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Kubin AM, Huhtinen P, Ohtonen P, Keskitalo A, Wirkkala J, Hautala N. Comparison of 21 artificial intelligence algorithms in automated diabetic retinopathy screening using handheld fundus camera. Ann Med 2024; 56:2352018. [PMID: 38738798 PMCID: PMC11095279 DOI: 10.1080/07853890.2024.2352018] [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: 10/10/2023] [Accepted: 04/21/2024] [Indexed: 05/14/2024] Open
Abstract
BACKGROUND Diabetic retinopathy (DR) is a common complication of diabetes and may lead to irreversible visual loss. Efficient screening and improved treatment of both diabetes and DR have amended visual prognosis for DR. The number of patients with diabetes is increasing and telemedicine, mobile handheld devices and automated solutions may alleviate the burden for healthcare. We compared the performance of 21 artificial intelligence (AI) algorithms for referable DR screening in datasets taken by handheld Optomed Aurora fundus camera in a real-world setting. PATIENTS AND METHODS Prospective study of 156 patients (312 eyes) attending DR screening and follow-up. Both papilla- and macula-centred 50° fundus images were taken from each eye. DR was graded by experienced ophthalmologists and 21 AI algorithms. RESULTS Most eyes, 183 out of 312 (58.7%), had no DR and mild NPDR was noted in 21 (6.7%) of the eyes. Moderate NPDR was detected in 66 (21.2%) of the eyes, severe NPDR in 1 (0.3%), and PDR in 41 (13.1%) composing a group of 34.6% of eyes with referable DR. The AI algorithms achieved a mean agreement of 79.4% for referable DR, but the results varied from 49.4% to 92.3%. The mean sensitivity for referable DR was 77.5% (95% CI 69.1-85.8) and specificity 80.6% (95% CI 72.1-89.2). The rate for images ungradable by AI varied from 0% to 28.2% (mean 1.9%). Nineteen out of 21 (90.5%) AI algorithms resulted in grading for DR at least in 98% of the images. CONCLUSIONS Fundus images captured with Optomed Aurora were suitable for DR screening. The performance of the AI algorithms varied considerably emphasizing the need for external validation of screening algorithms in real-world settings before their clinical application.
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Affiliation(s)
- Anna-Maria Kubin
- Department of Ophthalmology, Oulu University Hospital, Oulu, Finland
- Research Unit of Clinical Medicine, Oulu, Finland
- Medical Research Center, University of Oulu, Oulu, Finland
| | | | - Pasi Ohtonen
- Research Service Unit, Oulu, Finland
- The Research Unit of Surgery, Anesthesia and Intensive Care, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Antti Keskitalo
- Department of Ophthalmology, Oulu University Hospital, Oulu, Finland
| | - Joonas Wirkkala
- Department of Ophthalmology, Oulu University Hospital, Oulu, Finland
- Research Unit of Clinical Medicine, Oulu, Finland
- Medical Research Center, University of Oulu, Oulu, Finland
| | - Nina Hautala
- Department of Ophthalmology, Oulu University Hospital, Oulu, Finland
- Research Unit of Clinical Medicine, Oulu, Finland
- Medical Research Center, University of Oulu, Oulu, Finland
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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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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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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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Karabeg M, Petrovski G, Hertzberg SN, Erke MG, Fosmark DS, Russell G, Moe MC, Volke V, Raudonis V, Verkauskiene R, Sokolovska J, Haugen IBK, Petrovski BE. A pilot cost-analysis study comparing AI-based EyeArt® and ophthalmologist assessment of diabetic retinopathy in minority women in Oslo, Norway. Int J Retina Vitreous 2024; 10:40. [PMID: 38783384 PMCID: PMC11112837 DOI: 10.1186/s40942-024-00547-3] [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: 01/14/2024] [Accepted: 03/17/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Diabetic retinopathy (DR) is the leading cause of adult blindness in the working age population worldwide, which can be prevented by early detection. Regular eye examinations are recommended and crucial for detecting sight-threatening DR. Use of artificial intelligence (AI) to lessen the burden on the healthcare system is needed. PURPOSE To perform a pilot cost-analysis study for detecting DR in a cohort of minority women with DM in Oslo, Norway, that have the highest prevalence of diabetes mellitus (DM) in the country, using both manual (ophthalmologist) and autonomous (AI) grading. This is the first study in Norway, as far as we know, that uses AI in DR- grading of retinal images. METHODS On Minority Women's Day, November 1, 2017, in Oslo, Norway, 33 patients (66 eyes) over 18 years of age diagnosed with DM (T1D and T2D) were screened. The Eidon - True Color Confocal Scanner (CenterVue, United States) was used for retinal imaging and graded for DR after screening had been completed, by an ophthalmologist and automatically, using EyeArt Automated DR Detection System, version 2.1.0 (EyeArt, EyeNuk, CA, USA). The gradings were based on the International Clinical Diabetic Retinopathy (ICDR) severity scale [1] detecting the presence or absence of referable DR. Cost-minimization analyses were performed for both grading methods. RESULTS 33 women (64 eyes) were eligible for the analysis. A very good inter-rater agreement was found: 0.98 (P < 0.01), between the human and AI-based EyeArt grading system for detecting DR. The prevalence of DR was 18.6% (95% CI: 11.4-25.8%), and the sensitivity and specificity were 100% (95% CI: 100-100% and 95% CI: 100-100%), respectively. The cost difference for AI screening compared to human screening was $143 lower per patient (cost-saving) in favour of AI. CONCLUSION Our results indicate that The EyeArt AI system is both a reliable, cost-saving, and useful tool for DR grading in clinical practice.
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Affiliation(s)
- Mia Karabeg
- Center for Eye Research and Innovative Diagnostics, Department of Ophthalmology, Institute for Clinical Medicine, University of Oslo, Kirkeveien 166, 0450, Oslo, Norway
- Department of Ophthalmology, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway
| | - Goran Petrovski
- Center for Eye Research and Innovative Diagnostics, Department of Ophthalmology, Institute for Clinical Medicine, University of Oslo, Kirkeveien 166, 0450, Oslo, Norway
- Department of Ophthalmology, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway
- Department of Ophthalmology, University of Split School of Medicine and University Hospital Centre, 21000, Split, Croatia
- UKLONetwork, University St. Kliment Ohridski-Bitola, 7000, Bitola, Macedonia
| | - Silvia Nw Hertzberg
- Center for Eye Research and Innovative Diagnostics, Department of Ophthalmology, Institute for Clinical Medicine, University of Oslo, Kirkeveien 166, 0450, Oslo, Norway
- Department of Ophthalmology, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway
| | - Maja Gran Erke
- Department of Ophthalmology, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway
| | - Dag Sigurd Fosmark
- Department of Ophthalmology, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway
| | - Greg Russell
- Clinical Development, Eyenuk Inc, Woodland Hills, CA, USA
| | - Morten C Moe
- Center for Eye Research and Innovative Diagnostics, Department of Ophthalmology, Institute for Clinical Medicine, University of Oslo, Kirkeveien 166, 0450, Oslo, Norway
- Department of Ophthalmology, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway
| | - Vallo Volke
- Faculty of Medicine, Tartu University, 50411, Tartu, Estonia
| | - Vidas Raudonis
- Automation Department, Kaunas University of Technology, 51368, Kaunas, Lithuania
| | - Rasa Verkauskiene
- Institute of Endocrinology, Lithuanian University of Health Sciences, 50161, Kaunas, Lithuania
| | | | | | - Beata Eva Petrovski
- Center for Eye Research and Innovative Diagnostics, Department of Ophthalmology, Institute for Clinical Medicine, University of Oslo, Kirkeveien 166, 0450, Oslo, Norway.
- Department of Ophthalmology, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway.
- Institute of Endocrinology, Lithuanian University of Health Sciences, 50161, Kaunas, Lithuania.
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Martin E, Cook AG, Frost SM, Turner AW, Chen FK, McAllister IL, Nolde JM, Schlaich MP. Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs. Eye (Lond) 2024:10.1038/s41433-024-03085-2. [PMID: 38734746 DOI: 10.1038/s41433-024-03085-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 04/03/2024] [Accepted: 04/11/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND/OBJECTIVES Artificial intelligence can assist with ocular image analysis for screening and diagnosis, but it is not yet capable of autonomous full-spectrum screening. Hypothetically, false-positive results may have unrealized screening potential arising from signals persisting despite training and/or ambiguous signals such as from biomarker overlap or high comorbidity. The study aimed to explore the potential to detect clinically useful incidental ocular biomarkers by screening fundus photographs of hypertensive adults using diabetic deep learning algorithms. SUBJECTS/METHODS Patients referred for treatment-resistant hypertension were imaged at a hospital unit in Perth, Australia, between 2016 and 2022. The same 45° colour fundus photograph selected for each of the 433 participants imaged was processed by three deep learning algorithms. Two expert retinal specialists graded all false-positive results for diabetic retinopathy in non-diabetic participants. RESULTS Of the 29 non-diabetic participants misclassified as positive for diabetic retinopathy, 28 (97%) had clinically useful retinal biomarkers. The models designed to screen for fewer diseases captured more incidental disease. All three algorithms showed a positive correlation between severity of hypertensive retinopathy and misclassified diabetic retinopathy. CONCLUSIONS The results suggest that diabetic deep learning models may be responsive to hypertensive and other clinically useful retinal biomarkers within an at-risk, hypertensive cohort. Observing that models trained for fewer diseases captured more incidental pathology increases confidence in signalling hypotheses aligned with using self-supervised learning to develop autonomous comprehensive screening. Meanwhile, non-referable and false-positive outputs of other deep learning screening models could be explored for immediate clinical use in other populations.
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Affiliation(s)
- Eve Martin
- Commonwealth Scientific and Industrial Research Organisation (CSIRO), Kensington, WA, Australia.
- School of Population and Global Health, The University of Western Australia, Crawley, Australia.
- Dobney Hypertension Centre - Royal Perth Hospital Unit, Medical School, The University of Western Australia, Perth, Australia.
- Australian e-Health Research Centre, Floreat, WA, Australia.
| | - Angus G Cook
- School of Population and Global Health, The University of Western Australia, Crawley, Australia
| | - Shaun M Frost
- Commonwealth Scientific and Industrial Research Organisation (CSIRO), Kensington, WA, Australia
- Australian e-Health Research Centre, Floreat, WA, Australia
| | - Angus W Turner
- Lions Eye Institute, Nedlands, WA, Australia
- Centre for Ophthalmology and Visual Science, The University of Western Australia, Perth, Australia
| | - Fred K Chen
- Lions Eye Institute, Nedlands, WA, Australia
- Centre for Ophthalmology and Visual Science, The University of Western Australia, Perth, Australia
- Centre for Eye Research Australia, The Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia
- Ophthalmology, Department of Surgery, The University of Melbourne, East Melbourne, VIC, Australia
- Ophthalmology Department, Royal Perth Hospital, Perth, Australia
| | - Ian L McAllister
- Lions Eye Institute, Nedlands, WA, Australia
- Centre for Ophthalmology and Visual Science, The University of Western Australia, Perth, Australia
| | - Janis M Nolde
- Dobney Hypertension Centre - Royal Perth Hospital Unit, Medical School, The University of Western Australia, Perth, Australia
- Departments of Cardiology and Nephrology, Royal Perth Hospital, Perth, Australia
| | - Markus P Schlaich
- Dobney Hypertension Centre - Royal Perth Hospital Unit, Medical School, The University of Western Australia, Perth, Australia
- Departments of Cardiology and Nephrology, Royal Perth Hospital, Perth, Australia
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Ullah N, Kiu Chou W, Vardanyan R, Arjomandi Rad A, Shah V, Torabi S, Avavde D, Airapetyan AA, Zubarevich A, Weymann A, Ruhparwar A, Miller G, Malawana J. Machine learning algorithms for the prognostication of abdominal aortic aneurysm progression: a systematic review. Minerva Surg 2024; 79:219-227. [PMID: 37987755 DOI: 10.23736/s2724-5691.23.10130-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
INTRODUCTION Abdominal aortic aneurysm (AAA), often characterized by an abdominal aortic diameter over 3.0 cm, is managed through screening, surveillance, and surgical intervention. AAA growth can be heterogeneous and rupture carries a high mortality rate, with size and certain risk factors influencing rupture risk. Research is ongoing to accurately predict individual AAA growth rates for personalized management. Machine learning, a subset of artificial intelligence, has shown promise in various medical fields, including endoleak detection post-EVAR. However, its application for predicting AAA growth remains insufficiently explored, thus necessitating further investigation. Subsequently, this paper aims to summarize the current status of machine learning in predicting AAA growth. EVIDENCE ACQUISITION A systematic database search of Embase, MEDLINE, Cochrane, PubMed and Google Scholar from inception till December 2022 was conducted of original articles that discussed the use of machine learning in predicting AAA growth using the aforementioned databases. EVIDENCE SYNTHESIS Overall, 2742 articles were extracted, of which seven retrospective studies involving 410 patients were included using a predetermined criteria. Six out of seven studies applied a supervised learning approach for their machine learning (ML) models, with considerable diversity observed within specific ML models. The majority of the studies concluded that machine learning models perform better in predicting AAA growth in comparison to reference models. All studies focused on predicting AAA growth over specified durations. Maximal luminal diameter was the most frequently used indicator, with alternative predictors being AAA volume, ILT (intraluminal thrombus) and flow-medicated diameter (FMD). CONCLUSIONS The nascent field of applying machine learning (ML) for Abdominal Aortic Aneurysm (AAA) expansion prediction exhibits potential to enhance predictive accuracy across diverse parameters. Future studies must emphasize evidencing clinical utility in a healthcare system context, thereby ensuring patient outcome improvement. This will necessitate addressing key ethical implications in establishing prospective studies related to this topic and collaboration among pivotal stakeholders within the AI field.
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Affiliation(s)
- Nazifa Ullah
- Faculty of Medicine, University College London, London, UK
| | - Wing Kiu Chou
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Robert Vardanyan
- Department of Medicine, Faculty of Medicine, Imperial College London, London, UK -
- Research Unit, The Healthcare Leadership Academy, London, UK
| | - Arian Arjomandi Rad
- Department of Medicine, Faculty of Medicine, Imperial College London, London, UK
- Research Unit, The Healthcare Leadership Academy, London, UK
- Medical Sciences Division, University of Oxford, Oxford, UK
| | - Viraj Shah
- Department of Medicine, Faculty of Medicine, Imperial College London, London, UK
| | - Saeed Torabi
- Department of Anesthesiology, University Hospital Cologne, Cologne, Germany
| | - Dani Avavde
- Department of Vascular Surgery, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Arkady A Airapetyan
- Department of Research and Academia, National Institute of Health, Yerevan, Armenia
| | - Alina Zubarevich
- Department of Cardiothoracic Transplant and Vascular Surgery, Hannover Medical School, Hannover, Germany
| | - Alexander Weymann
- Department of Cardiothoracic Transplant and Vascular Surgery, Hannover Medical School, Hannover, Germany
| | - Arjang Ruhparwar
- Department of Cardiothoracic Transplant and Vascular Surgery, Hannover Medical School, Hannover, Germany
| | - George Miller
- Research Unit, The Healthcare Leadership Academy, London, UK
- Centre for Digital Health and Education Research (CoDHER), University of Central Lancashire Medical School, Preston, UK
| | - Johann Malawana
- Research Unit, The Healthcare Leadership Academy, London, UK
- Centre for Digital Health and Education Research (CoDHER), University of Central Lancashire Medical School, Preston, UK
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Macdonald T, Dinnes J, Maniatopoulos G, Taylor-Phillips S, Shinkins B, Hogg J, Dunbar JK, Solebo AL, Sutton H, Attwood J, Pogose M, Given-Wilson R, Greaves F, Macrae C, Pearson R, Bamford D, Tufail A, Liu X, Denniston AK. Target Product Profile for a Machine Learning-Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study. JMIR Res Protoc 2024; 13:e50568. [PMID: 38536234 PMCID: PMC11007610 DOI: 10.2196/50568] [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: 09/14/2023] [Revised: 02/02/2024] [Accepted: 02/13/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND Diabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluation, and implementation. Target product profiles (TPPs) summarize the requirements necessary for successful implementation so these can guide product development and evaluation. OBJECTIVE This study aims to produce a TPP for an ML-automated retinal imaging analysis software (ML-ARIAS) system for use in DES in England. METHODS This work will consist of 3 phases. Phase 1 will establish the characteristics to be addressed in the TPP. A list of candidate characteristics will be generated from the following sources: an overview of systematic reviews of diagnostic test TPPs; a systematic review of digital health TPPs; and the National Institute for Health and Care Excellence's Evidence Standards Framework for Digital Health Technologies. The list of characteristics will be refined and validated by a study advisory group (SAG) made up of representatives from key stakeholders in DES. This includes people with diabetes; health care professionals; health care managers and leaders; and regulators and policy makers. In phase 2, specifications for these characteristics will be drafted following a series of semistructured interviews with participants from these stakeholder groups. Data collected from these interviews will be analyzed using the shortlist of characteristics as a framework, after which specifications will be drafted to create a draft TPP. Following approval by the SAG, in phase 3, the draft will enter an internet-based Delphi consensus study with participants sought from the groups previously identified, as well as ML-ARIAS developers, to ensure feasibility. Participants will be invited to score characteristic and specification pairs on a scale from "definitely exclude" to "definitely include," and suggest edits. The document will be iterated between rounds based on participants' feedback. Feedback on the draft document will be sought from a group of ML-ARIAS developers before its final contents are agreed upon in an in-person consensus meeting. At this meeting, representatives from the stakeholder groups previously identified (minus ML-ARIAS developers, to avoid bias) will be presented with the Delphi results and feedback of the user group and asked to agree on the final contents by vote. RESULTS Phase 1 was completed in November 2023. Phase 2 is underway and expected to finish in March 2024. Phase 3 is expected to be complete in July 2024. CONCLUSIONS The multistakeholder development of a TPP for an ML-ARIAS for use in DES in England will help developers produce tools that serve the needs of patients, health care providers, and their staff. The TPP development process will also provide methods and a template to produce similar documents in other disease areas. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/50568.
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Affiliation(s)
- Trystan Macdonald
- Ophthalmology Department, Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham National Health Service Foundation Trust, Birmingham, United Kingdom
- Academic Unit of Ophthalmology, Institute of Inflammation and Aging, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- National Institute for Health and Care Research Birmingham Biomedical Research Centre, Birmingham, United Kingdom
| | - Jacqueline Dinnes
- National Institute for Health and Care Research Birmingham Biomedical Research Centre, Birmingham, United Kingdom
| | | | | | - Bethany Shinkins
- Warwick Medical School, University of Warwick, Coventry, United Kingdom
| | - Jeffry Hogg
- Population Health Sciences Institute, Faculty of Medical Sciences, The University of Newcastle upon Tyne, Newcastle, United Kingdom
| | | | - Ameenat Lola Solebo
- Population Policy and Practice, University College London Great Ormond Street Institute of Child Health, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | | | - John Attwood
- Alder Hey Children's Hospital, Alder Hey Children's Hospital NHS Foundation Trust, Liverpool, United Kingdom
| | | | - Rosalind Given-Wilson
- St. George's University Hospitals National Health Service Foundation Trust, London, United Kingdom
| | - Felix Greaves
- National Institute for Health and Care Excellence, London, United Kingdom
- Faculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom
| | - Carl Macrae
- Nottingham University Business School, University of Nottingham, Nottingham, United Kingdom
| | - Russell Pearson
- Medicines and Healthcare Products Regulatory Agency, London, United Kingdom
| | | | - Adnan Tufail
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
| | - Xiaoxuan Liu
- Ophthalmology Department, Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham National Health Service Foundation Trust, Birmingham, United Kingdom
- Academic Unit of Ophthalmology, Institute of Inflammation and Aging, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- National Institute for Health and Care Research Birmingham Biomedical Research Centre, Birmingham, United Kingdom
| | - Alastair K Denniston
- Ophthalmology Department, Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham National Health Service Foundation Trust, Birmingham, United Kingdom
- Academic Unit of Ophthalmology, Institute of Inflammation and Aging, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- National Institute for Health and Care Research Birmingham Biomedical Research Centre, Birmingham, United Kingdom
- Centre for Regulatory Science and Innovation, Birmingham Health Partners, Birmingham, United Kingdom
- National Institute for Health and Care Research Biomedical Research Centre at Moorfields and University College London Institute of Ophthalmology, London, United Kingdom
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9
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Yim D, Khuntia J, Parameswaran V, Meyers A. Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review. JMIR Med Inform 2024; 12:e52073. [PMID: 38506918 PMCID: PMC10993141 DOI: 10.2196/52073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/12/2023] [Accepted: 01/30/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. OBJECTIVE This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. METHODS We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. RESULTS Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. CONCLUSIONS GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.
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Affiliation(s)
- Dobin Yim
- Loyola University, Maryland, MD, United States
| | - Jiban Khuntia
- University of Colorado Denver, Denver, CO, United States
| | | | - Arlen Meyers
- University of Colorado Denver, Denver, CO, United States
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Jairoun AA, Al-Hemyari SS, Shahwan M, Al-Qirim T, Shahwan M. Benefit-Risk Assessment of ChatGPT Applications in the Field of Diabetes and Metabolic Illnesses: A Qualitative Study. Clin Med Insights Endocrinol Diabetes 2024; 17:11795514241235514. [PMID: 38495947 PMCID: PMC10943713 DOI: 10.1177/11795514241235514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 02/06/2024] [Indexed: 03/19/2024] Open
Abstract
Background The use of ChatGPT and artificial intelligence (AI) in the management of metabolic and endocrine disorders presents both significant opportunities and notable risks. Objectives To investigate the benefits and risks associated with the application of ChatGPT in managing diabetes and metabolic illnesses by exploring the perspectives of endocrinologists and diabetologists. Methods and materials The study employed a qualitative research approach. A semi-structured in-depth interview guide was developed. A convenience sample of 25 endocrinologists and diabetologists was enrolled and interviewed. All interviews were audiotaped and verbatim transcribed; then, thematic analysis was used to determine the themes in the data. Results The findings of the thematic analysis resulted in 19 codes and 9 major themes regarding the benefits of implementing AI and ChatGPT in managing diabetes and metabolic illnesses. Moreover, the extracted risks of implementing AI and ChatGPT in managing diabetes and metabolic illnesses were categorized into 7 themes and 14 codes. The benefits of heightened diagnostic precision, tailored treatment, and efficient resource utilization have potential to improve patient results. Concurrently, the identification of potential challenges, such as data security concerns and the need for AI that can be explained, enables stakeholders to proactively tackle these issues. Conclusions Regulatory frameworks must evolve to keep pace with the rapid adoption of AI in healthcare. Sustained attention to ethical considerations, including obtaining patient consent, safeguarding data privacy, ensuring accountability, and promoting fairness, remains critical. Despite its potential impact on the human aspect of healthcare, AI will remain an integral component of patient-centered care. Striking a balance between AI-assisted decision-making and human expertise is essential to uphold trust and provide comprehensive patient care.
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Affiliation(s)
- Ammar Abdulrahman Jairoun
- Discipline of Clinical Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia (USM), Pulau Pinang, Malaysia
- Health and Safety Department, Dubai Municipality, Dubai, United Arab Emirates
| | - Sabaa Saleh Al-Hemyari
- Discipline of Clinical Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia (USM), Pulau Pinang, Malaysia
- Pharmacy Department, Emirates Health Services, Dubai, United Arab Emirates
| | - Moyad Shahwan
- College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates
- Centre of Medical and Bio-allied Health Sciences Research, Ajman University, United Arab Emirates
| | - Tariq Al-Qirim
- Faculty of Pharmacy, Al-Zaytoonah University of Jordan, Amman, Jordan
| | - Monzer Shahwan
- Diabetes Clinic, AL-Swity Center for Dermatology and Chronic Diseases, Ramallah, Palestine
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11
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Ramoutar RR. An Economic Analysis for the Use of Artificial Intelligence in Screening for Diabetic Retinopathy in Trinidad and Tobago. Cureus 2024; 16:e55745. [PMID: 38586698 PMCID: PMC10999161 DOI: 10.7759/cureus.55745] [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] [Accepted: 03/07/2024] [Indexed: 04/09/2024] Open
Abstract
This is a systematic review of 25 publications on the topics of the prevalence and cost of diabetic retinopathy (DR) in Trinidad and Tobago, the cost of traditional methods of screening for DR, and the use and cost of artificial intelligence (AI) in screening for DR. Analysis of these publications was used to identify and make estimates for how resources allocated to ophthalmology in public health systems in Trinidad and Tobago can be more efficiently utilized by employing AI in diagnosing treatable DR. DR screening was found to be an effective method of detecting the disease. Screening was found to be a universally cost-effective method of disease prevention and for altering the natural history of the disease in the spectrum of low-middle to high-income economies, such as Rwanda, Thailand, China, South Korea, and Singapore. AI and deep learning systems were found to be clinically superior to, or as effective as, human graders in areas where they were deployed, indicating that the systems are clinically safe. They have been shown to improve access to diabetic retinal screening, improve compliance with screening appointments, and prove to be cost-effective, especially in rural areas. Trinidad and Tobago, which is estimated to be disproportionately more affected by the burden of DR when projected out to the mid-21st century, stands to save as much as US$60 million annually from the implementation of an AI-based system to screen for DR versus conventional manual grading.
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Affiliation(s)
- Ryan R Ramoutar
- Ophthalmology, University Hospitals of Leicester NHS Trust, Leicester, GBR
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12
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Zhu J, Li FF, Li GX, Jiang SY, Cheng D, Bao FJ, Wu SQ, Dai Q, Ye YF. Enhancing Vault Prediction and ICL Sizing Through Advanced Machine Learning Models. J Refract Surg 2024; 40:e126-e132. [PMID: 38466764 DOI: 10.3928/1081597x-20240131-01] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
PURPOSE To use artificial intelligence (AI) technology to accurately predict vault and Implantable Collamer Lens (ICL) size. METHODS The methodology focused on enhancing predictive capabilities through the fusion of machine-learning algorithms. Specifically, AdaBoost, Random Forest, Decision Tree, Support Vector Regression, LightGBM, and XGBoost were integrated into a majority-vote model. The performance of each model was evaluated using appropriate metrics such as accuracy, precision, F1-score, and area under the curve (AUC). RESULTS The majority-vote model exhibited the highest performance among the classification models, with an accuracy of 81.9% area under the curve (AUC) of 0.807. Notably, LightGBM (accuracy = 0.788, AUC = 0.803) and XGBoost (ACC = 0.790, AUC = 0.801) demonstrated competitive results. For the ICL size prediction, the Random Forest model achieved an impressive accuracy of 85.3% (AUC = 0.973), whereas XG-Boost (accuracy = 0.834, AUC = 0.961) and LightGBM (accuracy = 0.816, AUC = 0.961) maintained their compatibility. CONCLUSIONS This study highlights the potential of diverse machine learning algorithms to enhance postoperative vault and ICL size prediction, ultimately contributing to the safety of ICL implantation procedures. Furthermore, the introduction of the novel majority-vote model demonstrates its capability to combine the advantages of multiple models, yielding superior accuracy. Importantly, this study will empower ophthalmologists to use a precise tool for vault prediction, facilitating informed ICL size selection in clinical practice. [J Refract Surg. 2024;40(3):e126-e132.].
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13
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Cicinelli MV, Gravina S, Rutigliani C, Checchin L, La Franca L, Lattanzio R, Bandello F. Assessing Diabetic Retinopathy Staging With AI: A Comparative Analysis Between Pseudocolor and LED Imaging. Transl Vis Sci Technol 2024; 13:11. [PMID: 38488432 PMCID: PMC10946690 DOI: 10.1167/tvst.13.3.11] [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: 12/02/2023] [Accepted: 02/04/2024] [Indexed: 03/19/2024] Open
Abstract
Purpose To compare the diagnostic performance of artificial intelligence (AI)-based diabetic retinopathy (DR) staging system across pseudocolor, simulated white light (SWL), and light-emitting diode (LED) camera imaging modalities. Methods A cross-sectional investigation involved patients with diabetes undergoing imaging with an iCare DRSplus confocal LED camera and an Optos confocal, ultra-widefield pseudocolor camera, with and without SWL. Macula-centered and optic nerve-centered 45 × 45-degree photographs were processed using EyeArt v2.1. Human graders established the ground truth (GT) for DR severity on dilated fundus exams. Sensitivity and weighted Cohen's weighted kappa (wκ) were calculated. An ordinal generalized linear mixed model identified factors influencing accurate DR staging. Results The study included 362 eyes from 189 patients. The LED camera excelled in identifying sight-threatening DR stages (sensitivity = 0.83, specificity = 0.95 for proliferative DR) and had the highest agreement with the GT (wκ = 0.71). The addition of SWL to pseudocolor imaging resulted in decreased performance (sensitivity = 0.33, specificity = 0.98 for proliferative DR; wκ = 0.55). Peripheral lesions reduced the likelihood of being staged in the same or higher DR category by 80% (P < 0.001). Conclusions Pseudocolor and LED cameras, although proficient, demonstrated non-interchangeable performance, with the LED camera exhibiting superior accuracy in identifying advanced DR stages. These findings underscore the importance of implementing AI systems trained for ultra-widefield imaging, considering the impact of peripheral lesions on correct DR staging. Translational Relevance This study underscores the need for artificial intelligence-based systems specifically trained for ultra-widefield imaging in diabetic retinopathy assessment.
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Affiliation(s)
- Maria Vittoria Cicinelli
- Department of Ophthalmology, IRCCS San Raffaele Hospital, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Salvatore Gravina
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Carola Rutigliani
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Lisa Checchin
- Department of Ophthalmology, IRCCS San Raffaele Hospital, Milan, Italy
| | | | | | - Francesco Bandello
- Department of Ophthalmology, IRCCS San Raffaele Hospital, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
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14
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Wang Y, Liu C, Hu W, Luo L, Shi D, Zhang J, Yin Q, Zhang L, Han X, He M. Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case. NPJ Digit Med 2024; 7:43. [PMID: 38383738 PMCID: PMC10881978 DOI: 10.1038/s41746-024-01032-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 02/05/2024] [Indexed: 02/23/2024] Open
Abstract
Artificial intelligence (AI) models have shown great accuracy in health screening. However, for real-world implementation, high accuracy may not guarantee cost-effectiveness. Improving AI's sensitivity finds more high-risk patients but may raise medical costs while increasing specificity reduces unnecessary referrals but may weaken detection capability. To evaluate the trade-off between AI model performance and the long-running cost-effectiveness, we conducted a cost-effectiveness analysis in a nationwide diabetic retinopathy (DR) screening program in China, comprising 251,535 participants with diabetes over 30 years. We tested a validated AI model in 1100 different diagnostic performances (presented as sensitivity/specificity pairs) and modeled annual screening scenarios. The status quo was defined as the scenario with the most accurate AI performance. The incremental cost-effectiveness ratio (ICER) was calculated for other scenarios against the status quo as cost-effectiveness metrics. Compared to the status quo (sensitivity/specificity: 93.3%/87.7%), six scenarios were cost-saving and seven were cost-effective. To achieve cost-saving or cost-effective, the AI model should reach a minimum sensitivity of 88.2% and specificity of 80.4%. The most cost-effective AI model exhibited higher sensitivity (96.3%) and lower specificity (80.4%) than the status quo. In settings with higher DR prevalence and willingness-to-pay levels, the AI needed higher sensitivity for optimal cost-effectiveness. Urban regions and younger patient groups also required higher sensitivity in AI-based screening. In real-world DR screening, the most accurate AI model may not be the most cost-effective. Cost-effectiveness should be independently evaluated, which is most likely to be affected by the AI's sensitivity.
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Affiliation(s)
- Yueye Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Chi Liu
- Faculty of Data Science, City University of Macau, Macao SAR, China
| | - Wenyi Hu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia
| | - Lixia Luo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Danli Shi
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Jian Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Qiuxia Yin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Lei Zhang
- Clinical Medical Research Center, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210008, China.
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC, Australia.
- Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia.
| | - Xiaotong Han
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
| | - Mingguang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
- Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park, Shatin, Hong Kong.
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15
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Gonçalves MB, Nakayama LF, Ferraz D, Faber H, Korot E, Malerbi FK, Regatieri CV, Maia M, Celi LA, Keane PA, Belfort R. Image quality assessment of retinal fundus photographs for diabetic retinopathy in the machine learning era: a review. Eye (Lond) 2024; 38:426-433. [PMID: 37667028 PMCID: PMC10858054 DOI: 10.1038/s41433-023-02717-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/26/2023] [Accepted: 08/25/2023] [Indexed: 09/06/2023] Open
Abstract
This study aimed to evaluate the image quality assessment (IQA) and quality criteria employed in publicly available datasets for diabetic retinopathy (DR). A literature search strategy was used to identify relevant datasets, and 20 datasets were included in the analysis. Out of these, 12 datasets mentioned performing IQA, but only eight specified the quality criteria used. The reported quality criteria varied widely across datasets, and accessing the information was often challenging. The findings highlight the importance of IQA for AI model development while emphasizing the need for clear and accessible reporting of IQA information. The study suggests that automated quality assessments can be a valid alternative to manual labeling and emphasizes the importance of establishing quality standards based on population characteristics, clinical use, and research purposes. In conclusion, image quality assessment is important for AI model development; however, strict data quality standards must not limit data sharing. Given the importance of IQA for developing, validating, and implementing deep learning (DL) algorithms, it's recommended that this information be reported in a clear, specific, and accessible way whenever possible. Automated quality assessments are a valid alternative to the traditional manual labeling process, and quality standards should be determined according to population characteristics, clinical use, and research purpose.
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Affiliation(s)
- Mariana Batista Gonçalves
- Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil
- Instituto Paulista de Estudos e Pesquisas em Oftalmologia, IPEPO, Vision Institute, São Paulo, SP, Brazil
- NIHR Biomedical Research Centre for Ophthalmology, Moorfield Eye Hospital, NHS Foundation Trust, and UCL Institute of Ophthalmology, London, UK
| | - Luis Filipe Nakayama
- Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil.
- Massachusetts Institute of Technology, Laboratory for Computational Physiology, Cambridge, MA, USA.
| | - Daniel Ferraz
- Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil
- Instituto Paulista de Estudos e Pesquisas em Oftalmologia, IPEPO, Vision Institute, São Paulo, SP, Brazil
- NIHR Biomedical Research Centre for Ophthalmology, Moorfield Eye Hospital, NHS Foundation Trust, and UCL Institute of Ophthalmology, London, UK
| | - Hanna Faber
- Department of Ophthalmology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Ophthalmology, University of Tuebingen, Tuebingen, Germany
| | - Edward Korot
- Retina Specialists of Michigan, Grand Rapids, MI, USA
- Stanford University Byers Eye Institute Palo Alto, Palo Alto, CA, USA
| | | | | | - Mauricio Maia
- Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil
| | - Leo Anthony Celi
- Massachusetts Institute of Technology, Laboratory for Computational Physiology, Cambridge, MA, USA
- Harvard TH Chan School of Public Health, Department of Biostatistics, Boston, MA, USA
- Beth Israel Deaconess Medical Center, Department of Medicine, Boston, MA, USA
| | - Pearse A Keane
- NIHR Biomedical Research Centre for Ophthalmology, Moorfield Eye Hospital, NHS Foundation Trust, and UCL Institute of Ophthalmology, London, UK
| | - Rubens Belfort
- Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil
- Instituto Paulista de Estudos e Pesquisas em Oftalmologia, IPEPO, Vision Institute, São Paulo, SP, Brazil
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Skevas C, de Olaguer NP, Lleó A, Thiwa D, Schroeter U, Lopes IV, Mautone L, Linke SJ, Spitzer MS, Yap D, Xiao D. Implementing and evaluating a fully functional AI-enabled model for chronic eye disease screening in a real clinical environment. BMC Ophthalmol 2024; 24:51. [PMID: 38302908 PMCID: PMC10832120 DOI: 10.1186/s12886-024-03306-y] [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: 07/02/2023] [Accepted: 01/16/2024] [Indexed: 02/03/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has the potential to increase the affordability and accessibility of eye disease screening, especially with the recent approval of AI-based diabetic retinopathy (DR) screening programs in several countries. METHODS This study investigated the performance, feasibility, and user experience of a seamless hardware and software solution for screening chronic eye diseases in a real-world clinical environment in Germany. The solution integrated AI grading for DR, age-related macular degeneration (AMD), and glaucoma, along with specialist auditing and patient referral decision. The study comprised several components: (1) evaluating the entire system solution from recruitment to eye image capture and AI grading for DR, AMD, and glaucoma; (2) comparing specialist's grading results with AI grading results; (3) gathering user feedback on the solution. RESULTS A total of 231 patients were recruited, and their consent forms were obtained. The sensitivity, specificity, and area under the curve for DR grading were 100.00%, 80.10%, and 90.00%, respectively. For AMD grading, the values were 90.91%, 78.79%, and 85.00%, and for glaucoma grading, the values were 93.26%, 76.76%, and 85.00%. The analysis of all false positive cases across the three diseases and their comparison with the final referral decisions revealed that only 17 patients were falsely referred among the 231 patients. The efficacy analysis of the system demonstrated the effectiveness of the AI grading process in the study's testing environment. Clinical staff involved in using the system provided positive feedback on the disease screening process, particularly praising the seamless workflow from patient registration to image transmission and obtaining the final result. Results from a questionnaire completed by 12 participants indicated that most found the system easy, quick, and highly satisfactory. The study also revealed room for improvement in the AMD model, suggesting the need to enhance its training data. Furthermore, the performance of the glaucoma model grading could be improved by incorporating additional measures such as intraocular pressure. CONCLUSIONS The implementation of the AI-based approach for screening three chronic eye diseases proved effective in real-world settings, earning positive feedback on the usability of the integrated platform from both the screening staff and auditors. The auditing function has proven valuable for obtaining efficient second opinions from experts, pointing to its potential for enhancing remote screening capabilities. TRIAL REGISTRATION Institutional Review Board of the Hamburg Medical Chamber (Ethik-Kommission der Ärztekammer Hamburg): 2021-10574-BO-ff.
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Affiliation(s)
- Christos Skevas
- Department of Ophthalmology, University Medical Center Hamburg - Eppendorf, Martinistr. 52, 20249, Hamburg, Germany
| | | | - Albert Lleó
- TeleMedC GmbH, Raboisen 32, 20095, Hamburg, Germany
| | - David Thiwa
- Department of Otorhinolaryngology, University Medical Center Hamburg - Eppendorf, Martinistr. 52, 20249, Hamburg, Germany
| | - Ulrike Schroeter
- Department of Ophthalmology, University Medical Center Hamburg - Eppendorf, Martinistr. 52, 20249, Hamburg, Germany
| | - Inês Valente Lopes
- Department of Ophthalmology, University Medical Center Hamburg - Eppendorf, Martinistr. 52, 20249, Hamburg, Germany.
| | - Luca Mautone
- Department of Ophthalmology, University Medical Center Hamburg - Eppendorf, Martinistr. 52, 20249, Hamburg, Germany
| | - Stephan J Linke
- Zentrum Sehestaerke, Martinistraße 64, 20251, Hamburg, Germany
| | - Martin Stephan Spitzer
- Department of Ophthalmology, University Medical Center Hamburg - Eppendorf, Martinistr. 52, 20249, Hamburg, Germany
| | - Daniel Yap
- TeleMedC Pty Ltd, 61 Ubi Avenue 1, #06-11 UBPoint, Singapore, 40894, Singapore
| | - Di Xiao
- TeleMedC Pty Ltd, Brisbane Technology Park, Level 2, 1 Westlink Court, Darra, QLD 4076, Australia
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17
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Kawasaki R. How Can Artificial Intelligence Be Implemented Effectively in Diabetic Retinopathy Screening in Japan? MEDICINA (KAUNAS, LITHUANIA) 2024; 60:243. [PMID: 38399532 PMCID: PMC10890175 DOI: 10.3390/medicina60020243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 01/26/2024] [Accepted: 01/29/2024] [Indexed: 02/25/2024]
Abstract
Diabetic retinopathy (DR) is a major microvascular complication of diabetes, affecting a substantial portion of diabetic patients worldwide. Timely intervention is pivotal in mitigating the risk of blindness associated with DR, yet early detection remains a challenge due to the absence of early symptoms. Screening programs have emerged as a strategy to address this burden, and this paper delves into the role of artificial intelligence (AI) in advancing DR screening in Japan. There are two pathways for DR screening in Japan: a health screening pathway and a clinical referral path from physicians to ophthalmologists. AI technologies that realize automated image classification by applying deep learning are emerging. These technologies have exhibited substantial promise, achieving sensitivity and specificity levels exceeding 90% in prospective studies. Moreover, we introduce the potential of Generative AI and large language models (LLMs) to transform healthcare delivery, particularly in patient engagement, medical records, and decision support. Considering the use of AI in DR screening in Japan, we propose to follow a seven-step framework for systematic screening and emphasize the importance of integrating AI into a well-designed screening program. Automated scoring systems with AI enhance screening quality, but their effectiveness depends on their integration into the broader screening ecosystem. LLMs emerge as an important tool to fill gaps in the screening process, from personalized invitations to reporting results, facilitating a seamless and efficient system. However, it is essential to address concerns surrounding technical accuracy and governance before full-scale integration into the healthcare system. In conclusion, this review highlights the challenges in the current screening pathway and the potential for AI, particularly LLM, to revolutionize DR screening in Japan. The future direction will depend on leadership from ophthalmologists and stakeholders to address long-standing challenges in DR screening so that all people have access to accessible and effective screening.
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Affiliation(s)
- Ryo Kawasaki
- Division of Public Health, Department of Social Medicine, Graduate School of Medicine, Osaka University, Suita 565-0871, Japan;
- Artificial Intelligence Center for Medical Research and Application, Osaka University Hospital, Suita 565-0871, Japan
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18
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Kemp O, Bascaran C, Cartwright E, McQuillan L, Matthew N, Shillingford-Ricketts H, Zondervan M, Foster A, Burton M. Real-world evaluation of smartphone-based artificial intelligence to screen for diabetic retinopathy in Dominica: a clinical validation study. BMJ Open Ophthalmol 2023; 8:e001491. [PMID: 38135351 DOI: 10.1136/bmjophth-2023-001491] [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: 09/11/2023] [Accepted: 12/10/2023] [Indexed: 12/24/2023] Open
Abstract
OBJECTIVE Several artificial intelligence (AI) systems for diabetic retinopathy screening have been validated but there is limited evidence on their performance in real-world settings. This study aimed to assess the performance of an AI software deployed within the diabetic retinopathy screening programme in Dominica. METHODS AND ANALYSIS We conducted a prospective, cross-sectional clinical validation study. Patients with diabetes aged 18 years and above attending the diabetic retinopathy screening in primary care facilities in Dominica from 5 June to 3 July 2021 were enrolled.Grading was done at the point of care by the field grader, followed by counselling and referral to the eye clinic. Images were then graded by an AI system. Sensitivity, specificity with 95% CIs and area under the curve (AUC) were calculated for comparing the AI to field grader as gold standard. RESULTS A total of 587 participants were screened. The AI had a sensitivity and specificity for detecting referable diabetic retinopathy of 77.5% and 91.5% compared with the grader, for all participants, including ungradable images. The AUC was 0.8455. Excluding 52 participants deemed ungradable by the grader, the AI had a sensitivity and specificity of 81.4% and 91.5%, with an AUC of 0.9648. CONCLUSION This study provides evidence that AI has the potential to be deployed to assist a diabetic screening programme in a middle-income real-world setting and perform with reasonable accuracy compared with a specialist grader.
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Affiliation(s)
- Oliver Kemp
- London School of Hygiene and Tropical Medicine, London, UK
| | | | | | | | - Nanda Matthew
- Dominica China Friendship Hospital, Roseau, Dominica
| | | | | | - Allen Foster
- London School of Hygiene and Tropical Medicine, London, UK
| | - Matthew Burton
- London School of Hygiene and Tropical Medicine, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
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19
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Chen JS, Lin MC, Yiu G, Thorne C, Kulasa K, Stewart J, Nudleman E, Freeby M, Han MA, Baxter SL. Barriers to Implementation of Teleretinal Diabetic Retinopathy Screening Programs Across the University of California. Telemed J E Health 2023; 29:1810-1818. [PMID: 37256712 PMCID: PMC10714257 DOI: 10.1089/tmj.2022.0489] [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: 11/17/2022] [Revised: 12/17/2022] [Accepted: 12/19/2022] [Indexed: 06/02/2023] Open
Abstract
Aim: To describe barriers to implementation of diabetic retinopathy (DR) teleretinal screening programs and artificial intelligence (AI) integration at the University of California (UC). Methods: Institutional representatives from UC Los Angeles, San Diego, San Francisco, Irvine, and Davis were surveyed for the year of their program's initiation, active status at the time of survey (December 2021), number of primary care clinics involved, screening image quality, types of eye providers, image interpretation turnaround time, and billing codes used. Representatives were asked to rate perceptions toward barriers to teleretinal DR screening and AI implementation using a 5-point Likert scale. Results: Four UC campuses had active DR teleretinal screening programs at the time of survey and screened between 246 and 2,123 patients at 1-6 clinics per campus. Sites reported variation between poor-quality photos (<5% to 15%) and average image interpretation time (1-5 days). Patient education, resource availability, and infrastructural support were identified as barriers to DR teleretinal screening. Cost and integration into existing technology infrastructures were identified as barriers to AI integration in DR screening. Conclusions: Despite the potential to increase access to care, there remain several barriers to widespread implementation of DR teleretinal screening. More research is needed to develop best practices to overcome these barriers.
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Affiliation(s)
- Jimmy S. Chen
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California, USA
| | - Mark C. Lin
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California, USA
| | - Glenn Yiu
- Department of Ophthalmology and Vision Science, University of California Davis Health, Sacramento, California, USA
| | - Christine Thorne
- Department of Family Medicine and Public Health, University of California San Diego, La Jolla, California, USA
| | - Kristen Kulasa
- Department of Endocrinology, University of California San Diego, La Jolla, California, USA
| | - Jay Stewart
- Department of Ophthalmology, University of California, San Francisco, San Francisco, California, USA
- Department of Ophthalmology, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, California, USA
| | - Eric Nudleman
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California, USA
| | - Matthew Freeby
- Department of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Maria A. Han
- Department of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Sally L. Baxter
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California, USA
- Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, USA
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20
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Olvera-Barrios A, Rudnicka AR, Anderson J, Bolter L, Chambers R, Warwick AN, Welikala R, Fajtl J, Barman S, Remgnino P, Wu Y, Lee AY, Chew EY, Ferris FL, Hingorani A, Sofat R, A Egan C, Tufail A, Owen CG. Two-year recall for people with no diabetic retinopathy: a multi-ethnic population-based retrospective cohort study using real-world data to quantify the effect. Br J Ophthalmol 2023; 107:1839-1845. [PMID: 37875374 DOI: 10.1136/bjo-2023-324097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 08/19/2023] [Indexed: 10/26/2023]
Abstract
BACKGROUND/AIMS The English Diabetic Eye Screening Programme (DESP) offers people living with diabetes (PLD) annual screening. Less frequent screening has been advocated among PLD without diabetic retinopathy (DR), but evidence for each ethnic group is limited. We examined the potential effect of biennial versus annual screening on the detection of sight-threatening diabetic retinopathy (STDR) and proliferative diabetic retinopathy (PDR) among PLD without DR from a large urban multi-ethnic English DESP. METHODS PLD in North-East London DESP (January 2012 to December 2021) with no DR on two prior consecutive screening visits with up to 8 years of follow-up were examined. Annual STDR and PDR incidence rates, overall and by ethnicity, were quantified. Delays in identification of STDR and PDR events had 2-year screening intervals been used were determined. FINDINGS Among 82 782 PLD (37% white, 36% South Asian, and 16% black people), there were 1788 incident STDR cases over mean (SD) 4.3 (2.4) years (STDR rate 0.51, 95% CI 0.47 to 0.55 per 100-person-years). STDR incidence rates per 100-person-years by ethnicity were 0.55 (95% CI 0.48 to 0.62) for South Asian, 0.34 (95% CI 0.29 to 0.40) for white, and 0.77 (95% CI 0.65 to 0.90) for black people. Biennial screening would have delayed diagnosis by 1 year for 56.3% (1007/1788) with STDR and 43.6% (45/103) with PDR. Standardised cumulative rates of delayed STDR per 100 000 persons for each ethnic group were 1904 (95% CI 1683 to 2154) for black people, 1276 (95% CI 1153 to 1412) for South Asian people, and 844 (95% CI 745 to 955) for white people. INTERPRETATION Biennial screening would have delayed detection of some STDR and PDR by 1 year, especially among those of black ethnic origin, leading to healthcare inequalities.
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Affiliation(s)
- Abraham Olvera-Barrios
- Institute of Ophthalmology, University College London, London, UK, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Alicja R Rudnicka
- Population Health Research Institute, St George's, University of London, London, UK
| | - John Anderson
- Homerton Healthcare NHS Foundation Trust, London, UK
| | - Louis Bolter
- Homerton Healthcare NHS Foundation Trust, London, UK
| | - Ryan Chambers
- Homerton Healthcare NHS Foundation Trust, London, UK
| | - Alasdair N Warwick
- Institute of Cardiovascular Science, University College London, London, UK
| | - Roshan Welikala
- Department of Computer Science, Kingston University, Kingston-Upon-Thames, UK
| | - Jiri Fajtl
- Department of Computer Science, Kingston University, Kingston-Upon-Thames, UK
| | - Sarah Barman
- Department of Computer Science, Kingston University, Kingston-Upon-Thames, UK
| | - Paolo Remgnino
- Department of Computer Science, University of Durham, Durham, UK
| | - Yue Wu
- Department of Ophthalmology, University of Washington, Seattle, Washington, USA
| | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington, USA
| | - Emily Y Chew
- Division of Epidemiology and Clinical Applications, National Institutes of Health, Bethesda, Maryland, USA
| | | | - Aroon Hingorani
- Institute of Cardiovascular Science, University College London, London, UK
| | - Reecha Sofat
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
| | - Catherine A Egan
- Institute of Ophthalmology, University College London, London, UK, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Adnan Tufail
- Institute of Ophthalmology, University College London, London, UK, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Christopher G Owen
- Population Health Research Institute, St George's, University of London, London, UK
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21
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Willis K, Chaudhry UAR, Chandrasekaran L, Wahlich C, Olvera-Barrios A, Chambers R, Bolter L, Anderson J, Barman SA, Fajtl J, Welikala R, Egan C, Tufail A, Owen CG, Rudnicka A. What are the perceptions and concerns of people living with diabetes and National Health Service staff around the potential implementation of AI-assisted screening for diabetic eye disease? Development and validation of a survey for use in a secondary care screening setting. BMJ Open 2023; 13:e075558. [PMID: 37968006 PMCID: PMC10660949 DOI: 10.1136/bmjopen-2023-075558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 09/05/2023] [Indexed: 11/17/2023] Open
Abstract
INTRODUCTION The English National Health Service (NHS) Diabetic Eye Screening Programme (DESP) performs around 2.3 million eye screening appointments annually, generating approximately 13 million retinal images that are graded by humans for the presence or severity of diabetic retinopathy. Previous research has shown that automated retinal image analysis systems, including artificial intelligence (AI), can identify images with no disease from those with diabetic retinopathy as safely and effectively as human graders, and could significantly reduce the workload for human graders. Some algorithms can also determine the level of severity of the retinopathy with similar performance to humans. There is a need to examine perceptions and concerns surrounding AI-assisted eye-screening among people living with diabetes and NHS staff, if AI was to be introduced into the DESP, to identify factors that may influence acceptance of this technology. METHODS AND ANALYSIS People living with diabetes and staff from the North East London (NEL) NHS DESP were invited to participate in two respective focus groups to codesign two online surveys exploring their perceptions and concerns around the potential introduction of AI-assisted screening.Focus group participants were representative of the local population in terms of ages and ethnicity. Participants' feedback was taken into consideration to update surveys which were circulated for further feedback. Surveys will be piloted at the NEL DESP and followed by semistructured interviews to assess accessibility, usability and to validate the surveys.Validated surveys will be distributed by other NHS DESP sites, and also via patient groups on social media, relevant charities and the British Association of Retinal Screeners. Post-survey evaluative interviews will be undertaken among those who consent to participate in further research. ETHICS AND DISSEMINATION Ethical approval has been obtained by the NHS Research Ethics Committee (IRAS ID: 316631). Survey results will be shared and discussed with focus groups to facilitate preparation of findings for publication and to inform codesign of outreach activities to address concerns and perceptions identified.
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Affiliation(s)
- Kathryn Willis
- Population Health Research Institute, St George's University of London, London, UK
| | - Umar A R Chaudhry
- Population Health Research Institute, St George's University of London, London, UK
| | | | - Charlotte Wahlich
- Population Health Research Institute, St George's University of London, London, UK
| | - Abraham Olvera-Barrios
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Ryan Chambers
- Diabetes and Endocrinolgy, Homerton Healthcare NHS Foundation Trust, London, UK
| | - Louis Bolter
- Diabetes and Endocrinolgy, Homerton Healthcare NHS Foundation Trust, London, UK
| | - John Anderson
- Diabetes and Endocrinolgy, Homerton Healthcare NHS Foundation Trust, London, UK
| | - S A Barman
- School of Computer Science and Mathematics, Kingston University London, London, UK
| | - Jiri Fajtl
- School of Computer Science and Mathematics, Kingston University London, London, UK
| | - Roshan Welikala
- School of Computer Science and Mathematics, Kingston University London, London, UK
| | - Catherine Egan
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Adnan Tufail
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Christopher G Owen
- Population Health Research Institute, St George's University of London, London, UK
| | - Alicja Rudnicka
- Population Health Research Institute, St George's University of London, London, UK
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22
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Korot E, Gonçalves MB, Huemer J, Beqiri S, Khalid H, Kelly M, Chia M, Mathijs E, Struyven R, Moussa M, Keane PA. Clinician-Driven AI: Code-Free Self-Training on Public Data for Diabetic Retinopathy Referral. JAMA Ophthalmol 2023; 141:1029-1036. [PMID: 37856110 PMCID: PMC10587830 DOI: 10.1001/jamaophthalmol.2023.4508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 08/23/2023] [Indexed: 10/20/2023]
Abstract
Importance Democratizing artificial intelligence (AI) enables model development by clinicians with a lack of coding expertise, powerful computing resources, and large, well-labeled data sets. Objective To determine whether resource-constrained clinicians can use self-training via automated machine learning (ML) and public data sets to design high-performing diabetic retinopathy classification models. Design, Setting, and Participants This diagnostic quality improvement study was conducted from January 1, 2021, to December 31, 2021. A self-training method without coding was used on 2 public data sets with retinal images from patients in France (Messidor-2 [n = 1748]) and the UK and US (EyePACS [n = 58 689]) and externally validated on 1 data set with retinal images from patients of a private Egyptian medical retina clinic (Egypt [n = 210]). An AI model was trained to classify referable diabetic retinopathy as an exemplar use case. Messidor-2 images were assigned adjudicated labels available on Kaggle; 4 images were deemed ungradable and excluded, leaving 1744 images. A total of 300 images randomly selected from the EyePACS data set were independently relabeled by 3 blinded retina specialists using the International Classification of Diabetic Retinopathy protocol for diabetic retinopathy grade and diabetic macular edema presence; 19 images were deemed ungradable, leaving 281 images. Data analysis was performed from February 1 to February 28, 2021. Exposures Using public data sets, a teacher model was trained with labeled images using supervised learning. Next, the resulting predictions, termed pseudolabels, were used on an unlabeled public data set. Finally, a student model was trained with the existing labeled images and the additional pseudolabeled images. Main Outcomes and Measures The analyzed metrics for the models included the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, and F1 score. The Fisher exact test was performed, and 2-tailed P values were calculated for failure case analysis. Results For the internal validation data sets, AUROC values for performance ranged from 0.886 to 0.939 for the teacher model and from 0.916 to 0.951 for the student model. For external validation of automated ML model performance, AUROC values and accuracy were 0.964 and 93.3% for the teacher model, 0.950 and 96.7% for the student model, and 0.890 and 94.3% for the manually coded bespoke model, respectively. Conclusions and Relevance These findings suggest that self-training using automated ML is an effective method to increase both model performance and generalizability while decreasing the need for costly expert labeling. This approach advances the democratization of AI by enabling clinicians without coding expertise or access to large, well-labeled private data sets to develop their own AI models.
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Affiliation(s)
- Edward Korot
- Retina Specialists of Michigan, Grand Rapids
- Moorfields Eye Hospital, London, United Kingdom
- Stanford University Byers Eye Institute, Palo Alto, California
| | - Mariana Batista Gonçalves
- Moorfields Eye Hospital, London, United Kingdom
- Federal University of Sao Paulo, Sao Paulo, Brazil
- Instituto da Visão, Sao Paulo, Brazil
| | | | - Sara Beqiri
- Moorfields Eye Hospital, London, United Kingdom
- University College London Medical School, London, United Kingdom
| | - Hagar Khalid
- Moorfields Eye Hospital, London, United Kingdom
- Ophthalmology Department, Faculty of Medicine, Tanta University Hospital, Tanta, Gharbia, Egypt
| | - Madeline Kelly
- Moorfields Eye Hospital, London, United Kingdom
- University College London Medical School, London, United Kingdom
- UCL Centre for Medical Image Computing, London, United Kingdom
| | - Mark Chia
- Moorfields Eye Hospital, London, United Kingdom
| | - Emily Mathijs
- Michigan State University College of Osteopathic Medicine, East Lansing
| | | | - Magdy Moussa
- Ophthalmology Department, Faculty of Medicine, Tanta University Hospital, Tanta, Gharbia, Egypt
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23
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Rajesh AE, Davidson OQ, Lee CS, Lee AY. Artificial Intelligence and Diabetic Retinopathy: AI Framework, Prospective Studies, Head-to-head Validation, and Cost-effectiveness. Diabetes Care 2023; 46:1728-1739. [PMID: 37729502 PMCID: PMC10516248 DOI: 10.2337/dci23-0032] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 07/15/2023] [Indexed: 09/22/2023]
Abstract
Current guidelines recommend that individuals with diabetes receive yearly eye exams for detection of referable diabetic retinopathy (DR), one of the leading causes of new-onset blindness. For addressing the immense screening burden, artificial intelligence (AI) algorithms have been developed to autonomously screen for DR from fundus photography without human input. Over the last 10 years, many AI algorithms have achieved good sensitivity and specificity (>85%) for detection of referable DR compared with human graders; however, many questions still remain. In this narrative review on AI in DR screening, we discuss key concepts in AI algorithm development as a background for understanding the algorithms. We present the AI algorithms that have been prospectively validated against human graders and demonstrate the variability of reference standards and cohort demographics. We review the limited head-to-head validation studies where investigators attempt to directly compare the available algorithms. Next, we discuss the literature regarding cost-effectiveness, equity and bias, and medicolegal considerations, all of which play a role in the implementation of these AI algorithms in clinical practice. Lastly, we highlight ongoing efforts to bridge gaps in AI model data sets to pursue equitable development and delivery.
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Affiliation(s)
- Anand E. Rajesh
- Department of Ophthalmology, University of Washington, Seattle, WA
- Roger H. and Angie Karalis Johnson Retina Center, Seattle, WA
| | - Oliver Q. Davidson
- Department of Ophthalmology, University of Washington, Seattle, WA
- Roger H. and Angie Karalis Johnson Retina Center, Seattle, WA
| | - Cecilia S. Lee
- Department of Ophthalmology, University of Washington, Seattle, WA
- Roger H. and Angie Karalis Johnson Retina Center, Seattle, WA
| | - Aaron Y. Lee
- Department of Ophthalmology, University of Washington, Seattle, WA
- Roger H. and Angie Karalis Johnson Retina Center, Seattle, WA
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24
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Fleming AD, Mellor J, McGurnaghan SJ, Blackbourn LAK, Goatman KA, Styles C, Storkey AJ, McKeigue PM, Colhoun HM. Deep learning detection of diabetic retinopathy in Scotland's diabetic eye screening programme. Br J Ophthalmol 2023:bjo-2023-323395. [PMID: 37704266 DOI: 10.1136/bjo-2023-323395] [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: 02/09/2023] [Accepted: 08/17/2023] [Indexed: 09/15/2023]
Abstract
BACKGROUND/AIMS Support vector machine-based automated grading (known as iGradingM) has been shown to be safe, cost-effective and robust in the diabetic retinopathy (DR) screening (DES) programme in Scotland. It triages screening episodes as gradable with no DR versus manual grading required. The study aim was to develop a deep learning-based autograder using images and gradings from DES and to compare its performance with that of iGradingM. METHODS Retinal images, quality assurance (QA) data and routine DR grades were obtained from national datasets in 179 944 patients for years 2006-2016. QA grades were available for 744 images. We developed a deep learning-based algorithm to detect whether either eye contained ungradable images or any DR. The sensitivity and specificity were evaluated against consensus QA grades and routine grades. RESULTS Images used in QA which were ungradable or with DR were detected by deep learning with better specificity compared with manual graders (p<0.001) and with iGradingM (p<0.001) at the same sensitivities. Any DR according to the DES final grade was detected with 89.19% (270 392/303 154) sensitivity and 77.41% (500 945/647 158) specificity. Observable disease and referable disease were detected with sensitivities of 96.58% (16 613/17 201) and 98.48% (22 600/22 948), respectively. Overall, 43.84% of screening episodes would require manual grading. CONCLUSION A deep learning-based system for DR grading was evaluated in QA data and images from 11 years in 50% of people attending a national DR screening programme. The system could reduce the manual grading workload at the same sensitivity compared with the current automated grading system.
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Affiliation(s)
- Alan D Fleming
- The Institute of Genetics and Cancer, University of Edinburgh Western General Hospital, Edinburgh, UK
| | - Joseph Mellor
- Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Stuart J McGurnaghan
- The Institute of Genetics and Cancer, University of Edinburgh Western General Hospital, Edinburgh, UK
| | - Luke A K Blackbourn
- The Institute of Genetics and Cancer, University of Edinburgh Western General Hospital, Edinburgh, UK
| | | | | | - Amos J Storkey
- School of Informatics, The University of Edinburgh, Edinburgh, UK
| | | | - Helen M Colhoun
- The Institute of Genetics and Cancer, University of Edinburgh Western General Hospital, Edinburgh, UK
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25
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Zhelev Z, Peters J, Rogers M, Allen M, Kijauskaite G, Seedat F, Wilkinson E, Hyde C. Test accuracy of artificial intelligence-based grading of fundus images in diabetic retinopathy screening: A systematic review. J Med Screen 2023; 30:97-112. [PMID: 36617971 PMCID: PMC10399100 DOI: 10.1177/09691413221144382] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 11/14/2022] [Accepted: 11/18/2022] [Indexed: 01/10/2023]
Abstract
OBJECTIVES To systematically review the accuracy of artificial intelligence (AI)-based systems for grading of fundus images in diabetic retinopathy (DR) screening. METHODS We searched MEDLINE, EMBASE, the Cochrane Library and the ClinicalTrials.gov from 1st January 2000 to 27th August 2021. Accuracy studies published in English were included if they met the pre-specified inclusion criteria. Selection of studies for inclusion, data extraction and quality assessment were conducted by one author with a second reviewer independently screening and checking 20% of titles. Results were analysed narratively. RESULTS Forty-three studies evaluating 15 deep learning (DL) and 4 machine learning (ML) systems were included. Nine systems were evaluated in a single study each. Most studies were judged to be at high or unclear risk of bias in at least one QUADAS-2 domain. Sensitivity for referable DR and higher grades was ≥85% while specificity varied and was <80% for all ML systems and in 6/31 studies evaluating DL systems. Studies reported high accuracy for detection of ungradable images, but the latter were analysed and reported inconsistently. Seven studies reported that AI was more sensitive but less specific than human graders. CONCLUSIONS AI-based systems are more sensitive than human graders and could be safe to use in clinical practice but have variable specificity. However, for many systems evidence is limited, at high risk of bias and may not generalise across settings. Therefore, pre-implementation assessment in the target clinical pathway is essential to obtain reliable and applicable accuracy estimates.
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Affiliation(s)
- Zhivko Zhelev
- Exeter Test Group, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Jaime Peters
- Exeter Test Group, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Morwenna Rogers
- NIHR ARC South West Peninsula (PenARC), University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Michael Allen
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | | | | | | | - Christopher Hyde
- Exeter Test Group, University of Exeter Medical School, University of Exeter, Exeter, UK
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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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Elmahdy M, Sebro R. A snapshot of artificial intelligence research 2019-2021: is it replacing or assisting physicians? J Am Med Inform Assoc 2023; 30:1552-1557. [PMID: 37279884 PMCID: PMC10436151 DOI: 10.1093/jamia/ocad094] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/09/2023] [Indexed: 06/08/2023] Open
Abstract
Artificial intelligence (AI) has the potential to be a disruptive technology in healthcare. Recently, there is increased speculation that AI may be used to replace healthcare providers in the future. To answer this question, we reviewed over 21 000 articles published in medical specialty journals between 2019 and 2021 to evaluate whether these AI models were intended to assist or replace healthcare providers. We also evaluated whether all Food and Drug Administration (FDA)-approved AI models were used to assist or replace healthcare providers. We find that most AI models published in this time period were intended to assist rather than replace healthcare providers, and that most of the published AI models performed tasks that could not be done by healthcare providers.
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Affiliation(s)
- Mahmoud Elmahdy
- Department of Radiology, Mayo Clinic, Jacksonville, Florida, USA
| | - Ronnie Sebro
- Department of Radiology, Mayo Clinic, Jacksonville, Florida, USA
- Center for Augmented Intelligence, Mayo Clinic, Jacksonville, Florida, USA
- Department of Orthopedic Surgery, Mayo Clinic, Jacksonville, Florida, USA
- Department of Biostatistics, Center for Quantitative Health Sciences, Jacksonville, Florida, USA
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28
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Matta S, Lamard M, Conze PH, Le Guilcher A, Lecat C, Carette R, Basset F, Massin P, Rottier JB, Cochener B, Quellec G. Towards population-independent, multi-disease detection in fundus photographs. Sci Rep 2023; 13:11493. [PMID: 37460629 DOI: 10.1038/s41598-023-38610-y] [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: 06/21/2022] [Accepted: 07/11/2023] [Indexed: 07/20/2023] Open
Abstract
Independent validation studies of automatic diabetic retinopathy screening systems have recently shown a drop of screening performance on external data. Beyond diabetic retinopathy, this study investigates the generalizability of deep learning (DL) algorithms for screening various ocular anomalies in fundus photographs, across heterogeneous populations and imaging protocols. The following datasets are considered: OPHDIAT (France, diabetic population), OphtaMaine (France, general population), RIADD (India, general population) and ODIR (China, general population). Two multi-disease DL algorithms were developed: a Single-Dataset (SD) network, trained on the largest dataset (OPHDIAT), and a Multiple-Dataset (MD) network, trained on multiple datasets simultaneously. To assess their generalizability, both algorithms were evaluated whenever training and test data originate from overlapping datasets or from disjoint datasets. The SD network achieved a mean per-disease area under the receiver operating characteristic curve (mAUC) of 0.9571 on OPHDIAT. However, it generalized poorly to the other three datasets (mAUC < 0.9). When all four datasets were involved in training, the MD network significantly outperformed the SD network (p = 0.0058), indicating improved generality. However, in leave-one-dataset-out experiments, performance of the MD network was significantly lower on populations unseen during training than on populations involved in training (p < 0.0001), indicating imperfect generalizability.
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Affiliation(s)
- Sarah Matta
- Université de Bretagne Occidentale, Brest, Bretagne, France.
- INSERM, UMR 1101, Brest, F-29 200, France.
| | - Mathieu Lamard
- Université de Bretagne Occidentale, Brest, Bretagne, France
- INSERM, UMR 1101, Brest, F-29 200, France
| | - Pierre-Henri Conze
- INSERM, UMR 1101, Brest, F-29 200, France
- IMT Atlantique, Brest, F-29200, France
| | | | - Clément Lecat
- Evolucare Technologies, Villers-Bretonneux, F-80800, France
| | | | - Fabien Basset
- Evolucare Technologies, Villers-Bretonneux, F-80800, France
| | - Pascale Massin
- Service d'Ophtalmologie, Hôpital Lariboisière, APHP, Paris, F-75475, France
| | - Jean-Bernard Rottier
- Bâtiment de consultation porte 14 Pôle Santé Sud CMCM, 28 Rue de Guetteloup, Le Mans, F-72100, France
| | - Béatrice Cochener
- Université de Bretagne Occidentale, Brest, Bretagne, France
- INSERM, UMR 1101, Brest, F-29 200, France
- Service d'Ophtalmologie, CHRU Brest, Brest, F-29200, France
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Li Z, Wang L, Wu X, Jiang J, Qiang W, Xie H, Zhou H, Wu S, Shao Y, Chen W. Artificial intelligence in ophthalmology: The path to the real-world clinic. Cell Rep Med 2023:101095. [PMID: 37385253 PMCID: PMC10394169 DOI: 10.1016/j.xcrm.2023.101095] [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: 11/03/2022] [Revised: 04/17/2023] [Accepted: 06/07/2023] [Indexed: 07/01/2023]
Abstract
Artificial intelligence (AI) has great potential to transform healthcare by enhancing the workflow and productivity of clinicians, enabling existing staff to serve more patients, improving patient outcomes, and reducing health disparities. In the field of ophthalmology, AI systems have shown performance comparable with or even better than experienced ophthalmologists in tasks such as diabetic retinopathy detection and grading. However, despite these quite good results, very few AI systems have been deployed in real-world clinical settings, challenging the true value of these systems. This review provides an overview of the current main AI applications in ophthalmology, describes the challenges that need to be overcome prior to clinical implementation of the AI systems, and discusses the strategies that may pave the way to the clinical translation of these systems.
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Affiliation(s)
- Zhongwen Li
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China; School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.
| | - Lei Wang
- School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Xuefang Wu
- Guizhou Provincial People's Hospital, Guizhou University, Guiyang 550002, China
| | - Jiewei Jiang
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
| | - Wei Qiang
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China
| | - He Xie
- School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Hongjian Zhou
- Department of Computer Science, University of Oxford, Oxford, Oxfordshire OX1 2JD, UK
| | - Shanjun Wu
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China
| | - Yi Shao
- Department of Ophthalmology, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China.
| | - Wei Chen
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China; School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.
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Meredith S, van Grinsven M, Engelberts J, Clarke D, Prior V, Vodrey J, Hammond A, Muhammed R, Kirby P. Performance of an artificial intelligence automated system for diabetic eye screening in a large English population. Diabet Med 2023; 40:e15055. [PMID: 36719266 DOI: 10.1111/dme.15055] [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: 10/11/2022] [Revised: 01/20/2023] [Accepted: 01/30/2023] [Indexed: 02/01/2023]
Abstract
AIMS A diabetic eye screening programme has huge value in reducing avoidable sight loss by identifying diabetic retinopathy at a stage when it can be treated. Artificial intelligence automated systems can be used for diabetic eye screening but are not employed in the national English Diabetic Eye Screening Programme. The aim was to report the performance of a commercially available deep-learning artificial intelligence software in a large English population. METHODS 9817 anonymised image sets from 10,000 consecutive diabetic eye screening episodes were presented to an artificial intelligence software. The sensitivity and specificity of the artificial intelligence system for detecting diabetic retinopathy were determined using the diabetic eye screening programme manual grade according to national protocols as the reference standard. RESULTS For no diabetic retinopathy versus any diabetic retinopathy, the sensitivity of the artificial intelligence grading system was 69.7% and specificity 92.2%. The performance of the artificial intelligence system was superior for no or mild diabetic retinopathy versus significant or referrable diabetic retinopathy with a sensitivity of 95.4% and specificity of 92.0%. No cases were identified in which the artificial intelligence grade had missed significant diabetic retinopathy. CONCLUSION The performance of a commercially available deep-learning artificial intelligence system for identifying diabetic retinopathy in an English national Diabetic Eye Screening Programme is presented. Using the pre-defined settings artificial intelligence performance was highest when identifying diabetic retinopathy which requires an action by the screening programme.
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Affiliation(s)
| | | | | | | | | | - Jo Vodrey
- InHealth Intelligence Ltd, Winsford, UK
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Joseph S, Rajan RP, Sundar B, Venkatachalam S, Kempen JH, Kim R. Validation of diagnostic accuracy of retinal image grading by trained non-ophthalmologist grader for detecting diabetic retinopathy and diabetic macular edema. Eye (Lond) 2023; 37:1577-1582. [PMID: 35906419 PMCID: PMC10220051 DOI: 10.1038/s41433-022-02190-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 06/22/2022] [Accepted: 07/15/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE To validate the fundus image grading results by a trained grader (Non-ophthalmologist) and an ophthalmologist grader for detecting diabetic retinopathy (DR) and diabetic macular oedema (DMO) against fundus examination by a retina specialist (gold standard). METHODS A prospective diagnostic accuracy study was conducted using 2002 non-mydriatic colour fundus images from 1001 patients aged ≥40 years. Using the Aravind Diabetic Retinopathy Evaluation Software (ADRES) images were graded by both a trained non-ophthalmologist grader (grader-1) and an ophthalmologist (grader-2). Sensitivity, specificity, positive predictive value and negative predictive value were calculated for grader-1 and grader-2 against the grading results by an independent retina specialist who performed dilated fundus examination for every study participant. RESULTS Out of 1001 patients included, 42% were women and the mean ± (SD) age was 55.8 (8.39) years. For moderate or worse DR, the sensitivity and specificity for grading by grader-1 with respect to the gold standard was 66.9% and 91.0% respectively and the same for the ophthalmologist was 83.6% and 80.3% respectively. For referable DMO, grader-1 and grader-2 had a sensitivity of 74.6% and 85.6% respectively and a specificity of 83.7% and 79.8% respectively. CONCLUSIONS Our results demonstrate good level of accuracy for the fundus image grading performed by a trained non-ophthalmologist which was comparable with the grading by an ophthalmologist. Engaging trained non-ophthalmologists potentially can enhance the efficiency of DR diagnosis using fundus images. Further study with multiple non-ophthalmologist graders is needed to verify the results and strategies to improve agreement for DMO diagnosis are needed.
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Affiliation(s)
- Sanil Joseph
- Lions Aravind Institute of Community Ophthalmology, Aravind Eye Care System, Madurai, India
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Vic, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Vic, Australia
| | - Renu P Rajan
- Aravind Eye Hospital and Postgraduate Institute of Ophthalmology, Madurai, India
| | - Balagiri Sundar
- Aravind Eye Hospital and Postgraduate Institute of Ophthalmology, Madurai, India
| | | | - John H Kempen
- Department of Ophthalmology, , Massachusetts Eye and Ear and Harvard Medical School; Schepens Eye Research Institute, Boston, MA, USA
- MCM Eye Unit, MyungSung Christian Medical Center (MCM) Multispecialty Hospital and MyungSung Medical School, Addis Ababa, Ethiopia
- Department of Ophthalmology, Faculty of Medicine, Addis Ababa University, Addis Ababa, Ethiopia
| | - Ramasamy Kim
- Aravind Eye Hospital and Postgraduate Institute of Ophthalmology, Madurai, India.
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Vaghefi E, Yang S, Xie L, Han D, Yap A, Schmeidel O, Marshall J, Squirrell D. A multi-centre prospective evaluation of THEIA™ to detect diabetic retinopathy (DR) and diabetic macular oedema (DMO) in the New Zealand screening program. Eye (Lond) 2023; 37:1683-1689. [PMID: 36057664 PMCID: PMC10219993 DOI: 10.1038/s41433-022-02217-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 07/09/2022] [Accepted: 08/12/2022] [Indexed: 12/23/2022] Open
Abstract
PURPOSE To validate the potential application of THEIA™ as clinical decision making assistant in a national screening program. METHODS A total of 900 patients were recruited from either an urban large eye hospital, or a semi-rural optometrist led screening provider, as they were attending their appointment as part of New Zealand Diabetic Eye Screening Programme. The de-identified images were independently graded by three senior specialists, and final results were aggregated using New Zealand grading scheme, which was then converted to referable/non-referable and Healthy/mild/more than mild/sight threatening categories. RESULTS THEIA™ managed to grade all images obtained during the study. Comparing the adjudicated images from the specialist grading team, "ground truth", with the grading by the AI platform in detecting "sight threatening" disease, at the patient level THEIA™ achieved 100% imageability, 100% [98.49-100.00%] sensitivity and [97.02-99.16%] specificity, and negative predictive value of 100%. In other words, THEIA™ did not miss any patients with "more than mild" or "sight threatening" disease. The level of agreement between the clinicians and the aggregated results was (k value: 0.9881, 0.9557, and 0.9175), and the level of agreement between THEIA™ and the aggregated labels was (k value: 0.9515). CONCLUSION This multi-centre prospective trial showed that THEIA™ did not miss referable disease when screening for diabetic retinopathy and maculopathy. It also had a very high level of granularity in reporting the disease level. As THEIA™ has been tested on a variety of cameras, operating in a range of clinics (rural/urban, ophthalmologist-led\optometrist-led), we believe that it will be a suitable addition to a public diabetic screening program.
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Affiliation(s)
- Ehsan Vaghefi
- Toku Eyes®, Auckland, New Zealand.
- School of Optometry and Vision Science, The University of Auckland, Auckland, New Zealand.
| | | | - Li Xie
- Toku Eyes®, Auckland, New Zealand
| | | | - Aaron Yap
- Department of Ophthalmology, The University of Auckland, Auckland, New Zealand
| | - Ole Schmeidel
- Department of Diabetes, Auckland District Health Board, Auckland, New Zealand
| | - John Marshall
- Institute of Ophthalmology, University College of London, London, UK
| | - David Squirrell
- Toku Eyes®, Auckland, New Zealand
- Department of Ophthalmology, The University of Auckland, Auckland, New Zealand
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Srisubat A, Kittrongsiri K, Sangroongruangsri S, Khemvaranan C, Shreibati JB, Ching J, Hernandez J, Tiwari R, Hersch F, Liu Y, Hanutsaha P, Ruamviboonsuk V, Turongkaravee S, Raman R, Ruamviboonsuk P. Cost-Utility Analysis of Deep Learning and Trained Human Graders for Diabetic Retinopathy Screening in a Nationwide Program. Ophthalmol Ther 2023; 12:1339-1357. [PMID: 36841895 PMCID: PMC10011252 DOI: 10.1007/s40123-023-00688-y] [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: 12/30/2022] [Accepted: 02/10/2023] [Indexed: 02/27/2023] Open
Abstract
INTRODUCTION Deep learning (DL) for screening diabetic retinopathy (DR) has the potential to address limited healthcare resources by enabling expanded access to healthcare. However, there is still limited health economic evaluation, particularly in low- and middle-income countries, on this subject to aid decision-making for DL adoption. METHODS In the context of a middle-income country (MIC), using Thailand as a model, we constructed a decision tree-Markov hybrid model to estimate lifetime costs and outcomes of Thailand's national DR screening program via DL and trained human graders (HG). We calculated the incremental cost-effectiveness ratio (ICER) between the two strategies. Sensitivity analyses were performed to probe the influence of modeling parameters. RESULTS From a societal perspective, screening with DL was associated with a reduction in costs of ~ US$ 2.70, similar quality-adjusted life-years (QALY) of + 0.0043, and an incremental net monetary benefit of ~ US$ 24.10 in the base case. In sensitivity analysis, DL remained cost-effective even with a price increase from US$ 1.00 to US$ 4.00 per patient at a Thai willingness-to-pay threshold of ~ US$ 4.997 per QALY gained. When further incorporating recent findings suggesting improved compliance to treatment referral with DL, our analysis models effectiveness benefits of ~ US$ 20 to US$ 50 depending on compliance. CONCLUSION DR screening using DL in an MIC using Thailand as a model may result in societal cost-savings and similar health outcomes compared with HG. This study may provide an economic rationale to expand DL-based DR screening in MICs as an alternative solution for limited availability of skilled human resources for primary screening, particularly in MICs with similar prevalence of diabetes and low compliance to referrals for treatment.
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Affiliation(s)
- Attasit Srisubat
- Department of Medical Services, Ministry of Public Health, Nonthaburi, Thailand
| | - Kankamon Kittrongsiri
- Social, Economic and Administrative Pharmacy (SEAP) Graduate Program, Faculty of Pharmacy, Mahidol University, Bangkok, Thailand
| | - Sermsiri Sangroongruangsri
- Social and Administrative Pharmacy Division, Department of Pharmacy, Faculty of Pharmacy, Mahidol University, Bangkok, Thailand.
| | - Chalida Khemvaranan
- Department of Research and Technology Assessment, Lerdsin Hospital, Bangkok, Thailand
| | | | | | | | | | | | - Yun Liu
- Google LLC, Mountain View, CA, USA
| | - Prut Hanutsaha
- Department of Ophthalmology, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | | | - Saowalak Turongkaravee
- Social and Administrative Pharmacy Division, Department of Pharmacy, Faculty of Pharmacy, Mahidol University, Bangkok, Thailand
| | - Rajiv Raman
- Sri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India
| | - Paisan Ruamviboonsuk
- Department of Ophthalmology, College of Medicine, Rajavithi Hospital, Rangsit University, Bangkok, Thailand.
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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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Meng Y, Preston FG, Ferdousi M, Azmi S, Petropoulos IN, Kaye S, Malik RA, Alam U, Zheng Y. Artificial Intelligence Based Analysis of Corneal Confocal Microscopy Images for Diagnosing Peripheral Neuropathy: A Binary Classification Model. J Clin Med 2023; 12:1284. [PMID: 36835819 PMCID: PMC9963824 DOI: 10.3390/jcm12041284] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/27/2023] [Accepted: 02/02/2023] [Indexed: 02/08/2023] Open
Abstract
Diabetic peripheral neuropathy (DPN) is the leading cause of neuropathy worldwide resulting in excess morbidity and mortality. We aimed to develop an artificial intelligence deep learning algorithm to classify the presence or absence of peripheral neuropathy (PN) in participants with diabetes or pre-diabetes using corneal confocal microscopy (CCM) images of the sub-basal nerve plexus. A modified ResNet-50 model was trained to perform the binary classification of PN (PN+) versus no PN (PN-) based on the Toronto consensus criteria. A dataset of 279 participants (149 PN-, 130 PN+) was used to train (n = 200), validate (n = 18), and test (n = 61) the algorithm, utilizing one image per participant. The dataset consisted of participants with type 1 diabetes (n = 88), type 2 diabetes (n = 141), and pre-diabetes (n = 50). The algorithm was evaluated using diagnostic performance metrics and attribution-based methods (gradient-weighted class activation mapping (Grad-CAM) and Guided Grad-CAM). In detecting PN+, the AI-based DLA achieved a sensitivity of 0.91 (95%CI: 0.79-1.0), a specificity of 0.93 (95%CI: 0.83-1.0), and an area under the curve (AUC) of 0.95 (95%CI: 0.83-0.99). Our deep learning algorithm demonstrates excellent results for the diagnosis of PN using CCM. A large-scale prospective real-world study is required to validate its diagnostic efficacy prior to implementation in screening and diagnostic programmes.
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Affiliation(s)
- Yanda Meng
- Department of Eye and Vision Science, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L7 8TX, UK
| | - Frank George Preston
- Department of Cardiovascular & Metabolic Medicine, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L7 8TX, UK
| | - Maryam Ferdousi
- Institute of Cardiovascular Science, University of Manchester, Manchester M13 9PL, UK
- Manchester Diabetes Centre, Manchester Foundation Trust, Manchester M13 0JE, UK
| | - Shazli Azmi
- Institute of Cardiovascular Science, University of Manchester, Manchester M13 9PL, UK
- Manchester Diabetes Centre, Manchester Foundation Trust, Manchester M13 0JE, UK
| | | | - Stephen Kaye
- Department of Eye and Vision Science, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L7 8TX, UK
- St Paul’s Eye Unit, Royal Liverpool University Hospital, Liverpool L7 8XP, UK
| | - Rayaz Ahmed Malik
- Department of Medicine, Weill Cornell Medicine-Qatar, Doha 24144, Qatar
| | - Uazman Alam
- Department of Cardiovascular & Metabolic Medicine, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L7 8TX, UK
- St Paul’s Eye Unit, Royal Liverpool University Hospital, Liverpool L7 8XP, UK
- Liverpool Centre for Cardiovascular Science, Liverpool Heart and Chest Hospital, Liverpool L14 3PE, UK
| | - Yalin Zheng
- Department of Eye and Vision Science, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L7 8TX, UK
- St Paul’s Eye Unit, Royal Liverpool University Hospital, Liverpool L7 8XP, UK
- Liverpool Centre for Cardiovascular Science, Liverpool Heart and Chest Hospital, Liverpool L14 3PE, UK
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Yang Z, Tan TE, Shao Y, Wong TY, Li X. Classification of diabetic retinopathy: Past, present and future. Front Endocrinol (Lausanne) 2022; 13:1079217. [PMID: 36589807 PMCID: PMC9800497 DOI: 10.3389/fendo.2022.1079217] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Diabetic retinopathy (DR) is a leading cause of visual impairment and blindness worldwide. Since DR was first recognized as an important complication of diabetes, there have been many attempts to accurately classify the severity and stages of disease. These historical classification systems evolved as understanding of disease pathophysiology improved, methods of imaging and assessing DR changed, and effective treatments were developed. Current DR classification systems are effective, and have been the basis of major research trials and clinical management guidelines for decades. However, with further new developments such as recognition of diabetic retinal neurodegeneration, new imaging platforms such as optical coherence tomography and ultra wide-field retinal imaging, artificial intelligence and new treatments, our current classification systems have significant limitations that need to be addressed. In this paper, we provide a historical review of different classification systems for DR, and discuss the limitations of our current classification systems in the context of new developments. We also review the implications of new developments in the field, to see how they might feature in a future, updated classification.
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Affiliation(s)
- Zhengwei Yang
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
| | - Tien-En Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Yan Shao
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Duke-National University of Singapore Medical School, Singapore, Singapore
- Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Xiaorong Li
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
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Vujosevic S, Limoli C, Luzi L, Nucci P. Digital innovations for retinal care in diabetic retinopathy. Acta Diabetol 2022; 59:1521-1530. [PMID: 35962258 PMCID: PMC9374293 DOI: 10.1007/s00592-022-01941-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 07/04/2022] [Indexed: 12/02/2022]
Abstract
AIM The purpose of this review is to examine the applications of novel digital technology domains for the screening and management of patients with diabetic retinopathy (DR). METHODS A PubMed engine search was performed, using the terms "Telemedicine", "Digital health", "Telehealth", "Telescreening", "Artificial intelligence", "Deep learning", "Smartphone", "Triage", "Screening", "Home-based", "Monitoring", "Ophthalmology", "Diabetes", "Diabetic Retinopathy", "Retinal imaging". Full-text English language studies from January 1, 2010, to February 1, 2022, and reference lists were considered for the conceptual framework of this review. RESULTS Diabetes mellitus and its eye complications, including DR, are particularly well suited to digital technologies, providing an ideal model for telehealth initiatives and real-world applications. The current development in the adoption of telemedicine, artificial intelligence and remote monitoring as an alternative to or in addition to traditional forms of care will be discussed. CONCLUSIONS Advances in digital health have created an ecosystem ripe for telemedicine in the field of DR to thrive. Stakeholders and policymakers should adopt a participatory approach to ensure sustained implementation of these technologies after the COVID-19 pandemic. This article belongs to the Topical Collection "Diabetic Eye Disease", managed by Giuseppe Querques.
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Affiliation(s)
- Stela Vujosevic
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy.
- Eye Clinic, IRCCS MultiMedica, Via San Vittore 12, 20123, Milan, Italy.
| | - Celeste Limoli
- Eye Clinic, IRCCS MultiMedica, Via San Vittore 12, 20123, Milan, Italy
- University of Milan, Milan, Italy
| | - Livio Luzi
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
- Department of Endocrinology, Nutrition and Metabolic Diseases, IRCCS MultiMedica, Milan, Italy
| | - Paolo Nucci
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
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Nanegrungsunk O, Ruamviboonsuk P, Grzybowski A. Prospective studies on artificial intelligence (AI)-based diabetic retinopathy screening. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:1297. [PMID: 36660630 PMCID: PMC9843399 DOI: 10.21037/atm-2022-71] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 12/06/2022] [Indexed: 12/23/2022]
Affiliation(s)
- Onnisa Nanegrungsunk
- Retina Division, Department of Ophthalmology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Paisan Ruamviboonsuk
- Department of Ophthalmology, College of Medicine, Rangsit University, Rajavithi Hospital, Bangkok, Thailand
| | - Andrzej Grzybowski
- Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland;,Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland
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Chalkidou A, Shokraneh F, Kijauskaite G, Taylor-Phillips S, Halligan S, Wilkinson L, Glocker B, Garrett P, Denniston AK, Mackie A, Seedat F. Recommendations for the development and use of imaging test sets to investigate the test performance of artificial intelligence in health screening. Lancet Digit Health 2022; 4:e899-e905. [PMID: 36427951 DOI: 10.1016/s2589-7500(22)00186-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 08/11/2022] [Accepted: 09/09/2022] [Indexed: 11/24/2022]
Abstract
Rigorous evaluation of artificial intelligence (AI) systems for image classification is essential before deployment into health-care settings, such as screening programmes, so that adoption is effective and safe. A key step in the evaluation process is the external validation of diagnostic performance using a test set of images. We conducted a rapid literature review on methods to develop test sets, published from 2012 to 2020, in English. Using thematic analysis, we mapped themes and coded the principles using the Population, Intervention, and Comparator or Reference standard, Outcome, and Study design framework. A group of screening and AI experts assessed the evidence-based principles for completeness and provided further considerations. From the final 15 principles recommended here, five affect population, one intervention, two comparator, one reference standard, and one both reference standard and comparator. Finally, four are appliable to outcome and one to study design. Principles from the literature were useful to address biases from AI; however, they did not account for screening specific biases, which we now incorporate. The principles set out here should be used to support the development and use of test sets for studies that assess the accuracy of AI within screening programmes, to ensure they are fit for purpose and minimise bias.
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Affiliation(s)
| | - Farhad Shokraneh
- King's Technology Evaluation Centre, King's College London, London, UK
| | - Goda Kijauskaite
- UK National Screening Committee, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | | | - Steve Halligan
- Centre for Medical Imaging, Division of Medicine, University College London, London, UK
| | | | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Peter Garrett
- Department of Chemical Engineering and Analytical Science, University of Manchester, Manchester, UK
| | - Alastair K Denniston
- Department of Ophthalmology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Anne Mackie
- UK National Screening Committee, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | - Farah Seedat
- UK National Screening Committee, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
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Qian X, Jingying H, Xian S, Yuqing Z, Lili W, Baorui C, Wei G, Yefeng Z, Qiang Z, Chunyan C, Cheng B, Kai M, Yi Q. The effectiveness of artificial intelligence-based automated grading and training system in education of manual detection of diabetic retinopathy. Front Public Health 2022; 10:1025271. [PMID: 36419999 PMCID: PMC9678340 DOI: 10.3389/fpubh.2022.1025271] [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: 08/22/2022] [Accepted: 10/18/2022] [Indexed: 11/09/2022] Open
Abstract
Background The purpose of this study is to develop an artificial intelligence (AI)-based automated diabetic retinopathy (DR) grading and training system from a real-world diabetic dataset of China, and in particular, to investigate its effectiveness as a learning tool of DR manual grading for medical students. Methods We developed an automated DR grading and training system equipped with an AI-driven diagnosis algorithm to highlight highly prognostic related regions in the input image. Less experienced prospective physicians received pre- and post-training tests by the AI diagnosis platform. Then, changes in the diagnostic accuracy of the participants were evaluated. Results We randomly selected 8,063 cases diagnosed with DR and 7,925 with non-DR fundus images from type 2 diabetes patients. The automated DR grading system we developed achieved accuracy, sensitivity/specificity, and AUC values of 0.965, 0.965/0.966, and 0.980 for moderate or worse DR (95 percent CI: 0.976-0.984). When the graders received assistance from the output of the AI system, the metrics were enhanced in varying degrees. The automated DR grading system helped to improve the accuracy of human graders, i.e., junior residents and medical students, from 0.947 and 0.915 to 0.978 and 0.954, respectively. Conclusion The AI-based systemdemonstrated high diagnostic accuracy for the detection of DR on fundus images from real-world diabetics, and could be utilized as a training aid system for trainees lacking formal instruction on DR management.
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Affiliation(s)
- Xu Qian
- Department of Geriatrics, Qilu Hospital of Shandong University, Jinan, China,Key Laboratory of Cardiovascular Proteomics of Shandong Province, Jinan, China,Jinan Clinical Research Center for Geriatric Medicine (202132001), Jinan, China
| | - Han Jingying
- School of Basic Medical Sciences, Shandong University, Jinan, China
| | - Song Xian
- Department of Geriatrics, Qilu Hospital of Shandong University, Jinan, China
| | - Zhao Yuqing
- Department of Geriatrics, Qilu Hospital of Shandong University, Jinan, China
| | - Wu Lili
- Department of Geriatrics, Qilu Hospital of Shandong University, Jinan, China
| | - Chu Baorui
- Department of Geriatrics, Qilu Hospital of Shandong University, Jinan, China
| | - Guo Wei
- Lunan Eye Hospital, Linyi, China
| | | | | | | | | | - Ma Kai
- Tencent Healthcare, Shenzhen, China
| | - Qu Yi
- Department of Geriatrics, Qilu Hospital of Shandong University, Jinan, China,Key Laboratory of Cardiovascular Proteomics of Shandong Province, Jinan, China,Jinan Clinical Research Center for Geriatric Medicine (202132001), Jinan, China,*Correspondence: Qu Yi
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41
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Katz O, Presil D, Cohen L, Nachmani R, Kirshner N, Hoch Y, Lev T, Hadad A, Hewitt RJ, Owens DR. Evaluation of a New Neural Network Classifier for Diabetic Retinopathy. J Diabetes Sci Technol 2022; 16:1401-1409. [PMID: 34549633 PMCID: PMC9631541 DOI: 10.1177/19322968211042665] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Medical image segmentation is a well-studied subject within the field of image processing. The goal of this research is to create an AI retinal screening grading system that is both accurate and fast. We introduce a new segmentation network which achieves state-of-the-art results on semantic segmentation of color fundus photographs. By applying the net-work to identify anatomical markers of diabetic retinopathy (DR) and diabetic macular edema (DME), we collect sufficient information to classify patients by grades R0 and R1 or above, M0 and M1. METHODS The AI grading system was trained on screening data to evaluate the presence of DR and DME. The core algorithm of the system is a deep learning network that segments relevant anatomical features in a retinal image. Patients were graded according to the standard NHS Diabetic Eye Screening Program feature-based grading protocol. RESULTS The algorithm performance was evaluated with a series of 6,981 patient retinal images from routine diabetic eye screenings. It correctly predicted 98.9% of retinopathy events and 95.5% of maculopathy events. Non-disease events prediction rate was 68.6% for retinopathy and 81.2% for maculopathy. CONCLUSION This novel deep learning model was trained and tested on patient data from annual diabetic retinopathy screenings can classify with high accuracy the DR and DME status of a person with diabetes. The system can be easily reconfigured according to any grading protocol, without running a long AI training procedure. The incorporation of the AI grading system can increase the graders' productivity and improve the final outcome accuracy of the screening process.
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Affiliation(s)
- Or Katz
- NEC Israeli Research Center, Herzeliya,
Israel
| | - Dan Presil
- NEC Israeli Research Center, Herzeliya,
Israel
- Dan Presil, BSc, NEC Israeli Research
Center, 2 Maskit, Herzeliya, Israel.
| | - Liz Cohen
- NEC Israeli Research Center, Herzeliya,
Israel
| | | | | | - Yaacov Hoch
- NEC Israeli Research Center, Herzeliya,
Israel
| | - Tsvi Lev
- NEC Israeli Research Center, Herzeliya,
Israel
| | - Aviel Hadad
- MD MPH, Ophthalmology Department,
Soroka University Medical Center, Be’er Sheva, South District, Israel
| | | | - David R Owens
- Professor of Diabetes, Swansea
University Medical School, Swansea, Wales, UK
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John S, Srinivasan S, Ram K, Sivaprakasam M, Natarajan S. Efficacy of an Automated Algorithm for Screening Diabetic Retinopathy in Gradable and Ungradable Images in Real-Time Conditions. Telemed J E Health 2022. [DOI: 10.1089/tmj.2022.0113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- Sheila John
- Department of Teleophthalmology, Sankara Nethralaya, Chennai, India
| | | | - Keerthi Ram
- Healthcare Technology Innovation Centre, IIT Madras, Chennai, India
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Classification of diabetic retinopathy with feature selection over deep features using nature-inspired wrapper methods. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Lim JI, Regillo CD, Sadda SR, Ipp E, Bhaskaranand M, Ramachandra C, Solanki K. Artificial Intelligence Detection of Diabetic Retinopathy: Subgroup Comparison of the EyeArt System with Ophthalmologists' Dilated Exams. OPHTHALMOLOGY SCIENCE 2022; 3:100228. [PMID: 36345378 PMCID: PMC9636573 DOI: 10.1016/j.xops.2022.100228] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 08/30/2022] [Accepted: 09/22/2022] [Indexed: 11/26/2022]
Abstract
Objective To compare general ophthalmologists, retina specialists, and the EyeArt Artificial Intelligence (AI) system to the clinical reference standard for detecting more than mild diabetic retinopathy (mtmDR). Design Prospective, pivotal, multicenter trial conducted from April 2017 to May 2018. Participants Participants were aged ≥ 18 years who had diabetes mellitus and underwent dilated ophthalmoscopy. A total of 521 of 893 participants met these criteria and completed the study protocol. Testing Participants underwent 2-field fundus photography (macula centered, disc centered) for the EyeArt system, dilated ophthalmoscopy, and 4-widefield stereoscopic dilated fundus photography for reference standard grading. Main Outcome Measures For mtmDR detection, sensitivity and specificity of EyeArt gradings of 2-field, fundus photographs and ophthalmoscopy grading versus a rigorous clinical reference standard comprising Reading Center grading of 4-widefield stereoscopic dilated fundus photographs using the ETDRS severity scale. The AI system provided automatic eye-level results regarding mtmDR. Results Overall, 521 participants (999 eyes) at 10 centers underwent dilated ophthalmoscopy: 406 by nonretina and 115 by retina specialists. Reading Center graded 207 positive and 792 eyes negative for mtmDR. Of these 999 eyes, 26 eyes were ungradable by the EyeArt system, leaving 973 eyes with both EyeArt and Reading Center gradings. Retina specialists correctly identified 22 of 37 eyes as positive (sensitivity 59.5%) and 182 of 184 eyes as negative (specificity 98.9%) for mtmDR versus the EyeArt AI system that identified 36 of 37 as positive (sensitivity 97%) and 162 of 184 eyes as negative (specificity of 88%) for mtmDR. General ophthalmologists correctly identified 35 of 170 eyes as positive (sensitivity 20.6%) and 607 of 608 eyes as negative (specificity 99.8%) for mtmDR compared with the EyeArt AI system that identified 164 of 170 as positive (sensitivity 96.5%) and 525 of 608 eyes as negative (specificity 86%) for mtmDR. Conclusions The AI system had a higher sensitivity for detecting mtmDR than either general ophthalmologists or retina specialists compared with the clinical reference standard. It can potentially serve as a low-cost point-of-care diabetic retinopathy detection tool and help address the diabetic eye screening burden.
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45
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Castellanos-Canales D, Fawzi AA. Global challenges in the management of diabetic retinopathy in women with pre-gestational diabetes. Clin Exp Ophthalmol 2022; 50:711-713. [PMID: 36226519 PMCID: PMC9577478 DOI: 10.1111/ceo.14173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2022] [Indexed: 11/06/2022]
Affiliation(s)
| | - Amani A Fawzi
- Department of Ophthalmology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
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Pareja-Ríos A, Ceruso S, Romero-Aroca P, Bonaque-González S. A New Deep Learning Algorithm with Activation Mapping for Diabetic Retinopathy: Backtesting after 10 Years of Tele-Ophthalmology. J Clin Med 2022; 11:jcm11174945. [PMID: 36078875 PMCID: PMC9456446 DOI: 10.3390/jcm11174945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 08/17/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022] Open
Abstract
We report the development of a deep learning algorithm (AI) to detect signs of diabetic retinopathy (DR) from fundus images. For this, we use a ResNet-50 neural network with a double resolution, the addition of Squeeze–Excitation blocks, pre-trained in ImageNet, and trained for 50 epochs using the Adam optimizer. The AI-based algorithm not only classifies an image as pathological or not but also detects and highlights those signs that allow DR to be identified. For development, we have used a database of about half a million images classified in a real clinical environment by family doctors (FDs), ophthalmologists, or both. The AI was able to detect more than 95% of cases worse than mild DR and had 70% fewer misclassifications of healthy cases than FDs. In addition, the AI was able to detect DR signs in 1258 patients before they were detected by FDs, representing 7.9% of the total number of DR patients detected by the FDs. These results suggest that AI is at least comparable to the evaluation of FDs. We suggest that it may be useful to use signaling tools such as an aid to diagnosis rather than an AI as a stand-alone tool.
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Affiliation(s)
- Alicia Pareja-Ríos
- Department of Ophthalmology, University Hospital of the Canary Islands, 38320 San Cristóbal de La Laguna, Spain
| | - Sabato Ceruso
- School of Engineering and Technology, University of La Laguna, 38200 San Cristóbal de La Laguna, Spain
| | - Pedro Romero-Aroca
- Ophthalmology Department, University Hospital Sant Joan, Institute of Health Research Pere Virgili (IISPV), Universitat Rovira & Virgili, 43002 Tarragona, Spain
| | - Sergio Bonaque-González
- Instituto de Astrofísica de Canarias, 38205 San Cristóbal de La Laguna, Spain
- Correspondence:
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Zafar S, Mahjoub H, Mehta N, Domalpally A, Channa R. Artificial Intelligence Algorithms in Diabetic Retinopathy Screening. Curr Diab Rep 2022; 22:267-274. [PMID: 35438458 DOI: 10.1007/s11892-022-01467-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/07/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE OF REVIEW In this review, we focus on artificial intelligence (AI) algorithms for diabetic retinopathy (DR) screening and risk stratification and factors to consider when implementing AI algorithms in the clinic. RECENT FINDINGS AI algorithms have been adopted, and have received regulatory approval, for automated detection of referable DR with clinically acceptable diagnostic performance. While these metrics are an important first step, performance metrics that go beyond measures of technical accuracy are needed to fully evaluate the impact of AI algorithm on patient outcomes. Recent advances in AI present an exciting opportunity to improve patient care. Using DR as an example, we have reviewed factors to consider in the implementation of AI algorithms in real-world clinical practice. These include real-world evaluation of safety, efficacy, and equity (bias); impact on patient outcomes; ethical, logistical, and regulatory factors.
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Affiliation(s)
- Sidra Zafar
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Heba Mahjoub
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Nitish Mehta
- Department of Ophthalmology, New York University School of Medicine, New York, NY, USA
| | - Amitha Domalpally
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA.
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Gurudas S, Frudd K, Maheshwari JJ, Revathy YR, Sivaprasad S, Ramanathan SM, Pooleeswaran V, Prevost AT, Karatsai E, Halim S, Chandra S, Nderitu P, Conroy D, Krishnakumar S, Parameswaran S, Dharmalingam K, Ramasamy K, Raman R, Jones C, Eleftheriadis H, Greenwood J, Turowski P. Multicenter Evaluation of Diagnostic Circulating Biomarkers to Detect Sight-Threatening Diabetic Retinopathy. JAMA Ophthalmol 2022; 140:587-597. [PMID: 35511139 PMCID: PMC9073659 DOI: 10.1001/jamaophthalmol.2022.1175] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Question Can circulating serum biomarkers distinguish people with sight-threatening diabetic retinopathy (STDR) from those with no DR? Findings This multicenter cross-sectional study of 538 participants found an incremental benefit of circulating cystatin C beyond the standard clinical variables in discriminating STDR from no DR. Cystatin C outperformed 12 other biomarkers found to be distinguished in STDR in previous research. Meaning Results of this study suggest the consideration of circulating cystatin C levels as a triage test in prioritizing people with type 2 diabetes from the community for retinal screening in resource-restricted settings. Importance It is a global challenge to provide regular retinal screening for all people with diabetes to detect sight-threatening diabetic retinopathy (STDR). Objective To determine if circulating biomarkers could be used to prioritize people with type 2 diabetes for retinal screening to detect STDR. Design, Setting, and Participants This cross-sectional study collected data from October 22, 2018, to December 31, 2021. All laboratory staff were masked to the clinical diagnosis, assigned a study cohort, and provided with the database containing the clinical data. This was a multicenter study conducted in parallel in 3 outpatient ophthalmology clinics in the UK and 2 centers in India. Adults 40 years and older were categorized into 4 groups: (1) no history of diabetes, (2) type 2 diabetes of at least 5 years’ duration with no evidence of DR, (3) nonproliferative DR with diabetic macular edema (DME), or (4) proliferative DR. STDR comprised groups 3 and 4. Exposures Thirteen previously verified biomarkers were measured using enzyme-linked immunosorbent assay. Main Outcomes and Measures Severity of DR and presence of DME were diagnosed using fundus photographs and optical coherence tomography. Weighted logistic regression and receiver operating characteristic curve analysis (ROC) were performed to identify biomarkers that discriminate STDR from no DR beyond the standard clinical parameters of age, disease duration, ethnicity (in the UK) and hemoglobin A1c. Results A total of 538 participants (mean [SD] age, 60.8 [9.8] years; 319 men [59.3%]) were recruited into the study. A total of 264 participants (49.1%) were from India (group 1, 54 [20.5%]; group 2, 53 [20.1%]; group 3, 52 [19.7%]; group 4, 105 [39.8%]), and 274 participants (50.9%) were from the UK (group 1, 50 [18.2%]; group 2, 70 [25.5%]; group 3, 55 [20.1%]; group 4, 99 [36.1%]). ROC analysis (no DR vs STDR) showed that in addition to age, disease duration, ethnicity (in the UK) and hemoglobin A1c, inclusion of cystatin C had near-acceptable discrimination power in both countries (area under the receiver operating characteristic curve [AUC], 0.779; 95% CI, 0.700-0.857 in 215 patients in the UK with complete data; AUC, 0.696; 95% CI, 0.602-0.791 in 208 patients in India with complete data). Conclusions and Relevance Results of this cross-sectional study suggest that serum cystatin C had good discrimination power in the UK and India. Circulating cystatin-C levels may be considered as a test to identify those who require prioritization for retinal screening for STDR.
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Affiliation(s)
- Sarega Gurudas
- Institute of Ophthalmology, University College London, London, United Kingdom
| | - Karen Frudd
- Institute of Ophthalmology, University College London, London, United Kingdom
| | | | | | - Sobha Sivaprasad
- Institute of Ophthalmology, University College London, London, United Kingdom.,National Institute of Health Research Moorfields Biomedical Research Centre, Moorfields Eye Hospital London NHS Foundation Trust, London, United Kingdom
| | | | - Vignesh Pooleeswaran
- Aravind Medical Research Foundation, Proteomics Department, No.1 Anna Nagar, Madurai, India
| | - A Toby Prevost
- Nightingale-Saunders Clinical Trials and Epidemiology Unit, King's College London, London, United Kingdom
| | - Eleni Karatsai
- National Institute of Health Research Moorfields Biomedical Research Centre, Moorfields Eye Hospital London NHS Foundation Trust, London, United Kingdom
| | - Sandra Halim
- Institute of Ophthalmology, University College London, London, United Kingdom
| | - Shruti Chandra
- Institute of Ophthalmology, University College London, London, United Kingdom.,National Institute of Health Research Moorfields Biomedical Research Centre, Moorfields Eye Hospital London NHS Foundation Trust, London, United Kingdom
| | - Paul Nderitu
- Ophthalmology Department, King's College University Hospital Trust, London, United Kingdom
| | - Dolores Conroy
- Institute of Ophthalmology, University College London, London, United Kingdom
| | | | | | | | | | | | - Colin Jones
- Norfolk and Norwich University Hospitals NHS Trust, Norwich, United Kingdom
| | | | - John Greenwood
- Institute of Ophthalmology, University College London, London, United Kingdom
| | - Patric Turowski
- Institute of Ophthalmology, University College London, London, United Kingdom
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Kampik A. How to treat diabetic retinopathy and diabetic macular edema most efficiently. Oman J Ophthalmol 2022; 15:3-5. [PMID: 35388248 PMCID: PMC8979388 DOI: 10.4103/ojo.ojo_27_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 02/02/2022] [Indexed: 11/24/2022] Open
Affiliation(s)
- Anselm Kampik
- Direktor Augenzentrum im Brienner Hof, München, Germany,Emeritus Augenklinik der Universität München Member, Germany,Academia Ophthalmologica Internationalis, Briennerstrasse 12, D-80333 München, Germany,Address for correspondence: Prof. Anselm Kampik, FEBO, FARVO, Augenzentrum im Brienner Hof, Briennerstrasse 12, D-80333 Munchen, Germany. E-mail:
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50
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Ruamviboonsuk P, Tiwari R, Sayres R, Nganthavee V, Hemarat K, Kongprayoon A, Raman R, Levinstein B, Liu Y, Schaekermann M, Lee R, Virmani S, Widner K, Chambers J, Hersch F, Peng L, Webster DR. Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study. THE LANCET DIGITAL HEALTH 2022; 4:e235-e244. [DOI: 10.1016/s2589-7500(22)00017-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 12/17/2021] [Accepted: 01/14/2022] [Indexed: 02/08/2023]
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