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Jin K, Li Y, Wu H, Tham YC, Koh V, Zhao Y, Kawasaki R, Grzybowski A, Ye J. Integration of smartphone technology and artificial intelligence for advanced ophthalmic care: A systematic review. ADVANCES IN OPHTHALMOLOGY PRACTICE AND RESEARCH 2024; 4:120-127. [PMID: 38846624 PMCID: PMC11154117 DOI: 10.1016/j.aopr.2024.03.003] [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: 01/14/2024] [Revised: 02/23/2024] [Accepted: 03/24/2024] [Indexed: 06/09/2024]
Abstract
Background The convergence of smartphone technology and artificial intelligence (AI) has revolutionized the landscape of ophthalmic care, offering unprecedented opportunities for diagnosis, monitoring, and management of ocular conditions. Nevertheless, there is a lack of systematic studies on discussing the integration of smartphone and AI in this field. Main text This review includes 52 studies, and explores the integration of smartphones and AI in ophthalmology, delineating its collective impact on screening methodologies, disease detection, telemedicine initiatives, and patient management. The collective findings from the curated studies indicate promising performance of the smartphone-based AI screening for various ocular diseases which encompass major retinal diseases, glaucoma, cataract, visual impairment in children and ocular surface diseases. Moreover, the utilization of smartphone-based imaging modalities, coupled with AI algorithms, is able to provide timely, efficient and cost-effective screening for ocular pathologies. This modality can also facilitate patient self-monitoring, remote patient monitoring and enhancing accessibility to eye care services, particularly in underserved regions. Challenges involving data privacy, algorithm validation, regulatory frameworks and issues of trust are still need to be addressed. Furthermore, evaluation on real-world implementation is imperative as well, and real-world prospective studies are currently lacking. Conclusions Smartphone ocular imaging merged with AI enables earlier, precise diagnoses, personalized treatments, and enhanced service accessibility in eye care. Collaboration is crucial to navigate ethical and data security challenges while responsibly leveraging these innovations, promising a potential revolution in care access and global eye health equity.
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Affiliation(s)
- Kai Jin
- Eye Center, The Second Affiliated Hospital of Zhejiang University School of Medicine; Zhejiang Provincial Key Laboratory of Ophthalmology; Zhejiang Provincial Clinical Research Center for Eye Diseases; Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Yingyu Li
- Eye Center, The Second Affiliated Hospital of Zhejiang University School of Medicine; Zhejiang Provincial Key Laboratory of Ophthalmology; Zhejiang Provincial Clinical Research Center for Eye Diseases; Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Hongkang Wu
- Eye Center, The Second Affiliated Hospital of Zhejiang University School of Medicine; Zhejiang Provincial Key Laboratory of Ophthalmology; Zhejiang Provincial Clinical Research Center for Eye Diseases; Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Yih Chung Tham
- Centre for Innovation and Precision Eye Health, National University of Singapore, Singapore
- Department of Ophthalmology, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Victor Koh
- Centre for Innovation and Precision Eye Health, National University of Singapore, Singapore
- Department of Ophthalmology, National University of Singapore, Singapore
| | - Yitian Zhao
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Ningbo Eye Hospital, Ningbo, China
- Zhejiang International Scientific and Technological Cooperative Base of Biomedical Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Ryo Kawasaki
- Division of Public Health, Department of Social Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
- Artificial Intelligence Center for Medical Research and Application, Osaka University Hospital, Osaka, Japan
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland
| | - Juan Ye
- Eye Center, The Second Affiliated Hospital of Zhejiang University School of Medicine; Zhejiang Provincial Key Laboratory of Ophthalmology; Zhejiang Provincial Clinical Research Center for Eye Diseases; Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
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Latip AAA, Kipli K, Kamaruddin AMNA, Sapawi R, Lias K, Jalil MA, Tamrin KF, Tajudin NMA, Ong HY, Mahmood MH, Jali SK, Sahari SK, Mat DAA, Lim LT. Development of 3D-printed universal adapter in enhancing retinal imaging accessibility. 3D Print Med 2024; 10:23. [PMID: 39028380 DOI: 10.1186/s41205-024-00231-0] [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: 02/07/2024] [Accepted: 07/11/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND The revolutionary technology of smartphone-based retinal imaging has been consistently improving over the years. Smartphone-based retinal image acquisition devices are designed to be portable, easy to use, and cost-efficient, which enables eye care to be more widely accessible especially in geographically remote areas. This enables early disease detection for those who are in low- and middle- income population or just in general has very limited access to eye care. This study investigates the limitation of smartphone compatibility of existing smartphone-based retinal image acquisition devices. Additionally, this study aims to propose a universal adapter design that is usable with an existing smartphone-based retinal image acquisition device known as the PanOptic ophthalmoscope. This study also aims to simulate the reliability, validity, and performance overall of the developed prototype. METHODS A literature review has been conducted that identifies the limitation of smartphone compatibility among existing smartphone-based retinal image acquisition devices. Designing and modeling of proposed adapter were performed using the software AutoCAD 3D. For the proposed performance evaluation, finite element analysis (FEA) in the software Autodesk Inventor and 5-point scale method were demonstrated. RESULTS Published studies demonstrate that most of the existing smartphone-based retinal imaging devices have compatibility limited to specific older smartphone models. This highlights the benefit of a universal adapter in broadening the usability of existing smartphone-based retinal image acquisition devices. A functional universal adapter design has been developed that demonstrates its compatibility with a variety of smartphones regardless of the smartphone dimension or the position of the smartphone's camera lens. The proposed performance evaluation method generates an efficient stress analysis of the proposed adapter design. The end-user survey results show a positive overall performance of the developed universal adapter. However, a significant difference between the expert's views on the developed adapter and the quality of images is observed. CONCLUSION The compatibility of existing smartphone-based retinal imaging devices is still mostly limited to specific smartphone models. Besides this, the concept of a universal and suitable adapter for retinal imaging using the PanOptic ophthalmoscope was presented and validated in this paper. This work provides a platform for future development of smartphone-based ophthalmoscope that is universal.
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Affiliation(s)
- Aisya Amelia Abdul Latip
- Department of Electrical and Electronics Engineering, Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan, Sarawak, 94300, Malaysia
| | - Kuryati Kipli
- Department of Electrical and Electronics Engineering, Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan, Sarawak, 94300, Malaysia.
| | | | - Rohana Sapawi
- Department of Electrical and Electronics Engineering, Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan, Sarawak, 94300, Malaysia
| | - Kasumawati Lias
- Department of Electrical and Electronics Engineering, Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan, Sarawak, 94300, Malaysia
| | - Muhammad Arif Jalil
- Department of Physics, Faculty of Science, Universiti Teknologi Malaysia (UTM), Skudai, Johor, 81310, Malaysia
| | - Khairul Fikri Tamrin
- Department of Mechanical and Manufacturing Engineering, Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan, Sarawak, 94300, Malaysia
| | - Nurul Mirza Afiqah Tajudin
- Department of Electrical and Electronics Engineering, Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan, Sarawak, 94300, Malaysia
| | - Han Yi Ong
- Department of Clinical Science, Faculty of Medicine and Health Sciences (FMHS), Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan, 94300, Malaysia
| | - Muhammad Hamdi Mahmood
- Department of Basic Medical Sciences, Faculty of Medicine and Health Sciences (FMHS), Universiti Malaysia Sarawak (UNIMAS), 94300, Kota Samarahan, Malaysia
| | - Suriati Khartini Jali
- Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan, Sarawak, 94300, Malaysia
| | - Siti Kudnie Sahari
- Department of Electrical and Electronics Engineering, Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan, Sarawak, 94300, Malaysia
| | - Dayang Azra Awang Mat
- Department of Electrical and Electronics Engineering, Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan, Sarawak, 94300, Malaysia
| | - Lik Thai Lim
- Department of Ophthalmology, Faculty of Medicine and Health Sciences (FMHS), Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan, 94300, Malaysia
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Joseph S, Selvaraj J, Mani I, Kumaragurupari T, Shang X, Mudgil P, Ravilla T, He M. Diagnostic Accuracy of Artificial Intelligence-Based Automated Diabetic Retinopathy Screening in Real-World Settings: A Systematic Review and Meta-Analysis. Am J Ophthalmol 2024; 263:214-230. [PMID: 38438095 DOI: 10.1016/j.ajo.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 02/03/2024] [Accepted: 02/12/2024] [Indexed: 03/06/2024]
Abstract
PURPOSE To evaluate the diagnostic accuracy of artificial intelligence (AI)-based automated diabetic retinopathy (DR) screening in real-world settings. DESIGN Systematic review and meta-analysis METHODS: We conducted a systematic review of relevant literature from January 2012 to August 2022 using databases including PubMed, Scopus and Web of Science. The quality of studies was evaluated using Quality Assessment for Diagnostic Accuracy Studies 2 (QUADAS-2) checklist. We calculated pooled accuracy, sensitivity, specificity, and diagnostic odds ratio (DOR) as summary measures. The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO - CRD42022367034). RESULTS We included 34 studies which utilized AI algorithms for diagnosing DR based on real-world fundus images. Quality assessment of these studies indicated a low risk of bias and low applicability concern. Among gradable images, the overall pooled accuracy, sensitivity, specificity, and DOR were 81%, 94% (95% CI: 92.0-96.0), 89% (95% CI: 85.0-92.0) and 128 (95% CI: 80-204) respectively. Sub-group analysis showed that, when acceptable quality imaging could be obtained, non-mydriatic fundus images had a better DOR of 143 (95% CI: 82-251) and studies using 2 field images had a better DOR of 161 (95% CI 74-347). Our meta-regression analysis revealed a statistically significant association between DOR and variables such as the income status, and the type of fundus camera. CONCLUSION Our findings indicate that AI algorithms have acceptable performance in screening for DR using fundus images compared to human graders. Implementing a fundus camera with AI-based software has the potential to assist ophthalmologists in reducing their workload and improving the accuracy of DR diagnosis.
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Affiliation(s)
- Sanil Joseph
- From the Centre for Eye Research Australia (S.J, X.S, M.H), Royal Victorian Eye and Ear Hospital, East Melbourne, Australia; Department of Surgery (Ophthalmology) (S.J, X.S, M.H), The University of Melbourne, Melbourne, Australia; Lions Aravind Institute of Community Ophthalmology (S.J, J.S, T.R), Aravind Eye Care System. Madurai, India.
| | - Jerrome Selvaraj
- Lions Aravind Institute of Community Ophthalmology (S.J, J.S, T.R), Aravind Eye Care System. Madurai, India
| | - Iswarya Mani
- Aravind Eye Hospital and Postgraduate Institute of Ophthalmology (I.M, T.K), Madurai, India
| | | | - Xianwen Shang
- From the Centre for Eye Research Australia (S.J, X.S, M.H), Royal Victorian Eye and Ear Hospital, East Melbourne, Australia; Department of Surgery (Ophthalmology) (S.J, X.S, M.H), The University of Melbourne, Melbourne, Australia
| | - Poonam Mudgil
- School of Medicine (P.M), Western Sydney University, Campbell town, Australia; School of Rural Medicine (P.M), Charles Sturt University, Orange, NSW, Australia
| | - Thulasiraj Ravilla
- Lions Aravind Institute of Community Ophthalmology (S.J, J.S, T.R), Aravind Eye Care System. Madurai, India
| | - Mingguang He
- From the Centre for Eye Research Australia (S.J, X.S, M.H), Royal Victorian Eye and Ear Hospital, East Melbourne, Australia; Department of Surgery (Ophthalmology) (S.J, X.S, M.H), The University of Melbourne, Melbourne, Australia
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Malerbi FK, Nakayama LF, Melo GB, Stuchi JA, Lencione D, Prado PV, Ribeiro LZ, Dib SA, Regatieri CV. Automated Identification of Different Severity Levels of Diabetic Retinopathy Using a Handheld Fundus Camera and Single-Image Protocol. OPHTHALMOLOGY SCIENCE 2024; 4:100481. [PMID: 38694494 PMCID: PMC11060947 DOI: 10.1016/j.xops.2024.100481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 01/20/2024] [Accepted: 01/25/2024] [Indexed: 05/04/2024]
Abstract
Purpose To evaluate the performance of artificial intelligence (AI) systems embedded in a mobile, handheld retinal camera, with a single retinal image protocol, in detecting both diabetic retinopathy (DR) and more-than-mild diabetic retinopathy (mtmDR). Design Multicenter cross-sectional diagnostic study, conducted at 3 diabetes care and eye care facilities. Participants A total of 327 individuals with diabetes mellitus (type 1 or type 2) underwent a retinal imaging protocol enabling expert reading and automated analysis. Methods Participants underwent fundus photographs using a portable retinal camera (Phelcom Eyer). The captured images were automatically analyzed by deep learning algorithms retinal alteration score (RAS) and diabetic retinopathy alteration score (DRAS), consisting of convolutional neural networks trained on EyePACS data sets and fine-tuned using data sets of portable device fundus images. The ground truth was the classification of DR corresponding to adjudicated expert reading, performed by 3 certified ophthalmologists. Main Outcome Measures Primary outcome measures included the sensitivity and specificity of the AI system in detecting DR and/or mtmDR using a single-field, macula-centered fundus photograph for each eye, compared with a rigorous clinical reference standard comprising the reading center grading of 2-field imaging protocol using the International Classification of Diabetic Retinopathy severity scale. Results Of 327 analyzed patients (mean age, 57.0 ± 16.8 years; mean diabetes duration, 16.3 ± 9.7 years), 307 completed the study protocol. Sensitivity and specificity of the AI system were high in detecting any DR with DRAS (sensitivity, 90.48% [95% confidence interval (CI), 84.99%-94.46%]; specificity, 90.65% [95% CI, 84.54%-94.93%]) and mtmDR with the combination of RAS and DRAS (sensitivity, 90.23% [95% CI, 83.87%-94.69%]; specificity, 85.06% [95% CI, 78.88%-90.00%]). The area under the receiver operating characteristic curve was 0.95 for any DR and 0.89 for mtmDR. Conclusions This study showed a high accuracy for the detection of DR in different levels of severity with a single retinal photo per eye in an all-in-one solution, composed of a portable retinal camera powered by AI. Such a strategy holds great potential for increasing coverage rates of screening programs, contributing to prevention of avoidable blindness. Financial Disclosures F.K.M. is a medical consultant for Phelcom Technologies. J.A.S. is Chief Executive Officer and proprietary of Phelcom Technologies. D.L. is Chief Technology Officer and proprietary of Phelcom Technologies. P.V.P. is an employee at Phelcom Technologies.
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Chen D, Geevarghese A, Lee S, Plovnick C, Elgin C, Zhou R, Oermann E, Aphinyonaphongs Y, Al-Aswad LA. Transparency in Artificial Intelligence Reporting in Ophthalmology-A Scoping Review. OPHTHALMOLOGY SCIENCE 2024; 4:100471. [PMID: 38591048 PMCID: PMC11000111 DOI: 10.1016/j.xops.2024.100471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 11/18/2023] [Accepted: 01/12/2024] [Indexed: 04/10/2024]
Abstract
Topic This scoping review summarizes artificial intelligence (AI) reporting in ophthalmology literature in respect to model development and validation. We characterize the state of transparency in reporting of studies prospectively validating models for disease classification. Clinical Relevance Understanding what elements authors currently describe regarding their AI models may aid in the future standardization of reporting. This review highlights the need for transparency to facilitate the critical appraisal of models prior to clinical implementation, to minimize bias and inappropriate use. Transparent reporting can improve effective and equitable use in clinical settings. Methods Eligible articles (as of January 2022) from PubMed, Embase, Web of Science, and CINAHL were independently screened by 2 reviewers. All observational and clinical trial studies evaluating the performance of an AI model for disease classification of ophthalmic conditions were included. Studies were evaluated for reporting of parameters derived from reporting guidelines (CONSORT-AI, MI-CLAIM) and our previously published editorial on model cards. The reporting of these factors, which included basic model and dataset details (source, demographics), and prospective validation outcomes, were summarized. Results Thirty-seven prospective validation studies were included in the scoping review. Eleven additional associated training and/or retrospective validation studies were included if this information could not be determined from the primary articles. These 37 studies validated 27 unique AI models; multiple studies evaluated the same algorithms (EyeArt, IDx-DR, and Medios AI). Details of model development were variably reported; 18 of 27 models described training dataset annotation and 10 of 27 studies reported training data distribution. Demographic information of training data was rarely reported; 7 of the 27 unique models reported age and gender and only 2 reported race and/or ethnicity. At the level of prospective clinical validation, age and gender of populations was more consistently reported (29 and 28 of 37 studies, respectively), but only 9 studies reported race and/or ethnicity data. Scope of use was difficult to discern for the majority of models. Fifteen studies did not state or imply primary users. Conclusion Our scoping review demonstrates variable reporting of information related to both model development and validation. The intention of our study was not to assess the quality of the factors we examined, but to characterize what information is, and is not, regularly reported. Our results suggest the need for greater transparency in the reporting of information necessary to determine the appropriateness and fairness of these tools prior to clinical use. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Dinah Chen
- Department of Ophthalmology, NYU Langone Health, New York, New York
| | | | - Samuel Lee
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, New York
| | | | - Cansu Elgin
- Department of Ophthalmology, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Raymond Zhou
- Department of Neurosurgery, Vanderbilt School of Medicine, Nashville, Tennessee
| | - Eric Oermann
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, New York
- Department of Neurosurgery, NYU Langone Health, New York, New York
| | - Yindalon Aphinyonaphongs
- Department of Medicine, NYU Langone Health, New York, New York
- Department of Population Health, NYU Grossman School of Medicine, New York, New York
| | - Lama A. Al-Aswad
- Department of Ophthalmology, NYU Langone Health, New York, New York
- Department of Population Health, NYU Grossman School of Medicine, New York, New York
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Ahn SJ, Kim YH. Clinical Applications and Future Directions of Smartphone Fundus Imaging. Diagnostics (Basel) 2024; 14:1395. [PMID: 39001285 PMCID: PMC11240943 DOI: 10.3390/diagnostics14131395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 06/24/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024] Open
Abstract
The advent of smartphone fundus imaging technology has marked a significant evolution in the field of ophthalmology, offering a novel approach to the diagnosis and management of retinopathy. This review provides an overview of smartphone fundus imaging, including clinical applications, advantages, limitations, clinical applications, and future directions. The traditional fundus imaging techniques are limited by their cost, portability, and accessibility, particularly in resource-limited settings. Smartphone fundus imaging emerges as a cost-effective, portable, and accessible alternative. This technology facilitates the early detection and monitoring of various retinal pathologies, including diabetic retinopathy, age-related macular degeneration, and retinal vascular disorders, thereby democratizing access to essential diagnostic services. Despite its advantages, smartphone fundus imaging faces challenges in image quality, standardization, regulatory considerations, and medicolegal issues. By addressing these limitations, this review highlights the areas for future research and development to fully harness the potential of smartphone fundus imaging in enhancing patient care and visual outcomes. The integration of this technology into telemedicine is also discussed, underscoring its role in facilitating remote patient care and collaborative care among physicians. Through this review, we aim to contribute to the understanding and advancement of smartphone fundus imaging as a valuable tool in ophthalmic practice, paving the way for its broader adoption and integration into medical diagnostics.
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Affiliation(s)
- Seong Joon Ahn
- Department of Ophthalmology, Hanyang University Hospital, Hanyang University College of Medicine, Seoul 04763, Republic of Korea
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La Franca L, Rutigliani C, Checchin L, Lattanzio R, Bandello F, Cicinelli MV. Rate and Predictors of Misclassification of Active Diabetic Macular Edema as Detected by an Automated Retinal Image Analysis System. Ophthalmol Ther 2024; 13:1553-1567. [PMID: 38587776 PMCID: PMC11109071 DOI: 10.1007/s40123-024-00929-8] [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: 11/27/2023] [Accepted: 03/07/2024] [Indexed: 04/09/2024] Open
Abstract
INTRODUCTION The aim of this work is to estimate the sensitivity, specificity, and misclassification rate of an automated retinal image analysis system (ARIAS) in diagnosing active diabetic macular edema (DME) and to identify factors associated with true and false positives. METHODS We conducted a cross-sectional study of prospectively enrolled patients with diabetes mellitus (DM) referred to a tertiary medical retina center for screening or management of DME. All patients underwent two-field fundus photography (macula- and disc-centered) with a true-color confocal camera; images were processed by EyeArt V.2.1.0 (Woodland Hills, CA, USA). Active DME was defined as the presence of intraretinal or subretinal fluid on spectral-domain optical coherence tomography (SD-OCT). Sensitivity and specificity and their 95% confidence intervals (CIs) were calculated. Variables associated with true (i.e., DME labeled as present by ARIAS + fluid on SD-OCT) and false positives (i.e., DME labeled as present by ARIAS + no fluid on SD-OCT) of active DME were explored. RESULTS A total of 298 eyes were included; 92 eyes (31%) had active DME. ARIAS sensitivity and specificity were 82.61% (95% CI 72.37-89.60) and 84.47% (95% CI 78.34-89.10). The misclassification rate was 16%. Factors associated with true positives included younger age (p = 0.01), shorter DM duration (p = 0.006), presence of hard exudates (p = 0.005), and microaneurysms (p = 0.002). Factors associated with false positives included longer DM duration (p = 0.01), worse diabetic retinopathy severity (p = 0.008), history of inactivated DME (p < 0.001), and presence of hard exudates (p < 0.001), microaneurysms (p < 0.001), or epiretinal membrane (p = 0.06). CONCLUSIONS The sensitivity of ARIAS was diminished in older patients and those without DME-related fundus lesions, while the specificity was reduced in cases with a history of inactivated DME. ARIAS performed well in screening for naïve DME but is not effective in surveillance inactivated DME.
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Affiliation(s)
- Lamberto La Franca
- Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, IRCCS Ospedale San Raffaele, University Vita-Salute, Via Olgettina 60, 20132, Milan, Italy
| | - Carola Rutigliani
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Lisa Checchin
- Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, IRCCS Ospedale San Raffaele, University Vita-Salute, Via Olgettina 60, 20132, Milan, Italy
| | - Rosangela Lattanzio
- Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, IRCCS Ospedale San Raffaele, University Vita-Salute, Via Olgettina 60, 20132, Milan, Italy
| | - Francesco Bandello
- Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, IRCCS Ospedale San Raffaele, University Vita-Salute, Via Olgettina 60, 20132, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Maria Vittoria Cicinelli
- Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, IRCCS Ospedale San Raffaele, University Vita-Salute, Via Olgettina 60, 20132, Milan, Italy.
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy.
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Rajalakshmi R, Mohammed R, Vengatesan K, PramodKumar TA, Venkatesan U, Usha M, Arulmalar S, Prathiba V, Mohan V. Wide-field imaging with smartphone based fundus camera: grading of severity of diabetic retinopathy and locating peripheral lesions in diabetic retinopathy. Eye (Lond) 2024; 38:1471-1476. [PMID: 38297154 PMCID: PMC11126401 DOI: 10.1038/s41433-024-02928-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 12/13/2023] [Accepted: 01/08/2024] [Indexed: 02/02/2024] Open
Abstract
AIM To assess the performance of smartphone based wide-field retinal imaging (WFI) versus ultra-wide-field imaging (UWFI) for assessment of sight-threatening diabetic retinopathy (STDR) as well as locating predominantly peripheral lesions (PPL) of DR. METHODS Individuals with type 2 diabetes with varying grades of DR underwent nonmydriatic UWFI with Daytona Plus camera followed by mydriatic WFI with smartphone-based Vistaro camera at a tertiary care diabetes centre in South India in 2021-22. Grading of DR as well as identification of PPL (DR lesions beyond the posterior pole) in the retinal images of both cameras was performed by senior retina specialists. STDR was defined by the presence of severe non-proliferative DR, proliferative DR or diabetic macular oedema (DME). The sensitivity and specificity of smartphone based WFI for detection of PPL and STDR was assessed. Agreement between the graders for both cameras was compared. RESULTS Retinal imaging was carried out in 318 eyes of 160 individuals (mean age 54.7 ± 9 years; mean duration of diabetes 16.6 ± 7.9 years). The sensitivity and specificity for detection of STDR by Vistaro camera was 92.7% (95% CI 80.1-98.5) and 96.6% (95% CI 91.5-99.1) respectively and 95.1% (95% CI 83.5-99.4) and 95.7% (95% CI 90.3-98.6) by Daytona Plus respectively. PPL were detected in 89 (27.9%) eyes by WFI by Vistaro camera and in 160 (50.3%) eyes by UWFI. However, this did not translate to any significant difference in the grading of STDR between the two imaging systems. In both devices, PPL were most common in supero-temporal quadrant (34%). The prevalence of PPL increased with increasing severity of DR with both cameras (p < 0.001). The kappa comparison between the 2 graders for varying grades of severity of DR was 0.802 (p < 0.001) for Vistaro and 0.753 (p < 0.001) for Daytona Plus camera. CONCLUSION Mydriatic smartphone-based widefield imaging has high sensitivity and specificity for detecting STDR and can be used to screen for peripheral retinal lesions beyond the posterior pole in individuals with diabetes.
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Affiliation(s)
- Ramachandran Rajalakshmi
- Department of Ophthalmology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India.
| | - Rajah Mohammed
- Department of Ophthalmology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | - Kalaivani Vengatesan
- Department of Ophthalmology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | | | - Ulagamathesan Venkatesan
- Department of Biostatistics and Data Management, Madras Diabetes Research Foundation, Chennai, India
| | - Manoharan Usha
- Department of Ophthalmology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | - Subramanian Arulmalar
- Department of Ophthalmology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | - Vijayaraghavan Prathiba
- Department of Ophthalmology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | - Viswanathan Mohan
- Department of Diabetology, Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
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Ye J, Woods D, Jordan N, Starren J. The role of artificial intelligence for the application of integrating electronic health records and patient-generated data in clinical decision support. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2024; 2024:459-467. [PMID: 38827061 PMCID: PMC11141850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
This narrative review aims to identify and understand the role of artificial intelligence in the application of integrated electronic health records (EHRs) and patient-generated health data (PGHD) in clinical decision support. We focused on integrated data that combined PGHD and EHR data, and we investigated the role of artificial intelligence (AI) in the application. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search articles in six databases: PubMed, Embase, Web of Science, Scopus, ACM Digital Library, and IEEE Computer Society Digital Library. In addition, we also synthesized seminal sources, including other systematic reviews, reports, and white papers, to inform the context, history, and development of this field. Twenty-six publications met the review criteria after screening. The EHR-integrated PGHD introduces benefits to health care, including empowering patients and families to engage via shared decision-making, improving the patient-provider relationship, and reducing the time and cost of clinical visits. AI's roles include cleaning and management of heterogeneous datasets, assisting in identifying dynamic patterns to improve clinical care processes, and providing more sophisticated algorithms to better predict outcomes and propose precise recommendations based on the integrated data. Challenges mainly stem from the large volume of integrated data, data standards, data exchange and interoperability, security and privacy, interpretation, and meaningful use. The use of PGHD in health care is at a promising stage but needs further work for widespread adoption and seamless integration into health care systems. AI-driven, EHR-integrated PGHD systems can greatly improve clinicians' abilities to diagnose patients' health issues, classify risks at the patient level by drawing on the power of integrated data, and provide much-needed support to clinics and hospitals. With EHR-integrated PGHD, AI can help transform health care by improving diagnosis, treatment, and the delivery of clinical care, thus improving clinical decision support.
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Affiliation(s)
- Jiancheng Ye
- Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Donna Woods
- Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Neil Jordan
- Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Justin Starren
- Feinberg School of Medicine, Northwestern University, Chicago, USA
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10
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Passaro ML, Airaldi M, Ancona C, Ventura M, Iodice P, Costagliola C, Semeraro F, Romano V. Open-source, 3D printable IOL holder for detailed, smartphone-based anterior segment photography. Eur J Ophthalmol 2024:11206721241248305. [PMID: 38659359 DOI: 10.1177/11206721241248305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Smartphones are increasingly relevant resources in medical practice as they are ubiquitous and reasonably cheap. Among the advantages of using smartphones in medical practise, there is the possibility of obtaining reproducible photographic documentation of various conditions. This is particularly true in the ophthalmic field, where anterior segment color photography plays a significant role in the diagnosis and the management of ocular surface diseases. Here we propose an original design for an open-source smartphone accessory for taking and sharing high-definition photographs of the anterior segment. It can be easily reproduced via 3D printing, and it only needs to be integrated with an intraocular lens (IOL), widely available to the majority of ophthalmologists. Compared to other solutions described previously, it allows a precise and reproducible placement of the IOL on the smartphone camera, avoiding manual positioning that could result tricky and time-consuming. The IOL holder is cheap, scalable, portable and it can be quickly assembled and disassembled, without permanently modifying the smartphone camera.
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Affiliation(s)
- Maria Laura Passaro
- Department of Neurosciences, Reproductive Sciences and Dentistry, University of Naples "Federico II", Naples, Italy
- Department of Medicine and Health Sciences "V. Tiberio", University of Molise, Campobasso, Italy
| | - Matteo Airaldi
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
- St. Paul's Eye Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Chiara Ancona
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Mariacarmela Ventura
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Pierfrancesco Iodice
- Department of Architecture and Industrial Design, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Ciro Costagliola
- Department of Neurosciences, Reproductive Sciences and Dentistry, University of Naples "Federico II", Naples, Italy
| | - Francesco Semeraro
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
- Eye Unit, ASST Spedali Civili di Brescia, Brescia, Italy
| | - Vito Romano
- St. Paul's Eye Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
- Eye Unit, ASST Spedali Civili di Brescia, Brescia, Italy
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11
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Musetti D, Cutolo CA, Bonetto M, Giacomini M, Maggi D, Viviani GL, Gandin I, Traverso CE, Nicolò M. Autonomous artificial intelligence versus teleophthalmology for diabetic retinopathy. Eur J Ophthalmol 2024:11206721241248856. [PMID: 38656241 DOI: 10.1177/11206721241248856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Purpose: To assess the role of artificial intelligence (AI) based automated software for detection of Diabetic Retinopathy (DR) compared with the evaluation of digital retinography by two double masked retina specialists. Methods: Two-hundred one patients (mean age 65 ± 13 years) with type 1 diabetes mellitus or type 2 diabetes mellitus were included. All patients were undergoing a retinography and spectral domain optical coherence tomography (SD-OCT, DRI 3D OCT-2000, Topcon) of the macula. The retinal photographs were graded using two validated AI DR screening software (Eye Art TM and IDx-DR) designed to identify more than mild DR. Results: Retinal images of 201 patients were graded. DR (more than mild DR) was detected by the ophthalmologists in 38 (18.9%) patients and by the AI-algorithms in 36 patients (with 30 eyes diagnosed by both algorithms). Ungradable patients by the AI software were 13 (6.5%) and 16 (8%) for the Eye Art and IDx-DR, respectively. Both AI software strategies showed a high sensitivity and specificity for detecting any more than mild DR without showing any statistically significant difference between them. Conclusions: The comparison between the diagnosis provided by artificial intelligence based automated software and the reference clinical diagnosis showed that they can work at a level of sensitivity that is similar to that achieved by experts.
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Affiliation(s)
- Donatella Musetti
- Clinica Oculistica DiNOGMI, Università di Genova, Ospedale Policlinico San Martino IRCCS, Genova, Italy
| | - Carlo Alberto Cutolo
- Clinica Oculistica DiNOGMI, Università di Genova, Ospedale Policlinico San Martino IRCCS, Genova, Italy
| | | | | | - Davide Maggi
- Clinica Diabetologica, Università di Genova, Ospedale Policlinico San Martino IRCCS, Genova, Italy
| | - Giorgio Luciano Viviani
- Clinica Diabetologica, Università di Genova, Ospedale Policlinico San Martino IRCCS, Genova, Italy
| | - Ilaria Gandin
- Sciences, Biostatistic Unit, University of Trieste, Italy
| | - Carlo Enrico Traverso
- Clinica Oculistica DiNOGMI, Università di Genova, Ospedale Policlinico San Martino IRCCS, Genova, Italy
| | - Massimo Nicolò
- Clinica Oculistica DiNOGMI, Università di Genova, Ospedale Policlinico San Martino IRCCS, Genova, Italy
- Fondazione per la Macula onlus, Genova, Italy
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12
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Chen Q, Peng J, Zhao S, Liu W. Automatic artery/vein classification methods for retinal blood vessel: A review. Comput Med Imaging Graph 2024; 113:102355. [PMID: 38377630 DOI: 10.1016/j.compmedimag.2024.102355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 02/06/2024] [Accepted: 02/06/2024] [Indexed: 02/22/2024]
Abstract
Automatic retinal arteriovenous classification can assist ophthalmologists in disease early diagnosis. Deep learning-based methods and topological graph-based methods have become the main solutions for retinal arteriovenous classification in recent years. This paper reviews the automatic retinal arteriovenous classification methods from 2003 to 2022. Firstly, we compare different methods and provide comparison tables of the summary results. Secondly, we complete the classification of the public arteriovenous classification datasets and provide the annotation development tables of different datasets. Finally, we sort out the challenges of evaluation methods and provide a comprehensive evaluation system. Quantitative and qualitative analysis shows the changes in research hotspots over time, Quantitative and qualitative analyses reveal the evolution of research hotspots over time, highlighting the significance of exploring the integration of deep learning with topological information in future research.
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Affiliation(s)
- Qihan Chen
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Jianqing Peng
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; Guangdong Provincial Key Laboratory of Fire Science and Technology, Guangzhou 510006, China.
| | - Shen Zhao
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
| | - Wanquan Liu
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
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Parmar UPS, Surico PL, Singh RB, Romano F, Salati C, Spadea L, Musa M, Gagliano C, Mori T, Zeppieri M. Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:527. [PMID: 38674173 PMCID: PMC11052176 DOI: 10.3390/medicina60040527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 03/12/2024] [Accepted: 03/21/2024] [Indexed: 04/28/2024]
Abstract
Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its potential to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. Herein, we elucidate the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their application in ophthalmology, underscoring the significance of AI-driven solutions in addressing the complexity and variability of retinal diseases. Furthermore, we delve into the specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), Macular Neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), Retinitis Pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. We focus on the current landscape of AI technologies, including various AI models, their performance metrics, and clinical implications. Furthermore, we aim to address challenges and pitfalls associated with the integration of AI in clinical practice, including the "black box phenomenon", biases in data representation, and limitations in comprehensive patient assessment. In conclusion, this review emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery. It highlights the importance of leveraging AI to augment, rather than replace, human expertise, thereby maximizing its potential to revolutionize healthcare delivery, mitigate healthcare disparities, and improve patient outcomes in the evolving landscape of medicine.
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Affiliation(s)
| | - Pier Luigi Surico
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
- Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Rohan Bir Singh
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Francesco Romano
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Carlo Salati
- Department of Ophthalmology, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy
| | - Leopoldo Spadea
- Eye Clinic, Policlinico Umberto I, “Sapienza” University of Rome, 00142 Rome, Italy
| | - Mutali Musa
- Department of Optometry, University of Benin, Benin City 300238, Edo State, Nigeria
| | - Caterina Gagliano
- Faculty of Medicine and Surgery, University of Enna “Kore”, Piazza dell’Università, 94100 Enna, Italy
- Eye Clinic, Catania University, San Marco Hospital, Viale Carlo Azeglio Ciampi, 95121 Catania, Italy
| | - Tommaso Mori
- Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
- Department of Ophthalmology, University of California San Diego, La Jolla, CA 92122, USA
| | - Marco Zeppieri
- Department of Ophthalmology, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy
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14
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Panchal B, Asanad S, Malek R, Munir K, Schocket LS. Improving Access to Eye Care Through Community Health Screenings Using Artificial Intelligence. Ophthalmic Epidemiol 2024:1-3. [PMID: 38381150 DOI: 10.1080/09286586.2024.2317838] [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: 11/03/2023] [Accepted: 02/06/2024] [Indexed: 02/22/2024]
Abstract
PURPOSE To the best of our knowledge, implementation of artificial intelligence (AI)-based vision screening in community health fair settings has not been previously studied. This prospective cohort study explored the incorporation of AI in a community health fair setting to improve access to eyecare. METHODS Vision screening was implemented during a community health fair event using an AI-based non-mydriatic fundus camera. In addition, a questionnaire was provided to survey the various barriers to eyecare and assess eye health literacy. RESULTS A total of 53 individuals were screened at this event. Notably, about 88% of participants had follow-up appointments scheduled accordingly with an approximate 62% attendance rate. The most reported barrier to eyecare was lack of health insurance followed by transportation. CONCLUSION The addition of AI-based vision screening in community health fairs may ultimately help improve access to eye care.
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Affiliation(s)
- Bhakti Panchal
- Department of Ophthalmology and Visual Sciences, University of Maryland, Baltimore, Maryland, USA
| | - Samuel Asanad
- Department of Ophthalmology and Visual Sciences, University of Maryland, Baltimore, Maryland, USA
| | - Rana Malek
- Division of Endocrinology, University of Maryland, Baltimore, Maryland, USA
| | - Kashif Munir
- Division of Endocrinology, University of Maryland, Baltimore, Maryland, USA
| | - Lisa S Schocket
- Department of Ophthalmology and Visual Sciences, University of Maryland, Baltimore, Maryland, USA
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15
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Naz H, Nijhawan R, Ahuja NJ. Clinical utility of handheld fundus and smartphone-based camera for monitoring diabetic retinal diseases: a review study. Int Ophthalmol 2024; 44:41. [PMID: 38334896 DOI: 10.1007/s10792-024-02975-4] [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: 05/08/2023] [Accepted: 10/29/2023] [Indexed: 02/10/2024]
Abstract
Diabetic retinopathy (DR) is the leading global cause of vision loss, accounting for 4.8% of global blindness cases as estimated by the World Health Organization (WHO). Fundus photography is crucial in ophthalmology as a diagnostic tool for capturing retinal images. However, resource and infrastructure constraints limit access to traditional tabletop fundus cameras in developing countries. Additionally, these conventional cameras are expensive, bulky, and not easily transportable. In contrast, the newer generation of handheld and smartphone-based fundus cameras offers portability, user-friendliness, and affordability. Despite their potential, there is a lack of comprehensive review studies examining the clinical utilities of these handheld (e.g. Zeiss Visuscout 100, Volk Pictor Plus, Volk Pictor Prestige, Remidio NMFOP, FC161) and smartphone-based (e.g. D-EYE, iExaminer, Peek Retina, Volk iNview, Volk Vistaview, oDocs visoScope, oDocs Nun, oDocs Nun IR) fundus cameras. This review study aims to evaluate the feasibility and practicality of these available handheld and smartphone-based cameras in medical settings, emphasizing their advantages over traditional tabletop fundus cameras. By highlighting various clinical settings and use scenarios, this review aims to fill this gap by evaluating the efficiency, feasibility, cost-effectiveness, and remote capabilities of handheld and smartphone fundus cameras, ultimately enhancing the accessibility of ophthalmic services.
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Affiliation(s)
- Huma Naz
- Department of Computer Science, University of Petroleum and Energy Studies, Dehradun, India.
| | - Rahul Nijhawan
- Thapar Institute of Engineering and Technology, Patiala, Punjab, India
| | - Neelu Jyothi Ahuja
- Department of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
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16
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Abou Taha A, Dinesen S, Vergmann AS, Grauslund J. Present and future screening programs for diabetic retinopathy: a narrative review. Int J Retina Vitreous 2024; 10:14. [PMID: 38310265 PMCID: PMC10838429 DOI: 10.1186/s40942-024-00534-8] [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: 12/22/2023] [Accepted: 01/19/2024] [Indexed: 02/05/2024] Open
Abstract
Diabetes is a prevalent global concern, with an estimated 12% of the global adult population affected by 2045. Diabetic retinopathy (DR), a sight-threatening complication, has spurred diverse screening approaches worldwide due to advances in DR knowledge, rapid technological developments in retinal imaging and variations in healthcare resources.Many high income countries have fully implemented or are on the verge of completing a national Diabetic Eye Screening Programme (DESP). Although there have been some improvements in DR screening in Africa, Asia, and American countries further progress is needed. In low-income countries, only one out of 29, partially implemented a DESP, while 21 out of 50 lower-middle-income countries have started the DR policy cycle. Among upper-middle-income countries, a third of 59 nations have advanced in DR agenda-setting, with five having a comprehensive national DESP and 11 in the early stages of implementation.Many nations use 2-4 fields fundus images, proven effective with 80-98% sensitivity and 86-100% specificity compared to the traditional seven-field evaluation for DR. A cell phone based screening with a hand held retinal camera presents a potential low-cost alternative as imaging device. While this method in low-resource settings may not entirely match the sensitivity and specificity of seven-field stereoscopic photography, positive outcomes are observed.Individualized DR screening intervals are the standard in many high-resource nations. In countries that lacks a national DESP and resources, screening are more sporadic, i.e. screening intervals are not evidence-based and often less frequently, which can lead to late recognition of treatment required DR.The rising global prevalence of DR poses an economic challenge to nationwide screening programs AI-algorithms have showed high sensitivity and specificity for detection of DR and could provide a promising solution for the future screening burden.In summary, this narrative review enlightens on the epidemiology of DR and the necessity for effective DR screening programs. Worldwide evolution in existing approaches for DR screening has showed promising results but has also revealed limitations. Technological advancements, such as handheld imaging devices, tele ophthalmology and artificial intelligence enhance cost-effectiveness, but also the accessibility of DR screening in countries with low resources or where distance to or a shortage of ophthalmologists exists.
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Affiliation(s)
- Andreas Abou Taha
- Department of Ophthalmology, Odense University Hospital, Sdr. Boulevard 29, 5000, Odense, Denmark.
| | - Sebastian Dinesen
- Department of Ophthalmology, Odense University Hospital, Sdr. Boulevard 29, 5000, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Steno Diabetes Center Odense, Odense University Hospital, Odense, Denmark
| | - Anna Stage Vergmann
- Department of Ophthalmology, Odense University Hospital, Sdr. Boulevard 29, 5000, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Jakob Grauslund
- Department of Ophthalmology, Odense University Hospital, Sdr. Boulevard 29, 5000, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Steno Diabetes Center Odense, Odense University Hospital, Odense, Denmark
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17
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Vilela MAP, Arrigo A, Parodi MB, da Silva Mengue C. Smartphone Eye Examination: Artificial Intelligence and Telemedicine. Telemed J E Health 2024; 30:341-353. [PMID: 37585566 DOI: 10.1089/tmj.2023.0041] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023] Open
Abstract
Background: The current medical scenario is closely linked to recent progress in telecommunications, photodocumentation, and artificial intelligence (AI). Smartphone eye examination may represent a promising tool in the technological spectrum, with special interest for primary health care services. Obtaining fundus imaging with this technique has improved and democratized the teaching of fundoscopy, but in particular, it contributes greatly to screening diseases with high rates of blindness. Eye examination using smartphones essentially represents a cheap and safe method, thus contributing to public policies on population screening. This review aims to provide an update on the use of this resource and its future prospects, especially as a screening and ophthalmic diagnostic tool. Methods: In this review, we surveyed major published advances in retinal and anterior segment analysis using AI. We performed an electronic search on the Medical Literature Analysis and Retrieval System Online (MEDLINE), EMBASE, and Cochrane Library for published literature without a deadline. We included studies that compared the diagnostic accuracy of smartphone ophthalmoscopy for detecting prevalent diseases with an accurate or commonly employed reference standard. Results: There are few databases with complete metadata, providing demographic data, and few databases with sufficient images involving current or new therapies. It should be taken into consideration that these are databases containing images captured using different systems and formats, with information often being excluded without essential detailing of the reasons for exclusion, which further distances them from real-life conditions. The safety, portability, low cost, and reproducibility of smartphone eye images are discussed in several studies, with encouraging results. Conclusions: The high level of agreement between conventional and a smartphone method shows a powerful arsenal for screening and early diagnosis of the main causes of blindness, such as cataract, glaucoma, diabetic retinopathy, and age-related macular degeneration. In addition to streamlining the medical workflow and bringing benefits for public health policies, smartphone eye examination can make safe and quality assessment available to the population.
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Affiliation(s)
| | - Alessandro Arrigo
- Department of Ophthalmology, Scientific Institute San Raffaele, Milan, Italy
- University Vita-Salute, Milan, Italy
| | - Maurizio Battaglia Parodi
- Department of Ophthalmology, Scientific Institute San Raffaele, Milan, Italy
- University Vita-Salute, Milan, Italy
| | - Carolina da Silva Mengue
- Post-Graduation Ophthalmological School, Ivo Corrêa-Meyer/Cardiology Institute, Porto Alegre, Brazil
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18
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Wolf RM, Channa R, Liu TYA, Zehra A, Bromberger L, Patel D, Ananthakrishnan A, Brown EA, Prichett L, Lehmann HP, Abramoff MD. Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial. Nat Commun 2024; 15:421. [PMID: 38212308 PMCID: PMC10784572 DOI: 10.1038/s41467-023-44676-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 12/21/2023] [Indexed: 01/13/2024] Open
Abstract
Diabetic retinopathy can be prevented with screening and early detection. We hypothesized that autonomous artificial intelligence (AI) diabetic eye exams at the point-of-care would increase diabetic eye exam completion rates in a racially and ethnically diverse youth population. AI for Children's diabetiC Eye ExamS (NCT05131451) is a parallel randomized controlled trial that randomized youth (ages 8-21 years) with type 1 and type 2 diabetes to intervention (autonomous artificial intelligence diabetic eye exam at the point of care), or control (scripted eye care provider referral and education) in an academic pediatric diabetes center. The primary outcome was diabetic eye exam completion rate within 6 months. The secondary outcome was the proportion of participants who completed follow-through with an eye care provider if deemed appropriate. Diabetic eye exam completion rate was significantly higher (100%, 95%CI: 95.5%, 100%) in the intervention group (n = 81) than the control group (n = 83) (22%, 95%CI: 14.2%, 32.4%)(p < 0.001). In the intervention arm, 25/81 participants had an abnormal result, of whom 64% (16/25) completed follow-through with an eye care provider, compared to 22% in the control arm (p < 0.001). Autonomous AI increases diabetic eye exam completion rates in youth with diabetes.
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Affiliation(s)
- Risa M Wolf
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA
| | - T Y Alvin Liu
- Wilmer Eye Institute at the Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Anum Zehra
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Lee Bromberger
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Dhruva Patel
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | | | - Elizabeth A Brown
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Laura Prichett
- Johns Hopkins School of Medicine Biostatistics, Epidemiology and Data Management (BEAD) Core, Baltimore, MD, USA
| | - Harold P Lehmann
- Section on Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, USA
| | - Michael D Abramoff
- Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA
- Digital Diagnostics Inc, Coralville, IA, USA
- Iowa City VA Medical Center, Iowa City, IA, USA
- Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, USA
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA
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19
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Naz H, Nijhawan R, Ahuja NJ, Saba T, Alamri FS, Rehman A. Micro-segmentation of retinal image lesions in diabetic retinopathy using energy-based fuzzy C-Means clustering (EFM-FCM). Microsc Res Tech 2024; 87:78-94. [PMID: 37681440 DOI: 10.1002/jemt.24413] [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: 07/04/2023] [Revised: 08/06/2023] [Accepted: 08/24/2023] [Indexed: 09/09/2023]
Abstract
Diabetic retinopathy (DR) is a prevalent cause of global visual impairment, contributing to approximately 4.8% of blindness cases worldwide as reported by the World Health Organization (WHO). The condition is characterized by pathological abnormalities in the retinal layer, including microaneurysms, vitreous hemorrhages, and exudates. Microscopic analysis of retinal images is crucial in diagnosing and treating DR. This article proposes a novel method for early DR screening using segmentation and unsupervised learning techniques. The approach integrates a neural network energy-based model into the Fuzzy C-Means (FCM) algorithm to enhance convergence criteria, aiming to improve the accuracy and efficiency of automated DR screening tools. The evaluation of results includes the primary dataset from the Shiva Netralaya Centre, IDRiD, and DIARETDB1. The performance of the proposed method is compared against FCM, EFCM, FLICM, and M-FLICM techniques, utilizing metrics such as accuracy in noiseless and noisy conditions and average execution time. The results showcase auspicious performance on both primary and secondary datasets, achieving accuracy rates of 99.03% in noiseless conditions and 93.13% in noisy images, with an average execution time of 16.1 s. The proposed method holds significant potential in medical image analysis and could pave the way for future advancements in automated DR diagnosis and management. RESEARCH HIGHLIGHTS: A novel approach is proposed in the article, integrating a neural network energy-based model into the FCM algorithm to enhance the convergence criteria and the accuracy of automated DR screening tools. By leveraging the microscopic characteristics of retinal images, the proposed method significantly improves the accuracy of lesion segmentation, facilitating early detection and monitoring of DR. The evaluation of the method's performance includes primary datasets from reputable sources such as the Shiva Netralaya Centre, IDRiD, and DIARETDB1, demonstrating its effectiveness in comparison to other techniques (FCM, EFCM, FLICM, and M-FLICM) in terms of accuracy in both noiseless and noisy conditions. It achieves impressive accuracy rates of 99.03% in noiseless conditions and 93.13% in noisy images, with an average execution time of 16.1 s.
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Affiliation(s)
- Huma Naz
- Department of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
| | - Rahul Nijhawan
- Thapar Institute of Engineering and Technology, Patiala, Punjab, India
| | - Neelu Jyothi Ahuja
- Department of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
| | - Tanzila Saba
- Artificial Intelligence and Data Analytics Lab, Prince Sultan University, Riyadh, Saudi Arabia
| | - Faten S Alamri
- Department of Mathematical Sciences, College of Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Amjad Rehman
- Artificial Intelligence and Data Analytics Lab, Prince Sultan University, Riyadh, Saudi Arabia
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20
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Song A, Borkar DS. Advances in Teleophthalmology Screening for Diabetic Retinopathy. Int Ophthalmol Clin 2024; 64:97-113. [PMID: 38146884 DOI: 10.1097/iio.0000000000000505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
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21
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Acharyya M, Moharana B, Jain S, Tandon M. A double-blinded study for quantifiable assessment of the diagnostic accuracy of AI tool "ADVEN-i" in identifying diseased fundus images including diabetic retinopathy on a retrospective data. Indian J Ophthalmol 2024; 72:S46-S52. [PMID: 38131542 PMCID: PMC10833153 DOI: 10.4103/ijo.ijo_3342_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 04/15/2023] [Accepted: 07/28/2023] [Indexed: 12/23/2023] Open
Abstract
PURPOSE To quantifiably assess the diagnostic accuracy of Adven-I, a proprietary artificial intelligence (AI)-driven diagnostic system that automatically detects diseases from fundus images. The purpose is to quantify the performance of Adven-i in differentiating a nonreferable (within normal limits) image from a referable (diseased fundus) image and further segregating diabetic retinopathy (DR) from the rest of the abnormalities (non-DR) encompassing the wide spectrum of abnormal pathologies. The assessment is carried out in comparison to manual reading as the reference gold standard. Adven-i is the only AI system classifying retinal abnormalities into DR and non-DR classes separately, apart from predicting nonreferable fundus, while most existing systems classify fundus images into referable and nonreferable DR. METHODS The double-blinded study was conducted on retrospective data collected over the course of a year in the ophthalmology outpatient department (OPD) at a top Tier II eyecare hospital in Chandigarh, India. Three vitreoretina specialists who were blinded to one another read the images. The ground-truth was generated on the basis of majority agreement among the readers. An arbitrator's decision was regarded final if all three readers disagreed. RESULTS 2261 fundus images were analyzed by Adven-i. The sensitivity and specificity of Adven-i in diagnosing images with abnormalities were 95.12% and 85.77%, respectively, and for segregating DR from rest of the retinal abnormalities were 91.87% and 85.12%, respectively. CONCLUSIONS AND RELEVANCE Adven-i shows definite promise in automated screening for early diagnosis of referable fundus images including DR. Adven-i can be adopted to scale for mass screening in resource-limited settings.
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Affiliation(s)
| | - Bruttendu Moharana
- Department of Ophthalmology, Drishti Eye Hospital, Panchkula, Haryana, India
| | - Sahil Jain
- Department of Vitreo-retina Services, Mirchia Laser Eye Clinic, Chandigarh, India
| | - Manjari Tandon
- Department of Retina and Uvea Services, Mirchia Laser Eye Clinic, Chandigarh, India
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22
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Than J, Sim PY, Muttuvelu D, Ferraz D, Koh V, Kang S, Huemer J. Teleophthalmology and retina: a review of current tools, pathways and services. Int J Retina Vitreous 2023; 9:76. [PMID: 38053188 DOI: 10.1186/s40942-023-00502-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 10/02/2023] [Indexed: 12/07/2023] Open
Abstract
Telemedicine, the use of telecommunication and information technology to deliver healthcare remotely, has evolved beyond recognition since its inception in the 1970s. Advances in telecommunication infrastructure, the advent of the Internet, exponential growth in computing power and associated computer-aided diagnosis, and medical imaging developments have created an environment where telemedicine is more accessible and capable than ever before, particularly in the field of ophthalmology. Ever-increasing global demand for ophthalmic services due to population growth and ageing together with insufficient supply of ophthalmologists requires new models of healthcare provision integrating telemedicine to meet present day challenges, with the recent COVID-19 pandemic providing the catalyst for the widespread adoption and acceptance of teleophthalmology. In this review we discuss the history, present and future application of telemedicine within the field of ophthalmology, and specifically retinal disease. We consider the strengths and limitations of teleophthalmology, its role in screening, community and hospital management of retinal disease, patient and clinician attitudes, and barriers to its adoption.
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Affiliation(s)
- Jonathan Than
- Moorfields Eye Hospital NHS Foundation Trust, 162 City Road, London, UK
| | - Peng Y Sim
- Moorfields Eye Hospital NHS Foundation Trust, 162 City Road, London, UK
| | - Danson Muttuvelu
- Department of Ophthalmology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- MitØje ApS/Danske Speciallaeger Aps, Aarhus, Denmark
| | - Daniel Ferraz
- D'Or Institute for Research and Education (IDOR), São Paulo, Brazil
- Institute of Ophthalmology, University College London, London, UK
| | - Victor Koh
- Department of Ophthalmology, National University Hospital, Singapore, Singapore
| | - Swan Kang
- Moorfields Eye Hospital NHS Foundation Trust, 162 City Road, London, UK
| | - Josef Huemer
- Moorfields Eye Hospital NHS Foundation Trust, 162 City Road, London, UK.
- Department of Ophthalmology and Optometry, Kepler University Hospital, Johannes Kepler University, Linz, Austria.
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23
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Lim CC, Chong C, Tan G, Tan CS, Cheung CY, Wong TY, Cheng CY, Sabanayagam C. A deep learning system for retinal vessel calibre improves cardiovascular risk prediction in Asians with chronic kidney disease. Clin Kidney J 2023; 16:2693-2702. [PMID: 38046002 PMCID: PMC10689182 DOI: 10.1093/ckj/sfad227] [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: 03/29/2023] [Indexed: 12/05/2023] Open
Abstract
Backgraund Cardiovascular disease (CVD) and mortality is elevated in chronic kidney disease (CKD). Retinal vessel calibre in retinal photographs is associated with cardiovascular risk and automated measurements may aid CVD risk prediction. Methods Retrospective cohort study of 860 Chinese, Malay and Indian participants aged 40-80 years with CKD [estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m2] who attended the baseline visit (2004-2011) of the Singapore Epidemiology of Eye Diseases Study. Retinal vessel calibre measurements were obtained by a deep learning system (DLS). Incident CVD [non-fatal acute myocardial infarction (MI) and stroke, and death due to MI, stroke and other CVD] in those who were free of CVD at baseline was ascertained until 31 December 2019. Risk factors (established, kidney, and retinal features) were examined using Cox proportional hazards regression models. Model performance was assessed for discrimination, fit, and net reclassification improvement (NRI). Results Incident CVD occurred in 289 (33.6%) over mean follow-up of 9.3 (4.3) years. After adjusting for established cardiovascular risk factors, eGFR [adjusted HR 0.98 (95% CI: 0.97-0.99)] and retinal arteriolar narrowing [adjusted HR 1.40 (95% CI: 1.17-1.68)], but not venular dilation, were independent predictors for CVD in CKD. The addition of eGFR and retinal features to established cardiovascular risk factors improved model discrimination with significantly better fit and better risk prediction according to the low (<15%), intermediate (15-29.9%), and high (30% or more) risk categories (NRI 5.8%), and with higher risk thresholds (NRI 12.7%). Conclusions Retinal vessel calibre measurements by DLS were significantly associated with incident CVD independent of established CVD risk factors. Addition of kidney function and retinal vessel calibre parameters may improve CVD risk prediction among Asians with CKD.
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Affiliation(s)
| | - Crystal Chong
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore
| | - Gavin Tan
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore
- Eye-ACP, Duke-NUS Medical School, Singapore
| | - Chieh Suai Tan
- Department of Renal Medicine, Singapore General Hospital, Singapore
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Tien Y Wong
- Department of Renal Medicine, Singapore General Hospital, Singapore
| | - Ching Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore
- Eye-ACP, Duke-NUS Medical School, Singapore
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore
- Eye-ACP, Duke-NUS Medical School, Singapore
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24
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Daich Varela M, Sen S, De Guimaraes TAC, Kabiri N, Pontikos N, Balaskas K, Michaelides M. Artificial intelligence in retinal disease: clinical application, challenges, and future directions. Graefes Arch Clin Exp Ophthalmol 2023; 261:3283-3297. [PMID: 37160501 PMCID: PMC10169139 DOI: 10.1007/s00417-023-06052-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/20/2023] [Accepted: 03/24/2023] [Indexed: 05/11/2023] Open
Abstract
Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans.
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Affiliation(s)
- Malena Daich Varela
- UCL Institute of Ophthalmology, London, UK
- Moorfields Eye Hospital, London, UK
| | | | | | | | - Nikolas Pontikos
- UCL Institute of Ophthalmology, London, UK
- Moorfields Eye Hospital, London, UK
| | | | - Michel Michaelides
- UCL Institute of Ophthalmology, London, UK.
- Moorfields Eye Hospital, London, UK.
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25
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Hasan SU, Siddiqui MAR. Diagnostic accuracy of smartphone-based artificial intelligence systems for detecting diabetic retinopathy: A systematic review and meta-analysis. Diabetes Res Clin Pract 2023; 205:110943. [PMID: 37805002 DOI: 10.1016/j.diabres.2023.110943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 07/28/2023] [Accepted: 10/05/2023] [Indexed: 10/09/2023]
Abstract
AIMS Diabetic retinopathy (DR) is a major cause of blindness globally, early detection is critical to prevent vision loss. Traditional screening that, rely on human experts are, however, costly, and time-consuming. The purpose of this systematic review is to assess the diagnostic accuracy of smartphone-based artificial intelligence(AI) systems for DR detection. METHODS Literature review was conducted on MEDLINE, Embase, Scopus, CINAHL Plus, and Cochrane from inception to December 2022. We included diagnostic test accuracy studies evaluating the use of smartphone-based AI algorithms for DR screening in patients with diabetes, with expert human grader as the reference standard. Random-effects model was used to pool sensitivity and specificity. Any DR(ADR) and referable DR(RDR) were analyzed separately. RESULTS Out of 968 identified articles, six diagnostic test accuracy studies met our inclusion criteria, comprising 3,931 patients. Four of these studies used the Medios AI algorithm. The pooled sensitivity and specificity for diagnosis of ADR were 88 % and 91.5 % respectively and for diagnosis of RDR were 98.2 % and 81.2 % respectively. The overall risk of bias across the studies was low. CONCLUSIONS Smartphone-based AI algorithms show high diagnostic accuracy for detecting DR. However, more high-quality comparative studies are needed to evaluate the effectiveness in real-world clinical settings.
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Affiliation(s)
- S Umar Hasan
- Department of Ophthalmology and Visual Sciences, Aga Khan University Hospital, National Stadium Road, Karachi, Pakistan
| | - M A Rehman Siddiqui
- Department of Ophthalmology and Visual Sciences, Aga Khan University Hospital, National Stadium Road, Karachi, Pakistan.
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26
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Gholami S, Lim JI, Leng T, Ong SSY, Thompson AC, Alam MN. Federated learning for diagnosis of age-related macular degeneration. Front Med (Lausanne) 2023; 10:1259017. [PMID: 37901412 PMCID: PMC10613107 DOI: 10.3389/fmed.2023.1259017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 09/25/2023] [Indexed: 10/31/2023] Open
Abstract
This paper presents a federated learning (FL) approach to train deep learning models for classifying age-related macular degeneration (AMD) using optical coherence tomography image data. We employ the use of residual network and vision transformer encoders for the normal vs. AMD binary classification, integrating four unique domain adaptation techniques to address domain shift issues caused by heterogeneous data distribution in different institutions. Experimental results indicate that FL strategies can achieve competitive performance similar to centralized models even though each local model has access to a portion of the training data. Notably, the Adaptive Personalization FL strategy stood out in our FL evaluations, consistently delivering high performance across all tests due to its additional local model. Furthermore, the study provides valuable insights into the efficacy of simpler architectures in image classification tasks, particularly in scenarios where data privacy and decentralization are critical using both encoders. It suggests future exploration into deeper models and other FL strategies for a more nuanced understanding of these models' performance. Data and code are available at https://github.com/QIAIUNCC/FL_UNCC_QIAI.
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Affiliation(s)
- Sina Gholami
- Department of Electrical Engineering, University of North Carolina at Charlotte, Charlotte, NC, United States
| | - Jennifer I. Lim
- Department of Ophthalmology and Visual Science, University of Illinois at Chicago, Chicago, IL, United States
| | - Theodore Leng
- Department of Ophthalmology, School of Medicine, Stanford University, Stanford, CA, United States
| | - Sally Shin Yee Ong
- Department of Surgical Ophthalmology, Atrium-Health Wake Forest Baptist, Winston-Salem, NC, United States
| | - Atalie Carina Thompson
- Department of Surgical Ophthalmology, Atrium-Health Wake Forest Baptist, Winston-Salem, NC, United States
| | - Minhaj Nur Alam
- Department of Electrical Engineering, University of North Carolina at Charlotte, Charlotte, NC, United States
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27
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Vaughan N. Review of smartphone funduscopy for diabetic retinopathy screening. Surv Ophthalmol 2023:S0039-6257(23)00132-7. [PMID: 37806567 DOI: 10.1016/j.survophthal.2023.10.006] [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: 06/23/2023] [Revised: 09/23/2023] [Accepted: 10/03/2023] [Indexed: 10/10/2023]
Abstract
I detail advances in funduscopy diagnostic systems integrating smartphones. Smartphone funduscopy devices are comprised of lens devices connecting with smartphones and software applications to be used for mobile retinal image capturing and diagnosis of diabetic retinopathy. This is particularly beneficial to automate and mobilize retinopathy screening techniques and methods in remote and rural areas as those diabetes patients are often not meeting the required regular screening for diabetic retinopathy. Smartphone retinal image grading systems enable retinopathy to be screened remotely as teleophthalmology or as a stand-alone point-of-care-testing system. Smartphone funduscopy aims to avoid the need for patients to be seen by expert ophthalmologists, which can reduce patient travel, time taken for images to be processed, appointment backlog, health service overhead costs, and the workload burden for expert ophthalmologists.
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Affiliation(s)
- Neil Vaughan
- Exeter Centre of Excellence for Diabetes (ExCEeD), University of Exeter, Exeter, UK; Faculty of Health and Life Sciences (HLS), University of Exeter, Exeter, UK; Royal Academy of Engineering (RAEng), London, UK; NIHR Exeter Biomedical Research Centre, Exeter, UK.
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28
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Chen Y, Zhao W, Yi S, Liu J. The diagnostic performance of machine learning based on resting-state functional magnetic resonance imaging data for major depressive disorders: a systematic review and meta-analysis. Front Neurosci 2023; 17:1174080. [PMID: 37811326 PMCID: PMC10559726 DOI: 10.3389/fnins.2023.1174080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 08/11/2023] [Indexed: 10/10/2023] Open
Abstract
Objective Machine learning (ML) has been widely used to detect and evaluate major depressive disorder (MDD) using neuroimaging data, i.e., resting-state functional magnetic resonance imaging (rs-fMRI). However, the diagnostic efficiency is unknown. The aim of the study is to conduct an updated meta-analysis to evaluate the diagnostic performance of ML based on rs-fMRI data for MDD. Methods English databases were searched for relevant studies. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to assess the methodological quality of the included studies. A random-effects meta-analytic model was implemented to investigate the diagnostic efficiency, including sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Regression meta-analysis and subgroup analysis were performed to investigate the cause of heterogeneity. Results Thirty-one studies were included in this meta-analysis. The pooled sensitivity, specificity, DOR, and AUC with 95% confidence intervals were 0.80 (0.75, 0.83), 0.83 (0.74, 0.82), 14.00 (9, 22.00), and 0.86 (0.83, 0.89), respectively. Substantial heterogeneity was observed among the studies included. The meta-regression showed that the leave-one-out cross-validation (loocv) (sensitivity: p < 0.01, specificity: p < 0.001), graph theory (sensitivity: p < 0.05, specificity: p < 0.01), n > 100 (sensitivity: p < 0.001, specificity: p < 0.001), simens equipment (sensitivity: p < 0.01, specificity: p < 0.001), 3.0T field strength (Sensitivity: p < 0.001, specificity: p = 0.04), and Beck Depression Inventory (BDI) (sensitivity: p = 0.04, specificity: p = 0.06) might be the sources of heterogeneity. Furthermore, the subgroup analysis showed that the sample size (n > 100: sensitivity: 0.71, specificity: 0.72, n < 100: sensitivity: 0.81, specificity: 0.79), the different levels of disease evaluated by the Hamilton Depression Rating Scale (HDRS/HAMD) (mild vs. moderate vs. severe: sensitivity: 0.52 vs. 0.86 vs. 0.89, specificity: 0.62 vs. 0.78 vs. 0.82, respectively), the depression scales in patients with comparable levels of severity. (BDI vs. HDRS/HAMD: sensitivity: 0.86 vs. 0.87, specificity: 0.78 vs. 0.80, respectively), and the features (graph vs. functional connectivity: sensitivity: 0.84 vs. 0.86, specificity: 0.76 vs. 0.78, respectively) selected might be the causes of heterogeneity. Conclusion ML showed high accuracy for the automatic diagnosis of MDD. Future studies are warranted to promote the potential use of these classification algorithms in clinical settings.
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Affiliation(s)
- Yanjing Chen
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Wei Zhao
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan, China
| | - Sijie Yi
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jun Liu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan, China
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29
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Uy H, Fielding C, Hohlfeld A, Ochodo E, Opare A, Mukonda E, Minnies D, Engel ME. Diagnostic test accuracy of artificial intelligence in screening for referable diabetic retinopathy in real-world settings: A systematic review and meta-analysis. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0002160. [PMID: 37729122 PMCID: PMC10511145 DOI: 10.1371/journal.pgph.0002160] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 08/23/2023] [Indexed: 09/22/2023]
Abstract
Retrospective studies on artificial intelligence (AI) in screening for diabetic retinopathy (DR) have shown promising results in addressing the mismatch between the capacity to implement DR screening and increasing DR incidence. This review sought to evaluate the diagnostic test accuracy (DTA) of AI in screening for referable diabetic retinopathy (RDR) in real-world settings. We searched CENTRAL, PubMed, CINAHL, Scopus, and Web of Science on 9 February 2023. We included prospective DTA studies assessing AI against trained human graders (HGs) in screening for RDR in patients with diabetes. Two reviewers independently extracted data and assessed methodological quality against QUADAS-2 criteria. We used the hierarchical summary receiver operating characteristics (HSROC) model to pool estimates of sensitivity and specificity and, forest plots and SROC plots to visually examine heterogeneity in accuracy estimates. From our initial search results of 3899 studies, we included 15 studies comprising 17 datasets. Meta-analyses revealed a sensitivity of 95.33% (95%CI: 90.60-100%) and specificity of 92.01% (95%CI: 87.61-96.42%) for patient-level analysis (10 datasets, N = 45,785) while, for the eye-level analysis, sensitivity was 91.24% (95%CI: 79.15-100%) and specificity, 93.90% (95%CI: 90.63-97.16%) (7 datasets, N = 15,390). Subgroup analyses did not provide variations in the diagnostic accuracy of country classification and DR classification criteria. However, a moderate increase was observed in diagnostic accuracy in the primary-level healthcare settings: sensitivity of 99.35% (95%CI: 96.85-100%), specificity of 93.72% (95%CI: 88.83-98.61%) and, a minimal decrease in the tertiary-level healthcare settings: sensitivity of 94.71% (95%CI: 89.00-100%), specificity of 90.88% (95%CI: 83.22-98.53%). Sensitivity analyses did not show any variations in studies that included diabetic macular edema in the RDR definition, nor studies with ≥3 HGs. This review provides evidence, for the first time from prospective studies, for the effectiveness of AI in screening for RDR in real-world settings. The results may serve to strengthen existing guidelines to improve current practices.
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Affiliation(s)
- Holijah Uy
- Community Eye Health Institute, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Christopher Fielding
- Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Ameer Hohlfeld
- South African Medical Research Council, Cape Town, South Africa
| | - Eleanor Ochodo
- Centre for Global Health Research, Kenya Medical Research Institute, Nairobi, Kenya
- Centre for Evidence-Based Health Care, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Abraham Opare
- Community Eye Health Institute, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Elton Mukonda
- Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Deon Minnies
- Community Eye Health Institute, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Mark E. Engel
- South African Medical Research Council, Cape Town, South Africa
- Department of Medicine, University of Cape Town, Cape Town, South Africa
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30
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Tan TF, Thirunavukarasu AJ, Jin L, Lim J, Poh S, Teo ZL, Ang M, Chan RVP, Ong J, Turner A, Karlström J, Wong TY, Stern J, Ting DSW. Artificial intelligence and digital health in global eye health: opportunities and challenges. Lancet Glob Health 2023; 11:e1432-e1443. [PMID: 37591589 DOI: 10.1016/s2214-109x(23)00323-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 06/26/2023] [Accepted: 07/04/2023] [Indexed: 08/19/2023]
Abstract
Global eye health is defined as the degree to which vision, ocular health, and function are maximised worldwide, thereby optimising overall wellbeing and quality of life. Improving eye health is a global priority as a key to unlocking human potential by reducing the morbidity burden of disease, increasing productivity, and supporting access to education. Although extraordinary progress fuelled by global eye health initiatives has been made over the last decade, there remain substantial challenges impeding further progress. The accelerated development of digital health and artificial intelligence (AI) applications provides an opportunity to transform eye health, from facilitating and increasing access to eye care to supporting clinical decision making with an objective, data-driven approach. Here, we explore the opportunities and challenges presented by digital health and AI in global eye health and describe how these technologies could be leveraged to improve global eye health. AI, telehealth, and emerging technologies have great potential, but require specific work to overcome barriers to implementation. We suggest that a global digital eye health task force could facilitate coordination of funding, infrastructural development, and democratisation of AI and digital health to drive progress forwards in this domain.
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Affiliation(s)
- Ting Fang Tan
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore; Singapore National Eye Centre, Singapore General Hospital, Singapore
| | - Arun J Thirunavukarasu
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore; Corpus Christi College, University of Cambridge, Cambridge, UK; School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Liyuan Jin
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore; Duke-NUS Medical School, National University of Singapore, Singapore
| | - Joshua Lim
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore; Singapore National Eye Centre, Singapore General Hospital, Singapore
| | - Stanley Poh
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore; Singapore National Eye Centre, Singapore General Hospital, Singapore
| | - Zhen Ling Teo
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore; Singapore National Eye Centre, Singapore General Hospital, Singapore
| | - Marcus Ang
- Singapore National Eye Centre, Singapore General Hospital, Singapore; Duke-NUS Medical School, National University of Singapore, Singapore
| | - R V Paul Chan
- Illinois Eye and Ear Infirmary, University of Illinois College of Medicine, Urbana-Champaign, IL, USA
| | - Jasmine Ong
- Pharmacy Department, Singapore General Hospital, Singapore
| | - Angus Turner
- Lions Eye Institute, University of Western Australia, Nedlands, WA, Australia
| | - Jonas Karlström
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Tien Yin Wong
- Singapore National Eye Centre, Singapore General Hospital, Singapore; Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Jude Stern
- The International Agency for the Prevention of Blindness, London, UK
| | - Daniel Shu-Wei Ting
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore; Singapore National Eye Centre, Singapore General Hospital, Singapore; Duke-NUS Medical School, National University of Singapore, Singapore.
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31
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Spital G, Faatz H. Diabetic Retinopathy - a Common Disease. Klin Monbl Augenheilkd 2023; 240:1060-1070. [PMID: 37666252 DOI: 10.1055/a-2108-6758] [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: 09/06/2023]
Abstract
Diabetic retinopathy (DR) is one of the most common complications of diabetes mellitus and one of the leading causes of visual impairment in working age individuals in the western world. The treatment of DR depends on its severity, so it is of great importance to detect patients as early as possible, in order to initiate early treatment and preserve vision. Despite currently insufficient screening participation, patients with diabetes already visit ophthalmological practices and clinics above average. Their medical care, including DR diagnostics and treatment has been making up an increasing proportion of ophthalmic activity for years. Since the prevalence of diabetes is increasing dramatically worldwide and a further increase is also predicted for Germany, the challenge for ophthalmologists is likely to grow considerably. As the same time, the diagnostic possibilities for differentiating DR and the therapeutic measures, especially with IVOM therapy, are becoming more and more complex, which increases the time burden in everyday clinical practice. The hope to avoid healthcare deficits and to further improve screening rates and visual acuity prognosis in patients with DR is based, among other things, on camera-assisted screening supported by artificial intelligence. Better diabetes management to reduce the prevalence of DR, as well as longer-acting drugs to treat DR, could also improve the care and help reduce the burden on ophthalmology practices.
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Affiliation(s)
- Georg Spital
- Augenzentrum am St. Franziskus-Hospital, Münster, Deutschland
| | - Henrik Faatz
- Augenzentrum am St. Franziskus-Hospital, Münster, Deutschland
- Achim-Wessing-Institut für Ophthalmologische Bildgebung, Universität Essen, Deutschland
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Oganov AC, Seddon I, Jabbehdari S, Uner OE, Fonoudi H, Yazdanpanah G, Outani O, Arevalo JF. Artificial intelligence in retinal image analysis: Development, advances, and challenges. Surv Ophthalmol 2023; 68:905-919. [PMID: 37116544 DOI: 10.1016/j.survophthal.2023.04.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 04/20/2023] [Accepted: 04/24/2023] [Indexed: 04/30/2023]
Abstract
Modern advances in diagnostic technologies offer the potential for unprecedented insight into ophthalmic conditions relating to the retina. We discuss the current landscape of artificial intelligence in retina with respect to screening, diagnosis, and monitoring of retinal pathologies such as diabetic retinopathy, diabetic macular edema, central serous chorioretinopathy, and age-related macular degeneration. We review the methods used in these models and evaluate their performance in both research and clinical contexts and discuss potential future directions for investigation, use of multiple imaging modalities in artificial intelligence algorithms, and challenges in the application of artificial intelligence in retinal pathologies.
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Affiliation(s)
- Anthony C Oganov
- Department of Ophthalmology, Renaissance School of Medicine, Stony Brook, NY, USA
| | - Ian Seddon
- College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, USA
| | - Sayena Jabbehdari
- Jones Eye Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
| | - Ogul E Uner
- Casey Eye Institute, Department of Ophthalmology, Oregon Health and Science University, Portland, OR, USA
| | - Hossein Fonoudi
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Iranshahr University of Medical Sciences, Iranshahr, Sistan and Baluchestan, Iran
| | - Ghasem Yazdanpanah
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, USA
| | - Oumaima Outani
- Faculty of Medicine and Pharmacy of Rabat, Mohammed 5 University, Rabat, Rabat, Morocco
| | - J Fernando Arevalo
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Wroblewski JJ, Sanchez-Buenfil E, Inciarte M, Berdia J, Blake L, Wroblewski S, Patti A, Suter G, Sanborn GE. Diabetic Retinopathy Screening Using Smartphone-Based Fundus Photography and Deep-Learning Artificial Intelligence in the Yucatan Peninsula: A Field Study. J Diabetes Sci Technol 2023:19322968231194644. [PMID: 37641576 DOI: 10.1177/19322968231194644] [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] [Indexed: 08/31/2023]
Abstract
BACKGROUND To compare the performance of Medios (offline) and EyeArt (online) artificial intelligence (AI) algorithms for detecting diabetic retinopathy (DR) on images captured using fundus-on-smartphone photography in a remote outreach field setting. METHODS In June, 2019 in the Yucatan Peninsula, 248 patients, many of whom had chronic visual impairment, were screened for DR using two portable Remidio fundus-on-phone cameras, and 2130 images obtained were analyzed, retrospectively, by Medios and EyeArt. Screening performance metrics also were determined retrospectively using masked image analysis combined with clinical examination results as the reference standard. RESULTS A total of 129 patients were determined to have some level of DR; 119 patients had no DR. Medios was capable of evaluating every patient with a sensitivity (95% confidence intervals [CIs]) of 94% (88%-97%) and specificity of 94% (88%-98%). Owing primarily to photographer error, EyeArt evaluated 156 patients with a sensitivity of 94% (86%-98%) and specificity of 86% (77%-93%). In a head-to-head comparison of 110 patients, the sensitivities of Medios and EyeArt were 99% (93%-100%) and 95% (87%-99%). The specificities for both were 88% (73%-97%). CONCLUSIONS Medios and EyeArt AI algorithms demonstrated high levels of sensitivity and specificity for detecting DR when applied in this real-world field setting. Both programs should be considered in remote, large-scale DR screening campaigns where immediate results are desirable, and in the case of EyeArt, online access is possible.
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Affiliation(s)
- John J Wroblewski
- Retina Care International, Hagerstown, MD, USA
- Cumberland Valley Retina Consultants, Hagerstown, MD, USA
| | | | | | - Jay Berdia
- Cumberland Valley Retina Consultants, Hagerstown, MD, USA
| | - Lewis Blake
- Department of Applied Mathematics and Statistics, Colorado School of Mines, Golden, CO, USA
| | | | | | - Gretchen Suter
- Cumberland Valley Retina Consultants, Hagerstown, MD, USA
| | - George E Sanborn
- Department of Ophthalmology, Virginia Commonwealth University, Richmond, VA, USA
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Prayogo ME, Zaharo AF, Damayanti NNR, Widyaputri F, Thobari JA, Susanti VY, Sasongko MB. Accuracy of Low-Cost, Smartphone-Based Retinal Photography for Diabetic Retinopathy Screening: A Systematic Review. Clin Ophthalmol 2023; 17:2459-2470. [PMID: 37614846 PMCID: PMC10443682 DOI: 10.2147/opth.s416422] [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: 05/03/2023] [Accepted: 07/21/2023] [Indexed: 08/25/2023] Open
Abstract
Purpose Diabetic retinopathy (DR) is a leading cause of blindness. Early DR screening is essential, but the infrastructure can be less affordable in low resource countries. This study aims to review the accuracy of low-cost smartphone-based fundus cameras for DR screening in adult patients with diabetes. Methods We performed a systematic literature search to find studies that reported the sensitivity and specificity of low-cost smartphone-based devices for fundus photography in adult patients with diabetes. We searched three databases (MEDLINE, Google Scholar, Scopus) and one register (Cochrane CENTRAL). We presented the accuracy values by grouping the diagnosis into three: any DR, referrable DR, and diabetic macular oedema (DMO). Risk of bias and applicability of the studies were assessed using QUADAS-2. Results Five out of 294 retrieved records were included with a total of six smartphone-based devices reviewed. All of the reference diagnostic methods used in the included studies were either indirect ophthalmoscopy or slit-lamp examinations and all smartphone-based devices' imaging protocols used mydriatic drops. The reported sensitivity and specificity for any DR were 52-92.2% and 73.3-99%; for referral DR were 21-91.4% and 64.9-100%; and for DMO were 29.4-81% and 95-100%, respectively. Conclusion Sensitivity available low-cost smartphone-based devices for DR screening were acceptable and their specificity particularly for detecting referrable DR and DMO were considerably good. These findings support their potential utilization for DR screening in a low resources setting.
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Affiliation(s)
- Mohammad Eko Prayogo
- Department of Ophthalmology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada – Sardjito Eye Center, Dr. Sardjito General Hospital, Yogyakarta, Indonesia
- Department of Ophthalmology, Universitas Gadjah Mada Academic Hospital, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Alfia Fatma Zaharo
- Department of Ophthalmology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada – Sardjito Eye Center, Dr. Sardjito General Hospital, Yogyakarta, Indonesia
| | - Novandriati Nur Rizky Damayanti
- Department of Ophthalmology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada – Sardjito Eye Center, Dr. Sardjito General Hospital, Yogyakarta, Indonesia
| | - Felicia Widyaputri
- Department of Ophthalmology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada – Sardjito Eye Center, Dr. Sardjito General Hospital, Yogyakarta, Indonesia
| | - Jarir At Thobari
- Department of Pharmacology and Therapy, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
- Clinical Epidemiology and Biostatistics Unit (CE&BU), Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Vina Yanti Susanti
- Department of Internal Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Muhammad Bayu Sasongko
- Department of Ophthalmology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada – Sardjito Eye Center, Dr. Sardjito General Hospital, Yogyakarta, Indonesia
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Jacoba CMP, Doan D, Salongcay RP, Aquino LAC, Silva JPY, Salva CMG, Zhang D, Alog GP, Zhang K, Locaylocay KLRB, Saunar AV, Ashraf M, Sun JK, Peto T, Aiello LP, Silva PS. Performance of Automated Machine Learning for Diabetic Retinopathy Image Classification from Multi-field Handheld Retinal Images. Ophthalmol Retina 2023; 7:703-712. [PMID: 36924893 DOI: 10.1016/j.oret.2023.03.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 02/07/2023] [Accepted: 03/01/2023] [Indexed: 03/17/2023]
Abstract
PURPOSE To create and validate code-free automated deep learning models (AutoML) for diabetic retinopathy (DR) classification from handheld retinal images. DESIGN Prospective development and validation of AutoML models for DR image classification. PARTICIPANTS A total of 17 829 deidentified retinal images from 3566 eyes with diabetes, acquired using handheld retinal cameras in a community-based DR screening program. METHODS AutoML models were generated based on previously acquired 5-field (macula-centered, disc-centered, superior, inferior, and temporal macula) handheld retinal images. Each individual image was labeled using the International DR and diabetic macular edema (DME) Classification Scale by 4 certified graders at a centralized reading center under oversight by a senior retina specialist. Images for model development were split 8-1-1 for training, optimization, and testing to detect referable DR ([refDR], defined as moderate nonproliferative DR or worse or any level of DME). Internal validation was performed using a published image set from the same patient population (N = 450 images from 225 eyes). External validation was performed using a publicly available retinal imaging data set from the Asia Pacific Tele-Ophthalmology Society (N = 3662 images). MAIN OUTCOME MEASURES Area under the precision-recall curve (AUPRC), sensitivity (SN), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), accuracy, and F1 scores. RESULTS Referable DR was present in 17.3%, 39.1%, and 48.0% of the training set, internal validation, and external validation sets, respectively. The model's AUPRC was 0.995 with a precision and recall of 97% using a score threshold of 0.5. Internal validation showed that SN, SP, PPV, NPV, accuracy, and F1 scores were 0.96 (95% confidence interval [CI], 0.884-0.99), 0.98 (95% CI, 0.937-0.995), 0.96 (95% CI, 0.884-0.99), 0.98 (95% CI, 0.937-0.995), 0.97, and 0.96, respectively. External validation showed that SN, SP, PPV, NPV, accuracy, and F1 scores were 0.94 (95% CI, 0.929-0.951), 0.97 (95% CI, 0.957-0.974), 0.96 (95% CI, 0.952-0.971), 0.95 (95% CI, 0.935-0.956), 0.97, and 0.96, respectively. CONCLUSIONS This study demonstrates the accuracy and feasibility of code-free AutoML models for identifying refDR developed using handheld retinal imaging in a community-based screening program. Potentially, the use of AutoML may increase access to machine learning models that may be adapted for specific programs that are guided by the clinical need to rapidly address disparities in health care delivery. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found after the references.
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Affiliation(s)
- Cris Martin P Jacoba
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts; Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts
| | - Duy Doan
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts
| | - Recivall P Salongcay
- Philippine Eye Research Institute, University of the Philippines, Manila, Philippines; Centre for Public Health, Queen's University Belfast, United Kingdom; Eyes and Vision Institute, the Medical City, Pasig City, Philippines
| | - Lizzie Anne C Aquino
- Philippine Eye Research Institute, University of the Philippines, Manila, Philippines
| | - Joseph Paolo Y Silva
- Philippine Eye Research Institute, University of the Philippines, Manila, Philippines
| | | | - Dean Zhang
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts
| | - Glenn P Alog
- Philippine Eye Research Institute, University of the Philippines, Manila, Philippines; Eyes and Vision Institute, the Medical City, Pasig City, Philippines
| | - Kexin Zhang
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts
| | - Kaye Lani Rea B Locaylocay
- Philippine Eye Research Institute, University of the Philippines, Manila, Philippines; Eyes and Vision Institute, the Medical City, Pasig City, Philippines
| | - Aileen V Saunar
- Philippine Eye Research Institute, University of the Philippines, Manila, Philippines; Eyes and Vision Institute, the Medical City, Pasig City, Philippines
| | - Mohamed Ashraf
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts; Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts
| | - Jennifer K Sun
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts; Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts
| | - Tunde Peto
- Centre for Public Health, Queen's University Belfast, United Kingdom
| | - Lloyd Paul Aiello
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts; Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts
| | - Paolo S Silva
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts; Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts; Philippine Eye Research Institute, University of the Philippines, Manila, Philippines; Eyes and Vision Institute, the Medical City, Pasig City, Philippines.
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Cleland CR, Rwiza J, Evans JR, Gordon I, MacLeod D, Burton MJ, Bascaran C. Artificial intelligence for diabetic retinopathy in low-income and middle-income countries: a scoping review. BMJ Open Diabetes Res Care 2023; 11:e003424. [PMID: 37532460 PMCID: PMC10401245 DOI: 10.1136/bmjdrc-2023-003424] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 07/11/2023] [Indexed: 08/04/2023] Open
Abstract
Diabetic retinopathy (DR) is a leading cause of blindness globally. There is growing evidence to support the use of artificial intelligence (AI) in diabetic eye care, particularly for screening populations at risk of sight loss from DR in low-income and middle-income countries (LMICs) where resources are most stretched. However, implementation into clinical practice remains limited. We conducted a scoping review to identify what AI tools have been used for DR in LMICs and to report their performance and relevant characteristics. 81 articles were included. The reported sensitivities and specificities were generally high providing evidence to support use in clinical practice. However, the majority of studies focused on sensitivity and specificity only and there was limited information on cost, regulatory approvals and whether the use of AI improved health outcomes. Further research that goes beyond reporting sensitivities and specificities is needed prior to wider implementation.
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Affiliation(s)
- Charles R Cleland
- International Centre for Eye Health, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Eye Department, Kilimanjaro Christian Medical Centre, Moshi, United Republic of Tanzania
| | - Justus Rwiza
- Eye Department, Kilimanjaro Christian Medical Centre, Moshi, United Republic of Tanzania
| | - Jennifer R Evans
- International Centre for Eye Health, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Iris Gordon
- International Centre for Eye Health, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - David MacLeod
- Tropical Epidemiology Group, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Matthew J Burton
- International Centre for Eye Health, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
- National Institute for Health Research Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Covadonga Bascaran
- International Centre for Eye Health, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
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Penha FM, Priotto BM, Hennig F, Przysiezny B, Wiethorn BA, Orsi J, Nagel IBF, Wiggers B, Stuchi JA, Lencione D, de Souza Prado PV, Yamanaka F, Lojudice F, Malerbi FK. Single retinal image for diabetic retinopathy screening: performance of a handheld device with embedded artificial intelligence. Int J Retina Vitreous 2023; 9:41. [PMID: 37430345 DOI: 10.1186/s40942-023-00477-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 06/23/2023] [Indexed: 07/12/2023] Open
Abstract
BACKGROUND Diabetic retinopathy (DR) is a leading cause of blindness. Our objective was to evaluate the performance of an artificial intelligence (AI) system integrated into a handheld smartphone-based retinal camera for DR screening using a single retinal image per eye. METHODS Images were obtained from individuals with diabetes during a mass screening program for DR in Blumenau, Southern Brazil, conducted by trained operators. Automatic analysis was conducted using an AI system (EyerMaps™, Phelcom Technologies LLC, Boston, USA) with one macula-centered, 45-degree field of view retinal image per eye. The results were compared to the assessment by a retinal specialist, considered as the ground truth, using two images per eye. Patients with ungradable images were excluded from the analysis. RESULTS A total of 686 individuals (average age 59.2 ± 13.3 years, 56.7% women, diabetes duration 12.1 ± 9.4 years) were included in the analysis. The rates of insulin use, daily glycemic monitoring, and systemic hypertension treatment were 68.4%, 70.2%, and 70.2%, respectively. Although 97.3% of patients were aware of the risk of blindness associated with diabetes, more than half of them underwent their first retinal examination during the event. The majority (82.5%) relied exclusively on the public health system. Approximately 43.4% of individuals were either illiterate or had not completed elementary school. DR classification based on the ground truth was as follows: absent or nonproliferative mild DR 86.9%, more than mild (mtm) DR 13.1%. The AI system achieved sensitivity, specificity, positive predictive value, and negative predictive value percentages (95% CI) for mtmDR as follows: 93.6% (87.8-97.2), 71.7% (67.8-75.4), 42.7% (39.3-46.2), and 98.0% (96.2-98.9), respectively. The area under the ROC curve was 86.4%. CONCLUSION The portable retinal camera combined with AI demonstrated high sensitivity for DR screening using only one image per eye, offering a simpler protocol compared to the traditional approach of two images per eye. Simplifying the DR screening process could enhance adherence rates and overall program coverage.
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Affiliation(s)
- Fernando Marcondes Penha
- Fundacao Universidade Regional de Blumenau, Rua Antonio Veiga 140, Blumenau, 89030-903, SC, Brazil.
- Botelho Hospital da Visão, Rua 2 de Setembro, 2958, Blumenau, 89052-504, SC, Brazil.
| | - Bruna Milene Priotto
- Fundacao Universidade Regional de Blumenau, Rua Antonio Veiga 140, Blumenau, 89030-903, SC, Brazil
| | - Francini Hennig
- Fundacao Universidade Regional de Blumenau, Rua Antonio Veiga 140, Blumenau, 89030-903, SC, Brazil
| | - Bernardo Przysiezny
- Fundacao Universidade Regional de Blumenau, Rua Antonio Veiga 140, Blumenau, 89030-903, SC, Brazil
| | - Bruno Antunes Wiethorn
- Fundacao Universidade Regional de Blumenau, Rua Antonio Veiga 140, Blumenau, 89030-903, SC, Brazil
| | - Julia Orsi
- Fundacao Universidade Regional de Blumenau, Rua Antonio Veiga 140, Blumenau, 89030-903, SC, Brazil
| | | | - Brenda Wiggers
- Fundacao Universidade Regional de Blumenau, Rua Antonio Veiga 140, Blumenau, 89030-903, SC, Brazil
| | | | | | | | | | - Fernando Lojudice
- Bayer Healthcare - Brazil, São Paulo, SP, Brazil
- Cell and Molecular Theraphy Center (NUCEL), School of Medicine, University of São Paulo, São Paulo, SP, Brazil
| | - Fernando Korn Malerbi
- Department of Ophthalmology, Federal University of São Paulo (UNIFESP), São Paulo, SP, Brazil
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Teoh CS, Wong KH, Xiao D, Wong HC, Zhao P, Chan HW, Yuen YS, Naing T, Yogesan K, Koh VTC. Variability in Grading Diabetic Retinopathy Using Retinal Photography and Its Comparison with an Automated Deep Learning Diabetic Retinopathy Screening Software. Healthcare (Basel) 2023; 11:1697. [PMID: 37372815 DOI: 10.3390/healthcare11121697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 06/05/2023] [Accepted: 06/07/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND Diabetic retinopathy (DR) screening using colour retinal photographs is cost-effective and time-efficient. In real-world clinical settings, DR severity is frequently graded by individuals of different expertise levels. We aim to determine the agreement in DR severity grading between human graders of varying expertise and an automated deep learning DR screening software (ADLS). METHODS Using the International Clinical DR Disease Severity Scale, two hundred macula-centred fundus photographs were graded by retinal specialists, ophthalmology residents, family medicine physicians, medical students, and the ADLS. Based on referral urgency, referral grading was divided into no referral, non-urgent referral, and urgent referral to an ophthalmologist. Inter-observer and intra-group variations were analysed using Gwet's agreement coefficient, and the performance of ADLS was evaluated using sensitivity and specificity. RESULTS The agreement coefficient for inter-observer and intra-group variability ranged from fair to very good, and moderate to good, respectively. The ADLS showed a high area under curve of 0.879, 0.714, and 0.836 for non-referable DR, non-urgent referable DR, and urgent referable DR, respectively, with varying sensitivity and specificity values. CONCLUSION Inter-observer and intra-group agreements among human graders vary widely, but ADLS is a reliable and reasonably sensitive tool for mass screening to detect referable DR and urgent referable DR.
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Affiliation(s)
- Chin Sheng Teoh
- Department of Ophthalmology, National University Health System, Singapore 119228, Singapore
| | - Kah Hie Wong
- Department of Ophthalmology, National University Health System, Singapore 119228, Singapore
| | - Di Xiao
- Commonwealth Scientific and Industrial Research Organisation, Urrbrae 5064, Australia
| | - Hung Chew Wong
- Medicine Biostatistics Unit, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - Paul Zhao
- Department of Ophthalmology, National University Health System, Singapore 119228, Singapore
| | - Hwei Wuen Chan
- Department of Ophthalmology, National University Health System, Singapore 119228, Singapore
| | - Yew Sen Yuen
- Department of Ophthalmology, National University Health System, Singapore 119228, Singapore
| | - Thet Naing
- Department of Ophthalmology, National University Health System, Singapore 119228, Singapore
| | | | - Victor Teck Chang Koh
- Department of Ophthalmology, National University Health System, Singapore 119228, Singapore
- Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
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Meer E, Grob S, Antonsen EL, Sawyer A. Ocular conditions and injuries, detection and management in spaceflight. NPJ Microgravity 2023; 9:37. [PMID: 37193709 DOI: 10.1038/s41526-023-00279-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 04/12/2023] [Indexed: 05/18/2023] Open
Abstract
Ocular trauma or other ocular conditions can be significantly debilitating in space. A literature review of over 100 articles and NASA evidence books, queried for eye related trauma, conditions, and exposures was conducted. Ocular trauma and conditions during NASA space missions during the Space Shuttle Program and ISS through Expedition 13 in 2006 were reviewed. There were 70 corneal abrasions, 4 dry eyes, 4 eye debris, 5 complaints of ocular irritation, 6 chemical burns, and 5 ocular infections noted. Unique exposures on spaceflight, such as foreign bodies, including celestial dust, which may infiltrate the habitat and contact the ocular surface, as well as chemical and thermal injuries due to prolonged CO2 and heat exposure were reported. Diagnostic modalities used to evaluate the above conditions in space flight include vision questionnaires, visual acuity and Amsler grid testing, fundoscopy, orbital ultrasound, and ocular coherence tomography. Several types of ocular injuries and conditions, mostly affecting the anterior segment, are reported. Further research is necessary to understand the greatest ocular risks that astronauts face and how better we can prevent, but also diagnose and treat these conditions in space.
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Affiliation(s)
- Elana Meer
- Department of Ophthalmology, University of California San Francisco, San Francisco, CA, USA
- University of California Space Health Program, San Francisco, CA, USA
| | - Seanna Grob
- Department of Ophthalmology, University of California San Francisco, San Francisco, CA, USA
| | - Erik L Antonsen
- Department of Emergency Medicine and Center for Space Medicine, Baylor College of Medicine, Houstan, Texas, USA
| | - Aenor Sawyer
- University of California Space Health Program, San Francisco, CA, USA.
- Department of Orthopaedic Surgery, University of California San Francisco, San Francisco, CA, USA.
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Choudhary RA, Hashmi S, Tayyab H. Smartphone-based fundus imaging for evaluation of Retinopathy of Prematurity in a low-income country: A pilot study. Pak J Med Sci 2023; 39:638-643. [PMID: 37250571 PMCID: PMC10214799 DOI: 10.12669/pjms.39.3.7053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 01/21/2023] [Accepted: 01/31/2023] [Indexed: 11/02/2023] Open
Abstract
Objectives To evaluate the feasibility of a novel and simple smart phone-based Retinopathy of Prematurity (ROP) screening approach in a resource-constrained setting. Methods This cross-sectional validation study was conducted at the Department of Ophthalmology and Neonatal Intensive Care Unit (NICU) of The Aga Khan University Hospital, Pakistan, from January 2022 to April 2022. A total of 63 images of eyes with active ROP (stage-1, 2, 3, 4 and/or plus or pre-plus disease) were included in this study. The stage of ROP was documented by the principal investigator using an indirect ophthalmoscope and retinal images were obtained using this novel technique. These images were shared with two masked ROP experts who rated the image quality and determined the stage of ROP and presence of plus disease. Their reports were compared with the initial findings reported by principal investigator using indirect ophthalmoscope. Results We reviewed 63 images for image quality, stage of ROP and presence of plus disease. There was significant agreement between the gold standard and the Rater-1 and 2 for the presence of plus disease (Cohen's kappa was 0.84 and 1.0) and the stage of the disease (Cohen's kappa 0.65 and 1.0). There was significant agreement between the Rater for presence of plus disease and any stage of ROP (Cohen's κ: 0.84 and 0.65 for plus disease and any stage of the ROP, respectively). Rater-1 and 2 rated 96.83% and 98.41% images as excellent / acceptable respectively. Conclusions High quality retinal images can be captured with a smartphone and 28D lens without using any additional adapter equipment. This approach of ROP screening can form basis of telemedicine for ROP in resource constrained areas.
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Affiliation(s)
- Roha Ahmad Choudhary
- Roha Ahmad Choudhary, MBBS. Department of Ophthalmology and Visual Sciences, The Aga Khan University Hospital, Karachi Stadium, Stadium Road, Karachi, Pakistani
| | - Shiraz Hashmi
- Shiraz Hashmi, MBBS, MPH. Department of Ophthalmology and Visual Sciences, The Aga Khan University Hospital, Karachi Stadium, Stadium Road, Karachi, Pakistani
| | - Haroon Tayyab
- Haroon Tayyab, MBBS, FCPS (Ophth), FCPS (VRO), FRCS (Glasg), FACS. Department of Ophthalmology and Visual Sciences, The Aga Khan University Hospital, Karachi Stadium, Stadium Road, Karachi, Pakistani
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Kesavadev J, Mohan V. Reducing the Cost of Diabetes Care with Telemedicine, Smartphone, and Home Monitoring. J Indian Inst Sci 2023; 103:1-12. [PMID: 37362855 PMCID: PMC10119511 DOI: 10.1007/s41745-023-00363-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 02/14/2023] [Indexed: 06/28/2023]
Abstract
The effect of an increasing diabetes population has resulted in escalated costs and overburdened physicians. The increase in cost is not due to the disease per se, but because of its largely preventable complications. Patient-friendly technologies are proven to significantly reduce complications and thereby cost, but seldom practised. Telemedicine is increasingly being utilized in diabetology to improve access to health care, quality of care, and clinical/psychosocial outcomes in patients with diabetes (PWD). In PWD, patient-physician interactions are essential for improving health outcomes and preventing long-term complications. Smartphones are one of the basic modalities for telemedicine application. Mobile phone messaging applications, including text messaging and multimedia message service, could offer a convenient and cost-effective way to support desirable health behaviors. There are diabetes-related mobile apps mainly focusing on self-management of diabetes, lifestyle modification, and medication adherence motivation. With the widespread availability of high-speed Internet, remote monitoring has also become popular. Home monitoring of blood glucose and blood pressure, wearable devices, and continuous glucose monitoring also play a vital role in bringing down the long‑term vascular complications of diabetes and thereby reduce the overall cost and improve the quality of life of patients. There are hundreds of tech platforms for diabetes management, of which only a few with proven efficacy and safety are recommended by physicians.
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Affiliation(s)
| | - Viswanathan Mohan
- Madras Diabetes Research Foundation and Dr. Mohan’s Diabetes Specialities Centre, Chennai, Tamil Nadu India
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Sajid MZ, Qureshi I, Abbas Q, Albathan M, Shaheed K, Youssef A, Ferdous S, Hussain A. Mobile-HR: An Ophthalmologic-Based Classification System for Diagnosis of Hypertensive Retinopathy Using Optimized MobileNet Architecture. Diagnostics (Basel) 2023; 13:diagnostics13081439. [PMID: 37189539 DOI: 10.3390/diagnostics13081439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 04/13/2023] [Accepted: 04/15/2023] [Indexed: 05/17/2023] Open
Abstract
Hypertensive retinopathy (HR) is a serious eye disease that causes the retinal arteries to change. This change is mainly due to the fact of high blood pressure. Cotton wool patches, bleeding in the retina, and retinal artery constriction are affected lesions of HR symptoms. An ophthalmologist often makes the diagnosis of eye-related diseases by analyzing fundus images to identify the stages and symptoms of HR. The likelihood of vision loss can significantly decrease the initial detection of HR. In the past, a few computer-aided diagnostics (CADx) systems were developed to automatically detect HR eye-related diseases using machine learning (ML) and deep learning (DL) techniques. Compared to ML methods, the CADx systems use DL techniques that require the setting of hyperparameters, domain expert knowledge, a huge training dataset, and a high learning rate. Those CADx systems have shown to be good for automating the extraction of complex features, but they cause problems with class imbalance and overfitting. By ignoring the issues of a small dataset of HR, a high level of computational complexity, and the lack of lightweight feature descriptors, state-of-the-art efforts depend on performance enhancement. In this study, a pretrained transfer learning (TL)-based MobileNet architecture is developed by integrating dense blocks to optimize the network for the diagnosis of HR eye-related disease. We developed a lightweight HR-related eye disease diagnosis system, known as Mobile-HR, by integrating a pretrained model and dense blocks. To increase the size of the training and test datasets, we applied a data augmentation technique. The outcomes of the experiments show that the suggested approach was outperformed in many cases. This Mobile-HR system achieved an accuracy of 99% and an F1 score of 0.99 on different datasets. The results were verified by an expert ophthalmologist. These results indicate that the Mobile-HR CADx model produces positive outcomes and outperforms state-of-the-art HR systems in terms of accuracy.
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Affiliation(s)
- Muhammad Zaheer Sajid
- Department of Computer Software Engineering, MCS, National University of Science and Technology, Islamabad 44000, Pakistan
| | - Imran Qureshi
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
| | - Qaisar Abbas
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
| | - Mubarak Albathan
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
| | - Kashif Shaheed
- Department of Multimedia Systems, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland
| | - Ayman Youssef
- Department of Computers and Systems, Electronics Research Institute, Cairo 12622, Egypt
| | - Sehrish Ferdous
- Department of Software Engineering, National University of Modern Languages, Rawalpindi 44000, Pakistan
| | - Ayyaz Hussain
- Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan
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Artificial Intelligence for Diabetic Retinopathy Screening Using Color Retinal Photographs: From Development to Deployment. Ophthalmol Ther 2023; 12:1419-1437. [PMID: 36862308 PMCID: PMC10164194 DOI: 10.1007/s40123-023-00691-3] [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: 01/09/2023] [Accepted: 02/14/2023] [Indexed: 03/03/2023] Open
Abstract
Diabetic retinopathy (DR), a leading cause of preventable blindness, is expected to remain a growing health burden worldwide. Screening to detect early sight-threatening lesions of DR can reduce the burden of vision loss; nevertheless, the process requires intensive manual labor and extensive resources to accommodate the increasing number of patients with diabetes. Artificial intelligence (AI) has been shown to be an effective tool which can potentially lower the burden of screening DR and vision loss. In this article, we review the use of AI for DR screening on color retinal photographs in different phases of application, ranging from development to deployment. Early studies of machine learning (ML)-based algorithms using feature extraction to detect DR achieved a high sensitivity but relatively lower specificity. Robust sensitivity and specificity were achieved with the application of deep learning (DL), although ML is still used in some tasks. Public datasets were utilized in retrospective validations of the developmental phases in most algorithms, which require a large number of photographs. Large prospective clinical validation studies led to the approval of DL for autonomous screening of DR although the semi-autonomous approach may be preferable in some real-world settings. There have been few reports on real-world implementations of DL for DR screening. It is possible that AI may improve some real-world indicators for eye care in DR, such as increased screening uptake and referral adherence, but this has not been proven. The challenges in deployment may include workflow issues, such as mydriasis to lower ungradable cases; technical issues, such as integration into electronic health record systems and integration into existing camera systems; ethical issues, such as data privacy and security; acceptance of personnel and patients; and health-economic issues, such as the need to conduct health economic evaluations of using AI in the context of the country. The deployment of AI for DR screening should follow the governance model for AI in healthcare which outlines four main components: fairness, transparency, trustworthiness, and accountability.
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Computational intelligence in eye disease diagnosis: a comparative study. Med Biol Eng Comput 2023; 61:593-615. [PMID: 36595155 DOI: 10.1007/s11517-022-02737-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 12/09/2022] [Indexed: 01/04/2023]
Abstract
In recent years, eye disorders are an important health issue among older people. Generally, individuals with eye diseases are unaware of the gradual growth of symptoms. Therefore, routine eye examinations are required for early diagnosis. Usually, eye disorders are identified by an ophthalmologist via a slit-lamp investigation. Slit-lamp interpretations are inadequate due to the differences in the analytical skills of the ophthalmologist, inconsistency in eye disorder analysis, and record maintenance issues. Therefore, digital images of an eye and computational intelligence (CI)-based approaches are preferred as assistive methods for eye disease diagnosis. A comparative study of CI-based decision support models for eye disorder diagnosis is presented in this paper. The CI-based decision support systems used for eye abnormalities diagnosis were grouped as anterior and retinal eye abnormalities diagnostic systems, and numerous algorithms used for diagnosing the eye abnormalities were also briefed. Various eye imaging modalities, pre-processing methods such as reflection removal, contrast enhancement, region of interest segmentation methods, and public eye image databases used for CI-based eye disease diagnosis system development were also discussed in this paper. In this comparative study, the reliability of various CI-based systems used for anterior eye and retinal disorder diagnosis was compared based on the precision, sensitivity, and specificity in eye disease diagnosis. The outcomes of the comparative analysis indicate that the CI-based anterior and retinal disease diagnosis systems attained significant prediction accuracy. Hence, these CI-based diagnosis systems can be used in clinics to reduce the burden on physicians, minimize fatigue-related misdetection, and take precise clinical decisions.
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Feasibility and clinical utility of handheld fundus cameras for retinal imaging. Eye (Lond) 2023; 37:274-279. [PMID: 35022568 PMCID: PMC9873676 DOI: 10.1038/s41433-021-01926-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 12/07/2021] [Accepted: 12/22/2021] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND/OBJECTIVES Handheld fundus cameras are portable and cheaper alternatives to table-top counterparts. To date there have been no studies comparing feasibility and clinical utility of handheld fundus cameras to table-top devices. We compare the feasibility and clinical utility of four handheld fundus cameras/retinal imaging devices (Remidio NMFOP, Volk Pictor Plus, Volk iNview, oDocs visoScope) to a table-top camera (Zeiss VisucamNM/FA). SUBJECTS/METHODS Healthy participants (n = 10, mean age ± SD = 21.0 ± 0.9 years) underwent fundus photography with five devices to assess success/failure rates of image acquisition. Participants with optic disc abnormalities (n = 8, mean age ± SD = 26.8 ± 15.9) and macular abnormalities (n = 10, mean age ± SD = 71.6 ± 15.4) underwent imaging with the top three scoring fundus cameras. Images were randomised and subsequently validated by ophthalmologists masked to the diagnoses and devices used. RESULTS Image acquisition success rates (100%) were achieved in non-mydriatic and mydriatic settings for Zeiss, Remidio and Pictor, compared with lower success rates for iNview and oDocs. Image quality and gradeability were significantly higher for Zeiss, Remidio and Pictor (p < 0.0001) compared to iNview and oDocs. For cup:disc ratio estimates, similar levels of bias were seen for Zeiss (-0.09 ± SD:0.15), Remidio (-0.07 ± SD:0.14) and Pictor (-0.05 ± SD:0.16). Diagnostic sensitivities were highest for Zeiss (84.9%; 95% CI, 78.2-91.5%) followed by Pictor (78.1%; 95% CI, 66.6-89.5%) and Remidio (77.5%; 95% CI, 65.9-89.0%). CONCLUSIONS Remidio and Pictor achieve comparable results to the Zeiss table-top camera. Both devices achieved similar scores in feasibility, image quality, image gradeability and diagnostic sensitivity. This suggests that these devices potentially offer a more cost-effective alternative in certain clinical scenarios.
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A Datasheet for the INSIGHT Birmingham, Solihull and Black Country Diabetic Retinopathy Screening Dataset. OPHTHALMOLOGY SCIENCE 2023; 3:100293. [PMID: 37193316 PMCID: PMC10182318 DOI: 10.1016/j.xops.2023.100293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 02/01/2023] [Accepted: 02/21/2023] [Indexed: 02/27/2023]
Abstract
Purpose Diabetic retinopathy (DR) is the most common microvascular complication associated with diabetes mellitus (DM), affecting approximately 40% of this patient population. Early detection of DR is vital to ensure monitoring of disease progression and prompt sight saving treatments as required. This article describes the data contained within the INSIGHT Birmingham, Solihull, and Black Country Diabetic Retinopathy Dataset. Design Dataset descriptor for routinely collected eye screening data. Participants All diabetic patients aged 12 years and older, attending annual digital retinal photography-based screening within the Birmingham, Solihull, and Black Country Eye Screening Programme. Methods The INSIGHT Health Data Research Hub for Eye Health is a National Health Service (NHS)-led ophthalmic bioresource that provides researchers with safe access to anonymized, routinely collected data from contributing NHS hospitals to advance research for patient benefit. This report describes the INSIGHT Birmingham, Solihull, and Black Country DR Screening Dataset, a dataset of anonymized images and linked screening data derived from the United Kingdom's largest regional DR screening program. Main Outcome Measures This dataset consists of routinely collected data from the eye screening program. The data primarily include retinal photographs with the associated DR grading data. Additional data such as corresponding demographic details, information regarding patients' diabetic status, and visual acuity data are also available. Further details regarding available data points are available in the supplementary information, in addition to the INSIGHT webpage included below. Results At the time point of this analysis (December 31, 2019), the dataset comprised 6 202 161 images from 246 180 patients, with a dataset inception date of January 1, 2007. The dataset includes 1 360 547 grading episodes between R0M0 and R3M1. Conclusions This dataset descriptor article summarizes the content of the dataset, how it has been curated, and what its potential uses are. Data are available through a structured application process for research studies that support discovery, clinical evidence analyses, and innovation in artificial intelligence technologies for patient benefit. Further information regarding the data repository and contact details can be found at https://www.insight.hdrhub.org/. Financial Disclosures Proprietary or commercial disclosure may be found after the references.
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Nunez do Rio JM, Nderitu P, Raman R, Rajalakshmi R, Kim R, Rani PK, Sivaprasad S, Bergeles C. Using deep learning to detect diabetic retinopathy on handheld non-mydriatic retinal images acquired by field workers in community settings. Sci Rep 2023; 13:1392. [PMID: 36697482 PMCID: PMC9876892 DOI: 10.1038/s41598-023-28347-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 01/17/2023] [Indexed: 01/26/2023] Open
Abstract
Diabetic retinopathy (DR) at risk of vision loss (referable DR) needs to be identified by retinal screening and referred to an ophthalmologist. Existing automated algorithms have mostly been developed from images acquired with high cost mydriatic retinal cameras and cannot be applied in the settings used in most low- and middle-income countries. In this prospective multicentre study, we developed a deep learning system (DLS) that detects referable DR from retinal images acquired using handheld non-mydriatic fundus camera by non-technical field workers in 20 sites across India. Macula-centred and optic-disc-centred images from 16,247 eyes (9778 participants) were used to train and cross-validate the DLS and risk factor based logistic regression models. The DLS achieved an AUROC of 0.99 (1000 times bootstrapped 95% CI 0.98-0.99) using two-field retinal images, with 93.86 (91.34-96.08) sensitivity and 96.00 (94.68-98.09) specificity at the Youden's index operational point. With single field inputs, the DLS reached AUROC of 0.98 (0.98-0.98) for the macula field and 0.96 (0.95-0.98) for the optic-disc field. Intergrader performance was 90.01 (88.95-91.01) sensitivity and 96.09 (95.72-96.42) specificity. The image based DLS outperformed all risk factor-based models. This DLS demonstrated a clinically acceptable performance for the identification of referable DR despite challenging image capture conditions.
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Affiliation(s)
- Joan M Nunez do Rio
- Institute of Ophthalmology, University College London, 11-43 Bath St., London, EC1V 9EL, UK.
- Section of Ophthalmology, King's College London, London, WC2R 2LS, UK.
| | - Paul Nderitu
- Institute of Ophthalmology, University College London, 11-43 Bath St., London, EC1V 9EL, UK
- Section of Ophthalmology, King's College London, London, WC2R 2LS, UK
| | | | - Ramachandran Rajalakshmi
- Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | | | - Padmaja K Rani
- Anand Bajaj Retina Institute, Srimati Kannuri Santhamma Centre for Vitreoretinal Diseases, LV Prasad Eye Institute, Hyderabad, Telangana, India
| | - Sobha Sivaprasad
- Institute of Ophthalmology, University College London, 11-43 Bath St., London, EC1V 9EL, UK
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital, London, UK
| | - Christos Bergeles
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EU, UK
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Selvachandran G, Quek SG, Paramesran R, Ding W, Son LH. Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods. Artif Intell Rev 2023; 56:915-964. [PMID: 35498558 PMCID: PMC9038999 DOI: 10.1007/s10462-022-10185-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2022] [Indexed: 02/02/2023]
Abstract
The exponential increase in the number of diabetics around the world has led to an equally large increase in the number of diabetic retinopathy (DR) cases which is one of the major complications caused by diabetes. Left unattended, DR worsens the vision and would lead to partial or complete blindness. As the number of diabetics continue to increase exponentially in the coming years, the number of qualified ophthalmologists need to increase in tandem in order to meet the demand for screening of the growing number of diabetic patients. This makes it pertinent to develop ways to automate the detection process of DR. A computer aided diagnosis system has the potential to significantly reduce the burden currently placed on the ophthalmologists. Hence, this review paper is presented with the aim of summarizing, classifying, and analyzing all the recent development on automated DR detection using fundus images from 2015 up to this date. Such work offers an unprecedentedly thorough review of all the recent works on DR, which will potentially increase the understanding of all the recent studies on automated DR detection, particularly on those that deploys machine learning algorithms. Firstly, in this paper, a comprehensive state-of-the-art review of the methods that have been introduced in the detection of DR is presented, with a focus on machine learning models such as convolutional neural networks (CNN) and artificial neural networks (ANN) and various hybrid models. Each AI will then be classified according to its type (e.g. CNN, ANN, SVM), its specific task(s) in performing DR detection. In particular, the models that deploy CNN will be further analyzed and classified according to some important properties of the respective CNN architectures of each model. A total of 150 research articles related to the aforementioned areas that were published in the recent 5 years have been utilized in this review to provide a comprehensive overview of the latest developments in the detection of DR. Supplementary Information The online version contains supplementary material available at 10.1007/s10462-022-10185-6.
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Affiliation(s)
- Ganeshsree Selvachandran
- Department of Actuarial Science and Applied Statistics, Faculty of Business & Management, UCSI University, Jalan Menara Gading, Cheras, 56000 Kuala Lumpur, Malaysia
| | - Shio Gai Quek
- Department of Actuarial Science and Applied Statistics, Faculty of Business & Management, UCSI University, Jalan Menara Gading, Cheras, 56000 Kuala Lumpur, Malaysia
| | - Raveendran Paramesran
- Institute of Computer Science and Digital Innovation, UCSI University, Jalan Menara Gading, Cheras, 56000 Kuala Lumpur, Malaysia
| | - Weiping Ding
- School of Information Science and Technology, Nantong University, Nantong, 226019 People’s Republic of China
| | - Le Hoang Son
- VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam
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Bai J, Wan Z, Li P, Chen L, Wang J, Fan Y, Chen X, Peng Q, Gao P. Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening. Front Cell Dev Biol 2022; 10:1053483. [PMID: 36407116 PMCID: PMC9670537 DOI: 10.3389/fcell.2022.1053483] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 10/18/2022] [Indexed: 10/31/2023] Open
Abstract
Objective: To evaluate the accuracy and feasibility of the auto-detection of 15 retinal disorders with artificial intelligence (AI)-assisted optical coherence tomography (OCT) in community screening. Methods: A total of 954 eyes of 477 subjects from four local communities were enrolled in this study from September to December 2021. They received OCT scans covering an area of 12 mm × 9 mm at the posterior pole retina involving the macular and optic disc, as well as other ophthalmic examinations performed using their demographic information recorded. The OCT images were analyzed using integrated software with the previously established algorithm based on the deep-learning method and trained to detect 15 kinds of retinal disorders, namely, pigment epithelial detachment (PED), posterior vitreous detachment (PVD), epiretinal membranes (ERMs), sub-retinal fluid (SRF), choroidal neovascularization (CNV), drusen, retinoschisis, cystoid macular edema (CME), exudation, macular hole (MH), retinal detachment (RD), ellipsoid zone disruption, focal choroidal excavation (FCE), choroid atrophy, and retinal hemorrhage. Meanwhile, the diagnosis was also generated from three groups of individual ophthalmologists (group of retina specialists, senior ophthalmologists, and junior ophthalmologists) and compared with those by the AI. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated, and kappa statistics were performed. Results: A total of 878 eyes were finally enrolled, with 76 excluded due to poor image quality. In the detection of 15 retinal disorders, the ROC curve comparison between AI and professors' presented relatively large AUC (0.891-0.997), high sensitivity (87.65-100%), and high specificity (80.12-99.41%). Among the ROC curve comparisons with those by the retina specialists, AI was the closest one to the professors' compared to senior and junior ophthalmologists (p < 0.05). Conclusion: AI-assisted OCT is highly accurate, sensitive, and specific in auto-detection of 15 kinds of retinal disorders, certifying its feasibility and effectiveness in community ophthalmic screening.
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Affiliation(s)
- Jianhao Bai
- Department of Ophthalmology, Shanghai Tenth People’s Hospital of Tongji University, Tongji University School of Medicine, Shanghai, China
| | - Zhongqi Wan
- Department of Ophthalmology, Shanghai Tenth People’s Hospital of Tongji University, Tongji University School of Medicine, Shanghai, China
| | - Ping Li
- Department of Ophthalmology, Shanghai Tenth People’s Hospital of Tongji University, Tongji University School of Medicine, Shanghai, China
| | - Lei Chen
- Department of Ophthalmology, Shanghai Tenth People’s Hospital of Tongji University, Tongji University School of Medicine, Shanghai, China
| | - Jingcheng Wang
- Suzhou Big Vision Medical Technology Co Ltd, Suzhou, China
| | - Yu Fan
- Suzhou Big Vision Medical Technology Co Ltd, Suzhou, China
| | - Xinjian Chen
- School of Electronic and Information Engineering, Soochow University, Suzhou, China
| | - Qing Peng
- Department of Ophthalmology, Shanghai Tenth People’s Hospital of Tongji University, Tongji University School of Medicine, Shanghai, China
| | - Peng Gao
- Department of Ophthalmology, Shanghai Tenth People’s Hospital of Tongji University, Tongji University School of Medicine, Shanghai, China
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Lu Z, Miao J, Dong J, Zhu S, Wang X, Feng J. Automatic classification of retinal diseases with transfer learning-based lightweight convolutional neural network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.104365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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