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Whitestone N, Nkurikiye J, Patnaik JL, Jaccard N, Lanouette G, Cherwek DH, Congdon N, Mathenge W. Feasibility and acceptance of artificial intelligence-based diabetic retinopathy screening in Rwanda. Br J Ophthalmol 2024; 108:840-845. [PMID: 37541766 DOI: 10.1136/bjo-2022-322683] [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: 10/05/2022] [Accepted: 07/15/2023] [Indexed: 08/06/2023]
Abstract
BACKGROUND Evidence on the practical application of artificial intelligence (AI)-based diabetic retinopathy (DR) screening is needed. METHODS Consented participants were screened for DR using retinal imaging with AI interpretation from March 2021 to June 2021 at four diabetes clinics in Rwanda. Additionally, images were graded by a UK National Health System-certified retinal image grader. DR grades based on the International Classification of Diabetic Retinopathy with a grade of 2.0 or higher were considered referable. The AI system was designed to detect optic nerve and macular anomalies outside of DR. A vertical cup to disc ratio of 0.7 and higher and/or macular anomalies recognised at a cut-off of 60% and higher were also considered referable by AI. RESULTS Among 827 participants (59.6% women (n=493)) screened by AI, 33.2% (n=275) were referred for follow-up. Satisfaction with AI screening was high (99.5%, n=823), and 63.7% of participants (n=527) preferred AI over human grading. Compared with human grading, the sensitivity of the AI for referable DR was 92% (95% CI 0.863%, 0.968%), with a specificity of 85% (95% CI 0.751%, 0.882%). Of the participants referred by AI: 88 (32.0%) were for DR only, 109 (39.6%) for DR and an anomaly, 65 (23.6%) for an anomaly only and 13 (4.73%) for other reasons. Adherence to referrals was highest for those referred for DR at 53.4%. CONCLUSION DR screening using AI led to accurate referrals from diabetes clinics in Rwanda and high rates of participant satisfaction, suggesting AI screening for DR is practical and acceptable.
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Affiliation(s)
| | - John Nkurikiye
- RIIO iHospital, Rwanda International Institute of Ophthalmology, Kigali, Rwanda
- Department of Ophthalmology, Rwanda Military Hospital, Kigali, Rwanda
| | - Jennifer L Patnaik
- Clinical Services, Orbis International, New York, New York, USA
- Department of Ophthalmology, University of Colorado Denver School of Medicine, Aurora, Colorado, USA
| | - Nicolas Jaccard
- Clinical Services, Orbis International, New York, New York, USA
| | | | - David H Cherwek
- Clinical Services, Orbis International, New York, New York, USA
| | - Nathan Congdon
- Clinical Services, Orbis International, New York, New York, USA
- Centre for Public Health, Queen's University Belfast, Belfast, UK
| | - Wanjiku Mathenge
- Clinical Services, Orbis International, New York, New York, USA
- RIIO iHospital, Rwanda International Institute of Ophthalmology, Kigali, Rwanda
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Han R, Acosta JN, Shakeri Z, Ioannidis JPA, Topol EJ, Rajpurkar P. Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review. Lancet Digit Health 2024; 6:e367-e373. [PMID: 38670745 PMCID: PMC11068159 DOI: 10.1016/s2589-7500(24)00047-5] [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: 05/08/2023] [Revised: 03/01/2024] [Accepted: 03/04/2024] [Indexed: 04/28/2024]
Abstract
This scoping review of randomised controlled trials on artificial intelligence (AI) in clinical practice reveals an expanding interest in AI across clinical specialties and locations. The USA and China are leading in the number of trials, with a focus on deep learning systems for medical imaging, particularly in gastroenterology and radiology. A majority of trials (70 [81%] of 86) report positive primary endpoints, primarily related to diagnostic yield or performance; however, the predominance of single-centre trials, little demographic reporting, and varying reports of operational efficiency raise concerns about the generalisability and practicality of these results. Despite the promising outcomes, considering the likelihood of publication bias and the need for more comprehensive research including multicentre trials, diverse outcome measures, and improved reporting standards is crucial. Future AI trials should prioritise patient-relevant outcomes to fully understand AI's true effects and limitations in health care.
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Affiliation(s)
- Ryan Han
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; Department of Computer Science, Stanford University, Stanford, CA, USA; University of California Los Angeles-Caltech Medical Scientist Training Program, Los Angeles, CA, USA
| | - Julián N Acosta
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA; Rad AI, San Francisco, CA, USA
| | - Zahra Shakeri
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - John P A Ioannidis
- Stanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, CA, USA; Meta-Research Innovation Center at Stanford, Stanford University, Stanford, CA, USA
| | - Eric J Topol
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA, USA.
| | - Pranav Rajpurkar
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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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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4
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Lee CS. Entering the Exciting Era of Artificial Intelligence and Big Data in Ophthalmology. OPHTHALMOLOGY SCIENCE 2024; 4:100469. [PMID: 38333043 PMCID: PMC10851194 DOI: 10.1016/j.xops.2024.100469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
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Martindale APL, Ng B, Ngai V, Kale AU, Ferrante di Ruffano L, Golub RM, Collins GS, Moher D, McCradden MD, Oakden-Rayner L, Rivera SC, Calvert M, Kelly CJ, Lee CS, Yau C, Chan AW, Keane PA, Beam AL, Denniston AK, Liu X. Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines. Nat Commun 2024; 15:1619. [PMID: 38388497 PMCID: PMC10883966 DOI: 10.1038/s41467-024-45355-3] [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: 07/27/2023] [Accepted: 01/22/2024] [Indexed: 02/24/2024] Open
Abstract
The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77-94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.
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Affiliation(s)
| | - Benjamin Ng
- Birmingham and Midland Eye Centre, Sandwell and West Birmingham NHS Trust, Birmingham, UK
- Christ Church, University of Oxford, Oxford, UK
| | - Victoria Ngai
- University College London Medical School, London, UK
| | - Aditya U Kale
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK
| | | | - Robert M Golub
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Gary S Collins
- Centre for Statistics in Medicine//UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - David Moher
- Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottowa, ON, Canada
| | - Melissa D McCradden
- Department of Bioethics, The Hospital for Sick Children, Toronto, ON, Canada
- Genetics & Genome Biology Research Program, Peter Gilgan Centre for Research & Learning, Toronto, ON, Canada
- Division of Clinical and Public Health, Dalla Lana School of Public Health, Toronto, ON, Canada
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia
| | - Samantha Cruz Rivera
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- Centre for Patient Reported Outcomes Research (CPROR), Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Melanie Calvert
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- Centre for Patient Reported Outcomes Research (CPROR), Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- NIHR Applied Research Collaboration (ARC) West Midlands, University of Birmingham, Birmingham, UK
- NIHR Blood and Transplant Research Unit (BTRU) in Precision Transplant and Cellular Therapeutics, University of Birmingham, Birmingham, UK
| | | | | | - Christopher Yau
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Health Data Research UK, London, UK
| | - An-Wen Chan
- Department of Medicine, Women's College Hospital. University of Toronto, Toronto, ON, Canada
| | - Pearse A Keane
- NIHR Biomedical Research Centre at Moorfields, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Andrew L Beam
- Department of Epidemiology, Harvard. T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Alastair K Denniston
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- NIHR Biomedical Research Centre at Moorfields, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Xiaoxuan Liu
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK.
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK.
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Chia MA, Hersch F, Sayres R, Bavishi P, Tiwari R, Keane PA, Turner AW. Validation of a deep learning system for the detection of diabetic retinopathy in Indigenous Australians. Br J Ophthalmol 2024; 108:268-273. [PMID: 36746615 PMCID: PMC10850716 DOI: 10.1136/bjo-2022-322237] [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: 07/14/2022] [Accepted: 12/31/2022] [Indexed: 02/08/2023]
Abstract
BACKGROUND/AIMS Deep learning systems (DLSs) for diabetic retinopathy (DR) detection show promising results but can underperform in racial and ethnic minority groups, therefore external validation within these populations is critical for health equity. This study evaluates the performance of a DLS for DR detection among Indigenous Australians, an understudied ethnic group who suffer disproportionately from DR-related blindness. METHODS We performed a retrospective external validation study comparing the performance of a DLS against a retinal specialist for the detection of more-than-mild DR (mtmDR), vision-threatening DR (vtDR) and all-cause referable DR. The validation set consisted of 1682 consecutive, single-field, macula-centred retinal photographs from 864 patients with diabetes (mean age 54.9 years, 52.4% women) at an Indigenous primary care service in Perth, Australia. Three-person adjudication by a panel of specialists served as the reference standard. RESULTS For mtmDR detection, sensitivity of the DLS was superior to the retina specialist (98.0% (95% CI, 96.5 to 99.4) vs 87.1% (95% CI, 83.6 to 90.6), McNemar's test p<0.001) with a small reduction in specificity (95.1% (95% CI, 93.6 to 96.4) vs 97.0% (95% CI, 95.9 to 98.0), p=0.006). For vtDR, the DLS's sensitivity was again superior to the human grader (96.2% (95% CI, 93.4 to 98.6) vs 84.4% (95% CI, 79.7 to 89.2), p<0.001) with a slight drop in specificity (95.8% (95% CI, 94.6 to 96.9) vs 97.8% (95% CI, 96.9 to 98.6), p=0.002). For all-cause referable DR, there was a substantial increase in sensitivity (93.7% (95% CI, 91.8 to 95.5) vs 74.4% (95% CI, 71.1 to 77.5), p<0.001) and a smaller reduction in specificity (91.7% (95% CI, 90.0 to 93.3) vs 96.3% (95% CI, 95.2 to 97.4), p<0.001). CONCLUSION The DLS showed improved sensitivity and similar specificity compared with a retina specialist for DR detection. This demonstrates its potential to support DR screening among Indigenous Australians, an underserved population with a high burden of diabetic eye disease.
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Affiliation(s)
- Mark A Chia
- Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Lions Outback Vision, Lions Eye Institute, Nedlands, Western Australia, Australia
| | | | | | | | | | - Pearse A Keane
- Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Angus W Turner
- Lions Outback Vision, Lions Eye Institute, Nedlands, Western Australia, Australia
- Centre for Ophthalmology and Visual Science, The University of Western Australia, Nedlands, Western Australia, Australia
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Cleland CR, Bascaran C, Makupa W, Shilio B, Sandi FA, Philippin H, Marques AP, Egan C, Tufail A, Keane PA, Denniston AK, Macleod D, Burton MJ. Artificial intelligence-supported diabetic retinopathy screening in Tanzania: rationale and design of a randomised controlled trial. BMJ Open 2024; 14:e075055. [PMID: 38272554 PMCID: PMC10824006 DOI: 10.1136/bmjopen-2023-075055] [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: 04/25/2023] [Accepted: 01/12/2024] [Indexed: 01/27/2024] Open
Abstract
INTRODUCTION Globally, diabetic retinopathy (DR) is a major cause of blindness. Sub-Saharan Africa is projected to see the largest proportionate increase in the number of people living with diabetes over the next two decades. Screening for DR is recommended to prevent sight loss; however, in many low and middle-income countries, because of a lack of specialist eye care staff, current screening services for DR are not optimal. The use of artificial intelligence (AI) for DR screening, which automates the grading of retinal photographs and provides a point-of-screening result, offers an innovative potential solution to improve DR screening in Tanzania. METHODS AND ANALYSIS We will test the hypothesis that AI-supported DR screening increases the proportion of persons with true referable DR who attend the central ophthalmology clinic following referral after screening in a single-masked, parallel group, individually randomised controlled trial. Participants (2364) will be randomised (1:1 ratio) to either AI-supported or the standard of care DR screening pathway. Participants allocated to the AI-supported screening pathway will receive their result followed by point-of-screening counselling immediately after retinal image capture. Participants in the standard of care arm will receive their result and counselling by phone once the retinal images have been graded in the usual way (typically after 2-4 weeks). The primary outcome is the proportion of persons with true referable DR attending the central ophthalmology clinic within 8 weeks of screening. Secondary outcomes, by trial arm, include the proportion of persons attending the central ophthalmology clinic out of all those referred, sensitivity and specificity, number of false positive referrals, acceptability and fidelity of AI-supported screening. ETHICS AND DISSEMINATION The London School of Hygiene & Tropical Medicine, Kilimanjaro Christian Medical Centre and Tanzanian National Institute of Medical Research ethics committees have approved the trial. The results will be submitted to peer-reviewed journals for publication. TRIAL REGISTRATION NUMBER ISRCTN18317152.
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Affiliation(s)
- Charles R Cleland
- International Centre for Eye Health, London School of Hygiene & Tropical Medicine, London, UK
- Eye Department, Kilimanjaro Christian Medical Centre, Moshi, Tanzania
| | - Covadonga Bascaran
- International Centre for Eye Health, London School of Hygiene & Tropical Medicine, London, UK
| | - William Makupa
- Eye Department, Kilimanjaro Christian Medical Centre, Moshi, Tanzania
| | - Bernadetha Shilio
- Ministry of Health, Community Development, Gender, Elderly and Children, Dodoma, Tanzania
| | - Frank A Sandi
- Department of Ophthalmology, University of Dodoma School of Medicine and Nursing, Dodoma, Tanzania
| | - Heiko Philippin
- International Centre for Eye Health, London School of Hygiene & Tropical Medicine, London, UK
- Eye Centre, University of Freiburg Faculty of Medicine, Freiburg, Germany
| | - Ana Patricia Marques
- International Centre for Eye Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Catherine Egan
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre (BRC) for Ophthalmology, University College London, Moorfields Hospital London NHS Foundation Trust and Institute of Ophthalmology, London, UK
| | - Adnan Tufail
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre (BRC) for Ophthalmology, University College London, Moorfields Hospital London NHS Foundation Trust and Institute of Ophthalmology, London, UK
| | - Pearse A Keane
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre (BRC) for Ophthalmology, University College London, Moorfields Hospital London NHS Foundation Trust and Institute of Ophthalmology, London, UK
| | - Alastair K Denniston
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre (BRC) for Ophthalmology, University College London, Moorfields Hospital London NHS Foundation Trust and Institute of Ophthalmology, London, UK
- National Institute for Health and Care Research, Birmingham Biomedical Research Centre, Birmingham, UK
| | - David Macleod
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Matthew J Burton
- International Centre for Eye Health, London School of Hygiene & Tropical Medicine, London, UK
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre (BRC) for Ophthalmology, University College London, Moorfields Hospital London NHS Foundation Trust and Institute of Ophthalmology, London, UK
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Wolf RM, Channa R, Liu TYA, Zehra A, Bromberger L, Patel D, Ananthakrishnan A, Brown EA, Prichett L, Lehmann HP, Abramoff MD. Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial. Nat Commun 2024; 15:421. [PMID: 38212308 PMCID: PMC10784572 DOI: 10.1038/s41467-023-44676-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 12/21/2023] [Indexed: 01/13/2024] Open
Abstract
Diabetic retinopathy can be prevented with screening and early detection. We hypothesized that autonomous artificial intelligence (AI) diabetic eye exams at the point-of-care would increase diabetic eye exam completion rates in a racially and ethnically diverse youth population. AI for Children's diabetiC Eye ExamS (NCT05131451) is a parallel randomized controlled trial that randomized youth (ages 8-21 years) with type 1 and type 2 diabetes to intervention (autonomous artificial intelligence diabetic eye exam at the point of care), or control (scripted eye care provider referral and education) in an academic pediatric diabetes center. The primary outcome was diabetic eye exam completion rate within 6 months. The secondary outcome was the proportion of participants who completed follow-through with an eye care provider if deemed appropriate. Diabetic eye exam completion rate was significantly higher (100%, 95%CI: 95.5%, 100%) in the intervention group (n = 81) than the control group (n = 83) (22%, 95%CI: 14.2%, 32.4%)(p < 0.001). In the intervention arm, 25/81 participants had an abnormal result, of whom 64% (16/25) completed follow-through with an eye care provider, compared to 22% in the control arm (p < 0.001). Autonomous AI increases diabetic eye exam completion rates in youth with diabetes.
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Affiliation(s)
- Risa M Wolf
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA
| | - T Y Alvin Liu
- Wilmer Eye Institute at the Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Anum Zehra
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Lee Bromberger
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Dhruva Patel
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | | | - Elizabeth A Brown
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Laura Prichett
- Johns Hopkins School of Medicine Biostatistics, Epidemiology and Data Management (BEAD) Core, Baltimore, MD, USA
| | - Harold P Lehmann
- Section on Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, USA
| | - Michael D Abramoff
- Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA
- Digital Diagnostics Inc, Coralville, IA, USA
- Iowa City VA Medical Center, Iowa City, IA, USA
- Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, USA
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA
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Song A, Borkar DS. Advances in Teleophthalmology Screening for Diabetic Retinopathy. Int Ophthalmol Clin 2024; 64:97-113. [PMID: 38146884 DOI: 10.1097/iio.0000000000000505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
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10
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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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Olawoye OO, Ha TH, Pham N, Nguyen L, Cherwek DH, Fowobaje KR, Ross C, Coote M, Chan VF, Kahook M, Peto T, Azuara-Blanco A, Congdon N. Impact of a short online course on the accuracy of non-ophthalmic diabetic retinopathy graders in recognising glaucomatous optic nerves in Vietnam. BMJ Open 2023; 13:e076623. [PMID: 37945295 PMCID: PMC10649381 DOI: 10.1136/bmjopen-2023-076623] [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: 06/12/2023] [Accepted: 09/12/2023] [Indexed: 11/12/2023] Open
Abstract
PURPOSE To test an online training course for non-ophthalmic diabetic retinopathy (DR) graders for recognition of glaucomatous optic nerves in Vietnam. METHODS This was an uncontrolled, experimental, before-and-after study in which 43 non-ophthalmic DR graders underwent baseline testing on a standard image set, completed a self-paced, online training course and were retested using the same photographs presented randomly. Twenty-nine local ophthalmologists completed the same test without the training course. DR graders then underwent additional one-to-one training by a glaucoma specialist and were retested. Test performance (% correct, compared with consensus grades from four fellowship-trained glaucoma experts), sensitivity, specificity, positive and negative predictive value, and area under the receiver operating (AUC) curve, were computed. RESULTS Mean age of DR graders (32.6±5.5 years) did not differ from ophthalmologists (32.3±7.3 years, p=0.13). Online training required a mean of 297.9 (SD 144.6) minutes. Graders' mean baseline score (33.3%±14.3%) improved significantly after training (55.8%±12.6%, p<0.001), and post-training score did not differ from ophthalmologists (58.7±15.4%, p=0.384). Although grader sensitivity reduced before [85.5% (95% CI 83.5% to 87.3%)] versus after [80.4% (78.3% to 82.4%)] training, specificity improved significantly [47.8 (44.9 to 50.7) vs 79.8 (77.3 to 82.0), p<0.001]. Grader AUC also improved after training [66.6 (64.9 to 68.3)] to [80.1 (78.5 to 81.6), p<0.001]. Additional one-to-one grader training by a glaucoma specialist did not further improve grader scores. CONCLUSION Non-ophthalmic DR graders can be trained to recognise glaucoma using a short online course in this setting, with no additional benefit from more expensive one-to-one training. After 5-hour online training in recognising glaucomatous optic nerve head, scores of non-ophthalmic DR graders doubled, and did not differ from local ophthalmologists. Intensive one-to-one training did not further improve performance.
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Affiliation(s)
- Olusola Oluyinka Olawoye
- School of Medicine, Dentistry and Biomedical Sciences, Queens University Belfast, Belfast, UK
- Department of Ophthalmology, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | | | - Ngoc Pham
- ORBIS International, New York, New York, USA
| | - Lam Nguyen
- Hanoi Medical University, Hanoi, Viet Nam
| | | | | | - Craig Ross
- Centre for Eye Research Australia, East Melbourne, Victoria, Australia
| | - Michael Coote
- Centre for Eye Research Australia, East Melbourne, Victoria, Australia
| | - Ving Fai Chan
- School of Medicine, Dentistry and Biomedical Sciences, Queens University Belfast, Belfast, UK
| | - Malik Kahook
- University of Colorado at Colorado Springs, Colorado, UK
| | - Tunde Peto
- Faculty of Medicine Health and Life Sciences, Queen's University Belfast, Belfast, Belfast, UK
| | | | - Nathan Congdon
- Department of Ophthalmology and Public Health, Queen's University Belfast, Belfast, UK
- Orbis International NY USA, New York, New York, USA
- Department of Ophthalmology, Zhongshan Ophthalmic Centre, Guangzhou, People's Republic of China
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12
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Dow ER, Chen KM, Zhao CS, Knapp AN, Phadke A, Weng K, Do DV, Mahajan VB, Mruthyunjaya P, Leng T, Myung D. Artificial Intelligence Improves Patient Follow-Up in a Diabetic Retinopathy Screening Program. Clin Ophthalmol 2023; 17:3323-3330. [PMID: 38026608 PMCID: PMC10665027 DOI: 10.2147/opth.s422513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/30/2023] [Indexed: 12/01/2023] Open
Abstract
Purpose We examine the rate of and reasons for follow-up in an Artificial Intelligence (AI)-based workflow for diabetic retinopathy (DR) screening relative to two human-based workflows. Patients and Methods A DR screening program initiated September 2019 between one institution and its affiliated primary care and endocrinology clinics screened 2243 adult patients with type 1 or 2 diabetes without a diagnosis of DR in the previous year in the San Francisco Bay Area. For patients who screened positive for more-than-mild-DR (MTMDR), rates of follow-up were calculated under a store-and-forward human-based DR workflow ("Human Workflow"), an AI-based workflow involving IDx-DR ("AI Workflow"), and a two-step hybrid workflow ("AI-Human Hybrid Workflow"). The AI Workflow provided results within 48 hours, whereas the other workflows took up to 7 days. Patients were surveyed by phone about follow-up decisions. Results Under the AI Workflow, 279 patients screened positive for MTMDR. Of these, 69.2% followed up with an ophthalmologist within 90 days. Altogether 70.5% (N=48) of patients who followed up chose their location based on primary care referral. Among the subset of patients that were seen in person at the university eye institute under the Human Workflow and AI-Human Hybrid Workflow, 12.0% (N=14/117) and 11.7% (N=12/103) of patients with a referrable screening result followed up compared to 35.5% of patients under the AI Workflow (N=99/279; χ2df=2 = 36.70, p < 0.00000001). Conclusion Ophthalmology follow-up after a positive DR screening result is approximately three-fold higher under the AI Workflow than either the Human Workflow or AI-Human Hybrid Workflow. Improved follow-up behavior may be due to the decreased time to screening result.
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Grants
- P30 EY026877 NEI NIH HHS
- Research to Prevent Blindness
- Roche/Genentech, Protagonist Therapeutics, Alcon, Regeneron, Graybug, Boehringer Ingelheim, Kanaph
- Nanoscope Therapeutics, Apellis, Astellas
- Regeneron, Kriya, Boerhinger Ingelheim
- Genentech, Regeneron, Kodiak Sciences, Apellis, Iveric Bio
- Stanford Diabetes Research Center (SDRC)
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Affiliation(s)
- Eliot R Dow
- Department of Ophthalmology, Byers Eye Institute at Stanford, Palo Alto, CA, USA
- Department of Ophthalmology, Duke Eye Center, Durham, NC, USA
| | - Karen M Chen
- Department of Ophthalmology, Byers Eye Institute at Stanford, Palo Alto, CA, USA
| | - Cindy S Zhao
- Department of Ophthalmology, Byers Eye Institute at Stanford, Palo Alto, CA, USA
| | - Austen N Knapp
- Department of Ophthalmology, Byers Eye Institute at Stanford, Palo Alto, CA, USA
| | - Anuradha Phadke
- Department of Internal Medicine, Stanford Health Care, Palo Alto, CA, USA
| | - Kirsti Weng
- Department of Internal Medicine, Stanford Health Care, Palo Alto, CA, USA
| | - Diana V Do
- Department of Ophthalmology, Byers Eye Institute at Stanford, Palo Alto, CA, USA
| | - Vinit B Mahajan
- Department of Ophthalmology, Byers Eye Institute at Stanford, Palo Alto, CA, USA
| | - Prithvi Mruthyunjaya
- Department of Ophthalmology, Byers Eye Institute at Stanford, Palo Alto, CA, USA
| | - Theodore Leng
- Department of Ophthalmology, Byers Eye Institute at Stanford, Palo Alto, CA, USA
| | - David Myung
- Department of Ophthalmology, Byers Eye Institute at Stanford, Palo Alto, CA, USA
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13
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Curran K, Whitestone N, Zabeen B, Ahmed M, Husain L, Alauddin M, Hossain MA, Patnaik JL, Lanoutee G, Cherwek DH, Congdon N, Peto T, Jaccard N. CHILDSTAR: CHIldren Living With Diabetes See and Thrive with AI Review. Clin Med Insights Endocrinol Diabetes 2023; 16:11795514231203867. [PMID: 37822362 PMCID: PMC10563496 DOI: 10.1177/11795514231203867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 08/23/2023] [Indexed: 10/13/2023] Open
Abstract
Background Artificial intelligence (AI) appears capable of detecting diabetic retinopathy (DR) with a high degree of accuracy in adults; however, there are few studies in children and young adults. Methods Children and young adults (3-26 years) with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) were screened at the Dhaka BIRDEM-2 hospital, Bangladesh. All gradable fundus images were uploaded to Cybersight AI for interpretation. Two main outcomes were considered at a patient level: 1) Any DR, defined as mild non-proliferative diabetic retinopathy (NPDR or more severe; and 2) Referable DR, defined as moderate NPDR or more severe. Diagnostic test performance comparing Orbis International's Cybersight AI with the reference standard, a fully qualified optometrist certified in DR grading, was assessed using the Matthews correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), area under the precision-recall curve (AUC-PR), sensitivity, specificity, positive and negative predictive values. Results Among 1274 participants (53.1% female, mean age 16.7 years), 19.4% (n = 247) had any DR according to AI. For referable DR, 2.35% (n = 30) were detected by AI. The sensitivity and specificity of AI for any DR were 75.5% (CI 69.7-81.3%) and 91.8% (CI 90.2-93.5%) respectively, and for referable DR, these values were 84.2% (CI 67.8-100%) and 98.9% (CI 98.3%-99.5%). The MCC, AUC-ROC and the AUC-PR for referable DR were 63.4, 91.2 and 76.2% respectively. AI was most successful in accurately classifying younger children with shorter duration of diabetes. Conclusions Cybersight AI accurately detected any DR and referable DR among children and young adults, despite its algorithms having been trained on adults. The observed high specificity is particularly important to avoid over-referral in low-resource settings. AI may be an effective tool to reduce demands on scarce physician resources for the care of children with diabetes in low-resource settings.
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Affiliation(s)
- Katie Curran
- Centre for Public Health, Queens University Belfast, Belfast, UK
| | | | - Bedowra Zabeen
- Department of Paediatrics, Life for a Child & Changing Diabetes in Children Programme, Bangladesh Institute of Research & Rehabilitation in Diabetes, Endocrine & Metabolic Disorders (BIRDEM), Diabetic Association of Bangladesh, Dhaka, Bangladesh
| | | | | | | | | | - Jennifer L Patnaik
- Orbis International, New York, NY, USA
- Department of Ophthalmology, University of Colorado School of Medicine, Aurora, CO, USA
| | | | | | - Nathan Congdon
- Centre for Public Health, Queens University Belfast, Belfast, UK
- Orbis International, New York, NY, USA
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Tunde Peto
- Centre for Public Health, Queens University Belfast, Belfast, UK
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14
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Wolf RM, Channa R, Lehmann HP, Abramoff MD, Liu TA. Clinical Implementation of Autonomous Artificial Intelligence Systems for Diabetic Eye Exams: Considerations for Success. Clin Diabetes 2023; 42:142-149. [PMID: 38230333 PMCID: PMC10788651 DOI: 10.2337/cd23-0019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Affiliation(s)
- Risa M. Wolf
- Department of Pediatric Endocrinology and Diabetes, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI
| | - Harold P. Lehmann
- Section on Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD
| | - Michael D. Abramoff
- Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA
- Digital Diagnostics, Coralville, IA
| | - T.Y. Alvin Liu
- Wilmer Eye Institute at the Johns Hopkins University School of Medicine, Baltimore, MD
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15
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Tan TF, Thirunavukarasu AJ, Jin L, Lim J, Poh S, Teo ZL, Ang M, Chan RVP, Ong J, Turner A, Karlström J, Wong TY, Stern J, Ting DSW. Artificial intelligence and digital health in global eye health: opportunities and challenges. Lancet Glob Health 2023; 11:e1432-e1443. [PMID: 37591589 DOI: 10.1016/s2214-109x(23)00323-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 06/26/2023] [Accepted: 07/04/2023] [Indexed: 08/19/2023]
Abstract
Global eye health is defined as the degree to which vision, ocular health, and function are maximised worldwide, thereby optimising overall wellbeing and quality of life. Improving eye health is a global priority as a key to unlocking human potential by reducing the morbidity burden of disease, increasing productivity, and supporting access to education. Although extraordinary progress fuelled by global eye health initiatives has been made over the last decade, there remain substantial challenges impeding further progress. The accelerated development of digital health and artificial intelligence (AI) applications provides an opportunity to transform eye health, from facilitating and increasing access to eye care to supporting clinical decision making with an objective, data-driven approach. Here, we explore the opportunities and challenges presented by digital health and AI in global eye health and describe how these technologies could be leveraged to improve global eye health. AI, telehealth, and emerging technologies have great potential, but require specific work to overcome barriers to implementation. We suggest that a global digital eye health task force could facilitate coordination of funding, infrastructural development, and democratisation of AI and digital health to drive progress forwards in this domain.
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Affiliation(s)
- Ting Fang Tan
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore; Singapore National Eye Centre, Singapore General Hospital, Singapore
| | - Arun J Thirunavukarasu
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore; Corpus Christi College, University of Cambridge, Cambridge, UK; School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Liyuan Jin
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore; Duke-NUS Medical School, National University of Singapore, Singapore
| | - Joshua Lim
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore; Singapore National Eye Centre, Singapore General Hospital, Singapore
| | - Stanley Poh
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore; Singapore National Eye Centre, Singapore General Hospital, Singapore
| | - Zhen Ling Teo
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore; Singapore National Eye Centre, Singapore General Hospital, Singapore
| | - Marcus Ang
- Singapore National Eye Centre, Singapore General Hospital, Singapore; Duke-NUS Medical School, National University of Singapore, Singapore
| | - R V Paul Chan
- Illinois Eye and Ear Infirmary, University of Illinois College of Medicine, Urbana-Champaign, IL, USA
| | - Jasmine Ong
- Pharmacy Department, Singapore General Hospital, Singapore
| | - Angus Turner
- Lions Eye Institute, University of Western Australia, Nedlands, WA, Australia
| | - Jonas Karlström
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Tien Yin Wong
- Singapore National Eye Centre, Singapore General Hospital, Singapore; Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Jude Stern
- The International Agency for the Prevention of Blindness, London, UK
| | - Daniel Shu-Wei Ting
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore; Singapore National Eye Centre, Singapore General Hospital, Singapore; Duke-NUS Medical School, National University of Singapore, Singapore.
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16
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Cleland CR, Rwiza J, Evans JR, Gordon I, MacLeod D, Burton MJ, Bascaran C. Artificial intelligence for diabetic retinopathy in low-income and middle-income countries: a scoping review. BMJ Open Diabetes Res Care 2023; 11:e003424. [PMID: 37532460 PMCID: PMC10401245 DOI: 10.1136/bmjdrc-2023-003424] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 07/11/2023] [Indexed: 08/04/2023] Open
Abstract
Diabetic retinopathy (DR) is a leading cause of blindness globally. There is growing evidence to support the use of artificial intelligence (AI) in diabetic eye care, particularly for screening populations at risk of sight loss from DR in low-income and middle-income countries (LMICs) where resources are most stretched. However, implementation into clinical practice remains limited. We conducted a scoping review to identify what AI tools have been used for DR in LMICs and to report their performance and relevant characteristics. 81 articles were included. The reported sensitivities and specificities were generally high providing evidence to support use in clinical practice. However, the majority of studies focused on sensitivity and specificity only and there was limited information on cost, regulatory approvals and whether the use of AI improved health outcomes. Further research that goes beyond reporting sensitivities and specificities is needed prior to wider implementation.
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Affiliation(s)
- Charles R Cleland
- International Centre for Eye Health, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Eye Department, Kilimanjaro Christian Medical Centre, Moshi, United Republic of Tanzania
| | - Justus Rwiza
- Eye Department, Kilimanjaro Christian Medical Centre, Moshi, United Republic of Tanzania
| | - Jennifer R Evans
- International Centre for Eye Health, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Iris Gordon
- International Centre for Eye Health, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - David MacLeod
- Tropical Epidemiology Group, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Matthew J Burton
- International Centre for Eye Health, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
- National Institute for Health Research Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Covadonga Bascaran
- International Centre for Eye Health, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
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17
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Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review. NPJ Digit Med 2022; 5:162. [PMID: 36307479 PMCID: PMC9614192 DOI: 10.1038/s41746-022-00700-y] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 09/29/2022] [Indexed: 02/08/2023] Open
Abstract
In low- and middle-income countries (LMICs), AI has been promoted as a potential means of strengthening healthcare systems by a growing number of publications. We aimed to evaluate the scope and nature of AI technologies in the specific context of LMICs. In this systematic scoping review, we used a broad variety of AI and healthcare search terms. Our literature search included records published between 1st January 2009 and 30th September 2021 from the Scopus, EMBASE, MEDLINE, Global Health and APA PsycInfo databases, and grey literature from a Google Scholar search. We included studies that reported a quantitative and/or qualitative evaluation of a real-world application of AI in an LMIC health context. A total of 10 references evaluating the application of AI in an LMIC were included. Applications varied widely, including: clinical decision support systems, treatment planning and triage assistants and health chatbots. Only half of the papers reported which algorithms and datasets were used in order to train the AI. A number of challenges of using AI tools were reported, including issues with reliability, mixed impacts on workflows, poor user friendliness and lack of adeptness with local contexts. Many barriers exists that prevent the successful development and adoption of well-performing, context-specific AI tools, such as limited data availability, trust and evidence of cost-effectiveness in LMICs. Additional evaluations of the use of AI in healthcare in LMICs are needed in order to identify their effectiveness and reliability in real-world settings and to generate understanding for best practices for future implementations.
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18
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Song A, Johnson NA, Mirzania D, Ayala AM, Muir KW, Thompson AC. Factors Associated with Ophthalmology Referral and Adherence in a Teleretinal Screening Program: Insights from a Federally Qualified Health Center. Clin Ophthalmol 2022; 16:3019-3031. [PMID: 36119392 PMCID: PMC9480601 DOI: 10.2147/opth.s380629] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 08/29/2022] [Indexed: 11/23/2022] Open
Affiliation(s)
- Ailin Song
- Duke University School of Medicine, Durham, NC, USA
| | | | - Delaram Mirzania
- Duke University School of Medicine, Durham, NC, USA
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan Health, Ann Arbor, MI, USA
| | | | - Kelly W Muir
- Department of Ophthalmology, Duke University, Durham, NC, USA
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, USA
| | - Atalie C Thompson
- Department of Ophthalmology, Duke University, Durham, NC, USA
- Wake Forest Baptist Health, Winston Salem, NC, USA
- Correspondence: Atalie C Thompson, Wake Forest Baptist Health, Janeway Tower, 6 Floor, 1 Medical Center Blvd, Winston Salem, NC, 27103, USA, Tel +1 650-868-8050, Email
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