.C.); (V.B.M.); (P.M.); (D.V.D.); (T.L.)
| | - Prithvi Mruthyunjaya
- Byers Eye Institute at Stanford, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA 94305, USA; (N.C.K.); (C.P.); (E.R.D.); (K.M.C.); (V.B.M.); (P.M.); (D.V.D.); (T.L.)
| | - Diana V. Do
- Byers Eye Institute at Stanford, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA 94305, USA; (N.C.K.); (C.P.); (E.R.D.); (K.M.C.); (V.B.M.); (P.M.); (D.V.D.); (T.L.)
| | - Theodore Leng
- Byers Eye Institute at Stanford, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA 94305, USA; (N.C.K.); (C.P.); (E.R.D.); (K.M.C.); (V.B.M.); (P.M.); (D.V.D.); (T.L.)
| | - David Myung
- Byers Eye Institute at Stanford, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA 94305, USA; (N.C.K.); (C.P.); (E.R.D.); (K.M.C.); (V.B.M.); (P.M.); (D.V.D.); (T.L.)
- VA Palo Alto Health Care System, Palo Alto, CA 94304, USA
- Correspondence: ; Tel.: +1-650-724-3948
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Lim JS, Hong M, Lam WST, Zhang Z, Teo ZL, Liu Y, Ng WY, Foo LL, Ting DSW. Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology. Curr Opin Ophthalmol 2022; 33:174-187. [PMID: 35266894 DOI: 10.1097/icu.0000000000000846] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW The application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent years in diagnosis, prognosis, and aiding clinical decision-making. The use of digital data has also heralded the need for privacy-preserving technology to protect patient confidentiality and to guard against threats such as adversarial attacks. Hence, this review aims to outline novel AI-based systems for ophthalmology use, privacy-preserving measures, potential challenges, and future directions of each. RECENT FINDINGS Several key AI algorithms used to improve disease detection and outcomes include: Data-driven, imagedriven, natural language processing (NLP)-driven, genomics-driven, and multimodality algorithms. However, deep learning systems are susceptible to adversarial attacks, and use of data for training models is associated with privacy concerns. Several data protection methods address these concerns in the form of blockchain technology, federated learning, and generative adversarial networks. SUMMARY AI-applications have vast potential to meet many eyecare needs, consequently reducing burden on scarce healthcare resources. A pertinent challenge would be to maintain data privacy and confidentiality while supporting AI endeavors, where data protection methods would need to rapidly evolve with AI technology needs. Ultimately, for AI to succeed in medicine and ophthalmology, a balance would need to be found between innovation and privacy.
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Affiliation(s)
- Jane S Lim
- Singapore National Eye Centre, Singapore Eye Research Institute
| | | | - Walter S T Lam
- Yong Loo Lin School of Medicine, National University of Singapore
| | - Zheting Zhang
- Lee Kong Chian School of Medicine, Nanyang Technological University
| | - Zhen Ling Teo
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Yong Liu
- National University of Singapore, DukeNUS Medical School, Singapore
| | - Wei Yan Ng
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Li Lian Foo
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Daniel S W Ting
- Singapore National Eye Centre, Singapore Eye Research Institute
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Wagner SK, Hughes F, Cortina-Borja M, Pontikos N, Struyven R, Liu X, Montgomery H, Alexander DC, Topol E, Petersen SE, Balaskas K, Hindley J, Petzold A, Rahi JS, Denniston AK, Keane PA. AlzEye: longitudinal record-level linkage of ophthalmic imaging and hospital admissions of 353 157 patients in London, UK. BMJ Open 2022; 12:e058552. [PMID: 35296488 PMCID: PMC8928293 DOI: 10.1136/bmjopen-2021-058552] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
PURPOSE Retinal signatures of systemic disease ('oculomics') are increasingly being revealed through a combination of high-resolution ophthalmic imaging and sophisticated modelling strategies. Progress is currently limited not mainly by technical issues, but by the lack of large labelled datasets, a sine qua non for deep learning. Such data are derived from prospective epidemiological studies, in which retinal imaging is typically unimodal, cross-sectional, of modest number and relates to cohorts, which are not enriched with subpopulations of interest, such as those with systemic disease. We thus linked longitudinal multimodal retinal imaging from routinely collected National Health Service (NHS) data with systemic disease data from hospital admissions using a privacy-by-design third-party linkage approach. PARTICIPANTS Between 1 January 2008 and 1 April 2018, 353 157 participants aged 40 years or older, who attended Moorfields Eye Hospital NHS Foundation Trust, a tertiary ophthalmic institution incorporating a principal central site, four district hubs and five satellite clinics in and around London, UK serving a catchment population of approximately six million people. FINDINGS TO DATE Among the 353 157 individuals, 186 651 had a total of 1 337 711 Hospital Episode Statistics admitted patient care episodes. Systemic diagnoses recorded at these episodes include 12 022 patients with myocardial infarction, 11 735 with all-cause stroke and 13 363 with all-cause dementia. A total of 6 261 931 retinal images of seven different modalities and across three manufacturers were acquired from 1 54 830 patients. The majority of retinal images were retinal photographs (n=1 874 175) followed by optical coherence tomography (n=1 567 358). FUTURE PLANS AlzEye combines the world's largest single institution retinal imaging database with nationally collected systemic data to create an exceptional large-scale, enriched cohort that reflects the diversity of the population served. First analyses will address cardiovascular diseases and dementia, with a view to identifying hidden retinal signatures that may lead to earlier detection and risk management of these life-threatening conditions.
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Affiliation(s)
- Siegfried Karl Wagner
- Institute of Ophthalmology, University College London, London, UK
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Fintan Hughes
- Department of Anaesthesiology, Duke University Hospital, Durham, North Carolina, USA
| | | | - Nikolas Pontikos
- Institute of Ophthalmology, University College London, London, UK
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Robbert Struyven
- Institute of Ophthalmology, University College London, London, UK
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Xiaoxuan Liu
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- Centre for Regulatory Science and Innovation, Birmingham Health Partners, Birmingham, UK
| | - Hugh Montgomery
- Centre for Human Health and Performance, University College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Eric Topol
- Scripps Research Institute, La Jolla, California, USA
| | - Steffen Erhard Petersen
- William Harvey Research Institute, Queen Mary University of London, London, UK
- Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Konstantinos Balaskas
- Institute of Ophthalmology, University College London, London, UK
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
- Medical Retina Service, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Jack Hindley
- Department of Information Governance, University College London, London, UK
| | - Axel Petzold
- Institute of Ophthalmology, University College London, London, UK
- Institute of Neurology, University College London, London, UK
- Department of Neurophthalmology, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Jugnoo S Rahi
- Institute of Ophthalmology, University College London, London, UK
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
- Great Ormond Street Institute of Child Health, University College London, London, UK
- Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
- Ulverscroft Vision Research Group, University College London, London, UK
| | - Alastair K Denniston
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- Centre for Regulatory Science and Innovation, Birmingham Health Partners, Birmingham, UK
| | - Pearse A Keane
- Institute of Ophthalmology, University College London, London, UK
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
- Medical Retina Service, Moorfields Eye Hospital NHS Foundation Trust, London, UK
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Ma JP, Robbins CB, Lee JM, Soundararajan S, Stinnett SS, Agrawal R, Plassman BL, Lad EM, Whitson H, Grewal DS, Fekrat S. Longitudinal analysis of the retina and choroid in cognitively normal individuals at higher genetic risk for Alzheimer disease. Ophthalmol Retina 2022; 6:607-619. [PMID: 35283324 DOI: 10.1016/j.oret.2022.03.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 02/18/2022] [Accepted: 03/03/2022] [Indexed: 11/28/2022]
Abstract
PURPOSE To assess baseline differences and longitudinal rate of change in retinal and choroidal imaging parameters between APOE ε4 carriers and non-carriers with normal cognition. DESIGN Prospective study. SUBJECTS 413 eyes of 218 individuals with normal cognition aged ≥55 years with known APOE status (98 ε4 carriers, 120 non-carriers). Exclusion criteria included diabetes mellitus, uncontrolled hypertension, glaucoma, and vitreoretinal or neurodegenerative disease. METHODS Optical coherence tomography (OCT) and OCT angiography (OCTA) was performed at baseline and at 2 years [Zeiss Cirrus HD-OCT 5000 with AngioPlex (Zeiss Meditec, Dublin, CA)]. Groups were compared using sex- and age-adjusted generalized estimating equations. MAIN OUTCOME MEASURES OCT: retinal nerve fiber layer thickness, macular ganglion cell-inner plexiform layer thickness, central subfield thickness (CST), choroidal vascularity index. OCTA: foveal avascular zone area, perfusion density (PD), vessel density, peripapillary capillary perfusion density and capillary flux index (CFI). Rate of change per year was calculated. RESULTS At baseline, ε4 carriers demonstrated decreased CST (p=0.018), PD in the 6mm Early Treatment Diabetic Retinopathy Study (ETDRS) circle (p=0.049), and temporal CFI (p=0.047). Seventy-one ε4 carriers and 78 non-carriers returned at 2 years; at follow-up, the 6mm ETDRS circle (p=0.05) and outer ring (p=0.049) showed decreased PD in ε4 carriers, with no differences in rates of change between groups (all p>0.05). CONCLUSIONS There were measured differences in CST, PD, and peripapillary CFI between APOE ε4 carriers and non-carriers with normal cognition. Larger and longer-term studies may further elucidate the potential prognostic value of these findings.
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Affiliation(s)
- Justin P Ma
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA; Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
| | - Cason B Robbins
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA; Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
| | - Jia Min Lee
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore
| | - Srinath Soundararajan
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA; Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
| | - Sandra S Stinnett
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA; Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
| | - Rupesh Agrawal
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Singapore Eye Research Institute, Singapore; Duke NUS Medical School, Singapore
| | - Brenda L Plassman
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA; Departments of Psychiatry and Neurology, Duke University School of Medicine, Durham, NC, USA
| | - Eleonora M Lad
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
| | - Heather Whitson
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
| | - Dilraj S Grewal
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA; Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
| | - Sharon Fekrat
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA; Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA.
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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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Cai S, Han IC, Scott AW. Artificial intelligence for improving sickle cell retinopathy diagnosis and management. Eye (Lond) 2021; 35:2675-2684. [PMID: 33958737 PMCID: PMC8452674 DOI: 10.1038/s41433-021-01556-4] [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: 01/01/2021] [Revised: 03/17/2021] [Accepted: 04/13/2021] [Indexed: 02/04/2023] Open
Abstract
Sickle cell retinopathy is often initially asymptomatic even in proliferative stages, but can progress to cause vision loss due to vitreous haemorrhages or tractional retinal detachments. Challenges with access and adherence to screening dilated fundus examinations, particularly in medically underserved areas where the burden of sickle cell disease is highest, highlight the need for novel approaches to screening for patients with vision-threatening sickle cell retinopathy. This article reviews the existing literature on and suggests future research directions for coupling artificial intelligence with multimodal retinal imaging to expand access to automated, accurate, imaging-based screening for sickle cell retinopathy. Given the variability in retinal specialist practice patterns with regards to monitoring and treatment of sickle cell retinopathy, we also discuss recent progress toward development of machine learning models that can quantitatively track disease progression over time. These artificial intelligence-based applications have great potential for informing evidence-based and resource-efficient clinical diagnosis and management of sickle cell retinopathy.
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Affiliation(s)
- Sophie Cai
- Retina Division, Duke Eye Center, Durham, NC, USA
| | - Ian C Han
- Institute for Vision Research, Department of Ophthalmology and Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Adrienne W Scott
- Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine and Hospital, Baltimore, MD, USA.
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Renner H, Schöler HR, Bruder JM. Combining Automated Organoid Workflows With Artificial Intelligence-Based Analyses: Opportunities to Build a New Generation of Interdisciplinary High-Throughput Screens for Parkinson's Disease and Beyond. Mov Disord 2021; 36:2745-2762. [PMID: 34498298 DOI: 10.1002/mds.28775] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 08/05/2021] [Accepted: 08/09/2021] [Indexed: 12/14/2022] Open
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease and primarily characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta of the midbrain. Despite decades of research and the development of various disease model systems, there is no curative treatment. This could be due to current model systems, including cell culture and animal models, not adequately recapitulating human PD etiology. More complex human disease models, including human midbrain organoids, are maturing technologies that increasingly enable the strategic incorporation of the missing components needed to model PD in vitro. The resulting organoid-based biological complexity provides new opportunities and challenges in data analysis of rich multimodal data sets. Emerging artificial intelligence (AI) capabilities can take advantage of large, broad data sets and even correlate results across disciplines. Current organoid technologies no longer lack the prerequisites for large-scale high-throughput screening (HTS) and can generate complex yet reproducible data suitable for AI-based data mining. We have recently developed a fully scalable and HTS-compatible workflow for the generation, maintenance, and analysis of three-dimensional (3D) microtissues mimicking key characteristics of the human midbrain (called "automated midbrain organoids," AMOs). AMOs build a reproducible, scalable foundation for creating next-generation 3D models of human neural disease that can fuel mechanism-agnostic phenotypic drug discovery in human in vitro PD models and beyond. Here, we explore the opportunities and challenges resulting from the convergence of organoid HTS and AI-driven data analytics and outline potential future avenues toward the discovery of novel mechanisms and drugs in PD research. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Henrik Renner
- Department of Cell and Developmental Biology, Max Planck Institute for Molecular Biomedicine, Münster, Germany
| | - Hans R Schöler
- Department of Cell and Developmental Biology, Max Planck Institute for Molecular Biomedicine, Münster, Germany
| | - Jan M Bruder
- Department of Cell and Developmental Biology, Max Planck Institute for Molecular Biomedicine, Münster, Germany
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40
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Moons L, De Groef L. Multimodal retinal imaging to detect and understand Alzheimer's and Parkinson's disease. Curr Opin Neurobiol 2021; 72:1-7. [PMID: 34399146 DOI: 10.1016/j.conb.2021.07.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 07/01/2021] [Accepted: 07/14/2021] [Indexed: 12/28/2022]
Abstract
Retinal neurodegeneration and visual dysfunctions have been reported in a majority of Alzheimer's and Parkinson's patients, and, in light of the quest for novel biomarkers for these neurodegenerative proteinopathies, the retina has been receiving increasing attention as an organ for diagnosing, monitoring, and understanding disease. Thinning of retinal layers, abnormalities in vasculature, and protein deposition can be imaged at unprecedented resolution, which offers a unique systems biology view on the cellular and molecular changes underlying these pathologies. It makes the retina not only a promising target for biomarker development, but it also suggests that novel fundamental insights into the pathophysiology of Alzheimer's and Parkinson's disease can be obtained by studying the retina-brain axis.
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Affiliation(s)
- Lieve Moons
- Neural Circuit Development and Regeneration Research Group, Biology Department, University of Leuven, Naamsestraat 61 Box 2464, Leuven, 3000, Belgium; Leuven Brain Institute, Leuven, 3000, Belgium.
| | - Lies De Groef
- Neural Circuit Development and Regeneration Research Group, Biology Department, University of Leuven, Naamsestraat 61 Box 2464, Leuven, 3000, Belgium; Leuven Brain Institute, Leuven, 3000, Belgium
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41
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Chalkias IN, Tegos T, Topouzis F, Tsolaki M. Ocular biomarkers and their role in the early diagnosis of neurocognitive disorders. Eur J Ophthalmol 2021; 31:2808-2817. [PMID: 34000876 DOI: 10.1177/11206721211016311] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Given the fact that different types of dementia can be diagnosed only postmortem or when the disease has progressed enough to cause irreversible damage to certain brain areas, there has been an increasing need for the development of sensitive and reliable methods that can detect early preclinical forms of dementia, before the symptoms have even appeared. Ideally, such a method would have the following characteristics: to be inexpensive, sensitive and specific, Non-invasive, fast and easily accessible. The ophthalmologic examination and especially the study of the retina, has caught the attention of many researchers, as it can provide a lot of information about the CNS and it fulfills many of the aforementioned criteria. Since the introduction of the non-invasive optical coherence tomography (OCT) and the newly developed modality OCT-angiography (OCT-A) that can demonstrate the structure and the microvasculature of the retina and choroid, respectively, there have been promising results regarding the value of the ophthalmologic examination in the early diagnosis of Alzheimer's disease. In this review paper, we summarize and discuss the ocular findings in patients with cognitive impairment disorders and we highlight the importance of the ophthalmologic examination to the diagnosis of these disorders.
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Affiliation(s)
- Ioannis-Nikolaos Chalkias
- 1st Department of Ophthalmology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Makedonia Thraki, Greece
| | - Thomas Tegos
- 1st Department of Neurology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Makedonia Thraki, Greece
| | - Fotis Topouzis
- 1st Department of Ophthalmology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Makedonia Thraki, Greece
| | - Magda Tsolaki
- 1st Department of Neurology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Makedonia Thraki, Greece.,Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Greece
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Ng WY, Cheung CY, Milea D, Ting DSW. Artificial intelligence and machine learning for Alzheimer's disease: let's not forget about the retina. Br J Ophthalmol 2021; 105:593-594. [PMID: 33495160 DOI: 10.1136/bjophthalmol-2020-318407] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Wei Yan Ng
- Cataract and Comprehensive, Singapore National Eye Centre, Singapore
| | - Carol Y Cheung
- Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Dan Milea
- Neuro-ophthalmology Department, Singapore National Eye Centre, Singapore
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