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Martin E, Cook AG, Frost SM, Turner AW, Chen FK, McAllister IL, Nolde JM, Schlaich MP. Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs. Eye (Lond) 2024:10.1038/s41433-024-03085-2. [PMID: 38734746 DOI: 10.1038/s41433-024-03085-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 04/03/2024] [Accepted: 04/11/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND/OBJECTIVES Artificial intelligence can assist with ocular image analysis for screening and diagnosis, but it is not yet capable of autonomous full-spectrum screening. Hypothetically, false-positive results may have unrealized screening potential arising from signals persisting despite training and/or ambiguous signals such as from biomarker overlap or high comorbidity. The study aimed to explore the potential to detect clinically useful incidental ocular biomarkers by screening fundus photographs of hypertensive adults using diabetic deep learning algorithms. SUBJECTS/METHODS Patients referred for treatment-resistant hypertension were imaged at a hospital unit in Perth, Australia, between 2016 and 2022. The same 45° colour fundus photograph selected for each of the 433 participants imaged was processed by three deep learning algorithms. Two expert retinal specialists graded all false-positive results for diabetic retinopathy in non-diabetic participants. RESULTS Of the 29 non-diabetic participants misclassified as positive for diabetic retinopathy, 28 (97%) had clinically useful retinal biomarkers. The models designed to screen for fewer diseases captured more incidental disease. All three algorithms showed a positive correlation between severity of hypertensive retinopathy and misclassified diabetic retinopathy. CONCLUSIONS The results suggest that diabetic deep learning models may be responsive to hypertensive and other clinically useful retinal biomarkers within an at-risk, hypertensive cohort. Observing that models trained for fewer diseases captured more incidental pathology increases confidence in signalling hypotheses aligned with using self-supervised learning to develop autonomous comprehensive screening. Meanwhile, non-referable and false-positive outputs of other deep learning screening models could be explored for immediate clinical use in other populations.
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Affiliation(s)
- Eve Martin
- Commonwealth Scientific and Industrial Research Organisation (CSIRO), Kensington, WA, Australia.
- School of Population and Global Health, The University of Western Australia, Crawley, Australia.
- Dobney Hypertension Centre - Royal Perth Hospital Unit, Medical School, The University of Western Australia, Perth, Australia.
- Australian e-Health Research Centre, Floreat, WA, Australia.
| | - Angus G Cook
- School of Population and Global Health, The University of Western Australia, Crawley, Australia
| | - Shaun M Frost
- Commonwealth Scientific and Industrial Research Organisation (CSIRO), Kensington, WA, Australia
- Australian e-Health Research Centre, Floreat, WA, Australia
| | - Angus W Turner
- Lions Eye Institute, Nedlands, WA, Australia
- Centre for Ophthalmology and Visual Science, The University of Western Australia, Perth, Australia
| | - Fred K Chen
- Lions Eye Institute, Nedlands, WA, Australia
- Centre for Ophthalmology and Visual Science, The University of Western Australia, Perth, Australia
- Centre for Eye Research Australia, The Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia
- Ophthalmology, Department of Surgery, The University of Melbourne, East Melbourne, VIC, Australia
- Ophthalmology Department, Royal Perth Hospital, Perth, Australia
| | - Ian L McAllister
- Lions Eye Institute, Nedlands, WA, Australia
- Centre for Ophthalmology and Visual Science, The University of Western Australia, Perth, Australia
| | - Janis M Nolde
- Dobney Hypertension Centre - Royal Perth Hospital Unit, Medical School, The University of Western Australia, Perth, Australia
- Departments of Cardiology and Nephrology, Royal Perth Hospital, Perth, Australia
| | - Markus P Schlaich
- Dobney Hypertension Centre - Royal Perth Hospital Unit, Medical School, The University of Western Australia, Perth, Australia
- Departments of Cardiology and Nephrology, Royal Perth Hospital, Perth, Australia
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Shoeibi A, Ghassemi N, Khodatars M, Moridian P, Khosravi A, Zare A, Gorriz JM, Chale-Chale AH, Khadem A, Rajendra Acharya U. Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression. Cogn Neurodyn 2023; 17:1501-1523. [PMID: 37974583 PMCID: PMC10640504 DOI: 10.1007/s11571-022-09897-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 09/23/2022] [Accepted: 10/04/2022] [Indexed: 11/13/2022] Open
Abstract
Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy.
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Affiliation(s)
- Afshin Shoeibi
- FPGA Lab, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Navid Ghassemi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Marjane Khodatars
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran
| | - Juan M. Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, Granada, Spain
| | | | - Ali Khadem
- Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - U. Rajendra Acharya
- Ngee Ann Polytechnic, Singapore, 599489 Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
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Li H, Cao J, Grzybowski A, Jin K, Lou L, Ye J. Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images. Healthcare (Basel) 2023; 11:1739. [PMID: 37372857 DOI: 10.3390/healthcare11121739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 06/07/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
The advent of artificial intelligence (AI), especially the state-of-the-art deep learning frameworks, has begun a silent revolution in all medical subfields, including ophthalmology. Due to their specific microvascular and neural structures, the eyes are anatomically associated with the rest of the body. Hence, ocular image-based AI technology may be a useful alternative or additional screening strategy for systemic diseases, especially where resources are scarce. This review summarizes the current applications of AI related to the prediction of systemic diseases from multimodal ocular images, including cardiovascular diseases, dementia, chronic kidney diseases, and anemia. Finally, we also discuss the current predicaments and future directions of these applications.
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Affiliation(s)
- Huimin Li
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China
| | - Jing Cao
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, 60-836 Poznan, Poland
| | - Kai Jin
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China
| | - Lixia Lou
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China
| | - Juan Ye
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China
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Kennedy KG, Mio M, Goldstein BI, Brambilla P, Delvecchio G. Systematic review and meta-analysis of retinal microvascular caliber in bipolar disorder, major depressive disorder, and schizophrenia. J Affect Disord 2023; 331:342-351. [PMID: 36958491 DOI: 10.1016/j.jad.2023.03.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 03/06/2023] [Accepted: 03/09/2023] [Indexed: 03/25/2023]
Abstract
BACKGROUND Individuals with a severe mental illness (SMI), such as bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SZ), have increased rates of cardiovascular and cerebrovascular disease. Interestingly, it has been reported that retinal microvessels, a proxy cerebrovascular measure, non-invasively assessed via retinal imaging, predict future cardiovascular disease, with some studies also showing anomalous retinal microvascular caliber in SMI. Therefore, this review and meta-analysis evaluated whether retinal microvascular caliber differs between individuals with SMI vs controls and summarized current findings. METHODS A systematic literature search for retinal microvascular caliber and SMI was conducted in Embase and MEDLINE. Studies needed to be published in English before 2022 December 1st and examine retinal microvascular caliber in individuals diagnosed with a SMI. Finally, a meta-analysis of arteriolar and venular caliber in SMI case-controlled studies was also conducted. RESULTS The search yielded 65 unique articles, 11 were included in the review and 6 in the meta-analysis. The meta-analysis found that the SMI group had significantly wider venules than controls (SMD = -0.53; 95 % CI = 0.24, 0.81; p = 0.0004) but not arterioles (SMD = 0.07; 95 % CI = -0.29, 0.44; p = 0.70). Additionally, the systematic review found that poorer retinal microvascular health is associated with greater illness severity. LIMITATIONS Large heterogeneity of findings and small sample size. CONCLUSION This systematic review and meta-analysis found that SMI, specifically SZ, is associated with wider retinal venules. Retinal imaging, a fast, cost-effective, and non-invasive assay of cerebrovascular health, may provide insight into the pathophysiological processes of SMI. However, future longitudinal studies investigating these findings are warranted.
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Affiliation(s)
- Kody G Kennedy
- Centre for Youth Bipolar Disorder, Centre for Addictions and Mental Health, Toronto, Canada; Department of Pharmacology, University of Toronto, Toronto, Canada
| | - Megan Mio
- Centre for Youth Bipolar Disorder, Centre for Addictions and Mental Health, Toronto, Canada; Department of Pharmacology, University of Toronto, Toronto, Canada
| | - Benjamin I Goldstein
- Centre for Youth Bipolar Disorder, Centre for Addictions and Mental Health, Toronto, Canada; Department of Pharmacology, University of Toronto, Toronto, Canada
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
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Sharma M, Patel RK, Garg A, SanTan R, Acharya UR. Automated detection of schizophrenia using deep learning: a review for the last decade. Physiol Meas 2023; 44. [PMID: 36630717 DOI: 10.1088/1361-6579/acb24d] [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: 07/12/2022] [Accepted: 01/11/2023] [Indexed: 01/12/2023]
Abstract
Schizophrenia (SZ) is a devastating mental disorder that disrupts higher brain functions like thought, perception, etc., with a profound impact on the individual's life. Deep learning (DL) can detect SZ automatically by learning signal data characteristics hierarchically without the need for feature engineering associated with traditional machine learning. We performed a systematic review of DL models for SZ detection. Various deep models like long short-term memory, convolution neural networks, AlexNet, etc., and composite methods have been published based on electroencephalographic signals, and structural and/or functional magnetic resonance imaging acquired from SZ patients and healthy patients control subjects in diverse public and private datasets. The studies, the study datasets, and model methodologies are reported in detail. In addition, the challenges of DL models for SZ diagnosis and future works are discussed.
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Affiliation(s)
- Manish Sharma
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India
| | - Ruchit Kumar Patel
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India
| | - Akshat Garg
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India
| | - Ru SanTan
- Department of Cardiology, National Heart Centre Singapore, Singapore 169609, Singapore
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 639798, Singapore.,Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan.,Department of Biomedical Engineering, School of Science and Technology, Singapore 639798, Singapore
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Iao WC, Zhang W, Wang X, Wu Y, Lin D, Lin H. Deep Learning Algorithms for Screening and Diagnosis of Systemic Diseases Based on Ophthalmic Manifestations: A Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13050900. [PMID: 36900043 PMCID: PMC10001234 DOI: 10.3390/diagnostics13050900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 02/16/2023] [Accepted: 02/18/2023] [Indexed: 03/06/2023] Open
Abstract
Deep learning (DL) is the new high-profile technology in medical artificial intelligence (AI) for building screening and diagnosing algorithms for various diseases. The eye provides a window for observing neurovascular pathophysiological changes. Previous studies have proposed that ocular manifestations indicate systemic conditions, revealing a new route in disease screening and management. There have been multiple DL models developed for identifying systemic diseases based on ocular data. However, the methods and results varied immensely across studies. This systematic review aims to summarize the existing studies and provide an overview of the present and future aspects of DL-based algorithms for screening systemic diseases based on ophthalmic examinations. We performed a thorough search in PubMed®, Embase, and Web of Science for English-language articles published until August 2022. Among the 2873 articles collected, 62 were included for analysis and quality assessment. The selected studies mainly utilized eye appearance, retinal data, and eye movements as model input and covered a wide range of systemic diseases such as cardiovascular diseases, neurodegenerative diseases, and systemic health features. Despite the decent performance reported, most models lack disease specificity and public generalizability for real-world application. This review concludes the pros and cons and discusses the prospect of implementing AI based on ocular data in real-world clinical scenarios.
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Affiliation(s)
- Wai Cheng Iao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Weixing Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Xun Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Yuxuan Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Duoru Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
- Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Haikou 570311, China
- Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510060, China
- Correspondence:
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Atagun MI, Sonugur G, Yusifova A, Celik I, Ugurlu N. Machine learning algorithms revealed distorted retinal vascular branching in individuals with bipolar disorder. J Affect Disord 2022; 315:35-41. [PMID: 35905794 DOI: 10.1016/j.jad.2022.07.060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 07/18/2022] [Accepted: 07/22/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Converging evidence designate vascular vulnerability in bipolar disorder. The predisposition progresses into distortion in time, thus detection of the vascular susceptibility may help reducing morbidity and mortality. It was aimed to assess retinal fundus vasculature in cardiovascular risk-free patients with bipolar disorder. METHODS Total of 68 individuals (38 patients with bipolar disorder, 30 healthy controls) were enrolled. In order to avoid from degenerative processes, participants were between 18 and 45 years of age, vascular risk factors were eliminated. Microscopic retinal fundus images were processed with machine learning algorithms (multilayer perceptron and support vector machine) and artificial neural network approaches. RESULTS In comparison to the healthy control group, the bipolar disorder group had lower number of breaking points (P < 0.001), lower number of curved vessel segments (P < 0.001). Total length of smooth vessels was longer (P = 0.040), and total length of curved vessel segments was significantly shorter (P < 0.001) than the control group. Vascular endothelial growth factor levels and gender were the confounders. There were significant correlations between vascular measures and serum lipid levels. LIMITATIONS Sample size was small and patients were on various medications. CONCLUSIONS These results indicate distortion in retinal vascular branching in bipolar disorder. Disrupted branching may reflect disturbed prosperity of retinal vascular plexus in patients with bipolar disorder. Alterations in the retinal vessels might be indicators of disruption in cerebral vascular system efficiency and thus neurovascular unit dysfunction in bipolar disorder.
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Affiliation(s)
- Murat Ilhan Atagun
- Department of Psychiatry, Canakkale Onsekiz Mart University Faculty of Medicine, Canakkale, Turkey.
| | - Guray Sonugur
- Mechatronics Engineering, Faculty of Technology, Afyon Kocatepe University, Afyonkarahisar, Turkey
| | | | - Ibrahim Celik
- Mechatronics Engineering, Faculty of Technology, Afyon Kocatepe University, Afyonkarahisar, Turkey
| | - Nagihan Ugurlu
- Department of Ophtalmology, Ankara Yildirim Beyazit University Faculty of Medicine, Ankara, Turkey
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Silverstein SM, Choi JJ, Green KM, Bowles-Johnson KE, Ramchandran RS. Schizophrenia in Translation: Why the Eye? Schizophr Bull 2022; 48:728-737. [PMID: 35640030 PMCID: PMC9212100 DOI: 10.1093/schbul/sbac050] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Schizophrenia is increasingly recognized as a systemic disease, characterized by dysregulation in multiple physiological systems (eg, neural, cardiovascular, endocrine). Many of these changes are observed as early as the first psychotic episode, and in people at high risk for the disorder. Expanding the search for biomarkers of schizophrenia beyond genes, blood, and brain may allow for inexpensive, noninvasive, and objective markers of diagnosis, phenotype, treatment response, and prognosis. Several anatomic and physiologic aspects of the eye have shown promise as biomarkers of brain health in a range of neurological disorders, and of heart, kidney, endocrine, and other impairments in other medical conditions. In schizophrenia, thinning and volume loss in retinal neural layers have been observed, and are associated with illness progression, brain volume loss, and cognitive impairment. Retinal microvascular changes have also been observed. Abnormal pupil responses and corneal nerve disintegration are related to aspects of brain function and structure in schizophrenia. In addition, studying the eye can inform about emerging cardiovascular, neuroinflammatory, and metabolic diseases in people with early psychosis, and about the causes of several of the visual changes observed in the disorder. Application of the methods of oculomics, or eye-based biomarkers of non-ophthalmological pathology, to the treatment and study of schizophrenia has the potential to provide tools for patient monitoring and data-driven prediction, as well as for clarifying pathophysiology and course of illness. Given their demonstrated utility in neuropsychiatry, we recommend greater adoption of these tools for schizophrenia research and patient care.
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Affiliation(s)
- Steven M Silverstein
- To whom correspondence should be addressed; Department of Psychiatry, University of Rochester Medical Center, Rochester, NY 14642, USA; tel: +1 585-275-6742, e-mail:
| | - Joy J Choi
- Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, USA
| | - Kyle M Green
- Department of Ophthalmology, University of Rochester Medical Center, Rochester, NY, USA
| | | | - Rajeev S Ramchandran
- Department of Ophthalmology, University of Rochester Medical Center, Rochester, NY, USA,Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
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Betzler BK, Rim TH, Sabanayagam C, Cheng CY. Artificial Intelligence in Predicting Systemic Parameters and Diseases From Ophthalmic Imaging. Front Digit Health 2022; 4:889445. [PMID: 35706971 PMCID: PMC9190759 DOI: 10.3389/fdgth.2022.889445] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/06/2022] [Indexed: 12/14/2022] Open
Abstract
Artificial Intelligence (AI) analytics has been used to predict, classify, and aid clinical management of multiple eye diseases. Its robust performances have prompted researchers to expand the use of AI into predicting systemic, non-ocular diseases and parameters based on ocular images. Herein, we discuss the reasons why the eye is well-suited for systemic applications, and review the applications of deep learning on ophthalmic images in the prediction of demographic parameters, body composition factors, and diseases of the cardiovascular, hematological, neurodegenerative, metabolic, renal, and hepatobiliary systems. Three main imaging modalities are included—retinal fundus photographs, optical coherence tomographs and external ophthalmic images. We examine the range of systemic factors studied from ophthalmic imaging in current literature and discuss areas of future research, while acknowledging current limitations of AI systems based on ophthalmic images.
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Affiliation(s)
- Bjorn Kaijun Betzler
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
| | - Tyler Hyungtaek Rim
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Charumathi Sabanayagam
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
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