101
|
Acharyya M, Moharana B, Jain S, Tandon M. A double-blinded study for quantifiable assessment of the diagnostic accuracy of AI tool "ADVEN-i" in identifying diseased fundus images including diabetic retinopathy on a retrospective data. Indian J Ophthalmol 2024; 72:S46-S52. [PMID: 38131542 PMCID: PMC10833153 DOI: 10.4103/ijo.ijo_3342_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 04/15/2023] [Accepted: 07/28/2023] [Indexed: 12/23/2023] Open
Abstract
PURPOSE To quantifiably assess the diagnostic accuracy of Adven-I, a proprietary artificial intelligence (AI)-driven diagnostic system that automatically detects diseases from fundus images. The purpose is to quantify the performance of Adven-i in differentiating a nonreferable (within normal limits) image from a referable (diseased fundus) image and further segregating diabetic retinopathy (DR) from the rest of the abnormalities (non-DR) encompassing the wide spectrum of abnormal pathologies. The assessment is carried out in comparison to manual reading as the reference gold standard. Adven-i is the only AI system classifying retinal abnormalities into DR and non-DR classes separately, apart from predicting nonreferable fundus, while most existing systems classify fundus images into referable and nonreferable DR. METHODS The double-blinded study was conducted on retrospective data collected over the course of a year in the ophthalmology outpatient department (OPD) at a top Tier II eyecare hospital in Chandigarh, India. Three vitreoretina specialists who were blinded to one another read the images. The ground-truth was generated on the basis of majority agreement among the readers. An arbitrator's decision was regarded final if all three readers disagreed. RESULTS 2261 fundus images were analyzed by Adven-i. The sensitivity and specificity of Adven-i in diagnosing images with abnormalities were 95.12% and 85.77%, respectively, and for segregating DR from rest of the retinal abnormalities were 91.87% and 85.12%, respectively. CONCLUSIONS AND RELEVANCE Adven-i shows definite promise in automated screening for early diagnosis of referable fundus images including DR. Adven-i can be adopted to scale for mass screening in resource-limited settings.
Collapse
Affiliation(s)
| | - Bruttendu Moharana
- Department of Ophthalmology, Drishti Eye Hospital, Panchkula, Haryana, India
| | - Sahil Jain
- Department of Vitreo-retina Services, Mirchia Laser Eye Clinic, Chandigarh, India
| | - Manjari Tandon
- Department of Retina and Uvea Services, Mirchia Laser Eye Clinic, Chandigarh, India
| |
Collapse
|
102
|
Hu W, Joseph S, Li R, Woods E, Sun J, Shen M, Jan CL, Zhu Z, He M, Zhang L. Population impact and cost-effectiveness of artificial intelligence-based diabetic retinopathy screening in people living with diabetes in Australia: a cost effectiveness analysis. EClinicalMedicine 2024; 67:102387. [PMID: 38314061 PMCID: PMC10837545 DOI: 10.1016/j.eclinm.2023.102387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 11/29/2023] [Accepted: 12/05/2023] [Indexed: 02/06/2024] Open
Abstract
Background We aimed to evaluate the cost-effectiveness of an artificial intelligence-(AI) based diabetic retinopathy (DR) screening system in the primary care setting for both non-Indigenous and Indigenous people living with diabetes in Australia. Methods We performed a cost-effectiveness analysis between January 01, 2022 and August 01, 2023. A decision-analytic Markov model was constructed to simulate DR progression in a population of 1,197,818 non-Indigenous and 65,160 Indigenous Australians living with diabetes aged ≥20 years over 40 years. From a healthcare provider's perspective, we compared current practice to three primary care AI-based screening scenarios-(A) substitution of current manual grading, (B) scaling up to patient acceptance level, and (C) achieving universal screening. Study results were presented as incremental cost-effectiveness ratio (ICER), benefit-cost ratio (BCR), and net monetary benefits (NMB). A Willingness-to-pay (WTP) threshold of AU$50,000 per quality-adjusted life year (QALY) and a discount rate of 3.5% were adopted in this study. Findings With the status quo, the non-Indigenous diabetic population was projected to develop 96,269 blindness cases, resulting in AU$13,039.6 m spending on DR screening and treatment during 2020-2060. In comparison, all three intervention scenarios were effective and cost-saving. In particular, if a universal screening program was to be implemented (Scenario C), it would prevent 38,347 blindness cases, gain 172,090 QALYs and save AU$595.8 m, leading to a BCR of 3.96 and NMB of AU$9,200 m. Similar findings were also reported in the Indigenous population. With the status quo, 3,396 Indigenous individuals would develop blindness, which would cost the health system AU$796.0 m during 2020-2060. All three intervention scenarios were cost-saving for the Indigenous population. Notably, universal AI-based DR screening (Scenario C) would prevent 1,211 blindness cases and gain 9,800 QALYs in the Indigenous population, leading to a saving of AU$19.2 m with a BCR of 1.62 and NMB of AU$509 m. Interpretation Our findings suggest that implementing AI-based DR screening in primary care is highly effective and cost-saving in both Indigenous and non-Indigenous populations. Funding This project received grant funding from the Australian Government: the National Critical Research Infrastructure Initiative, Medical Research Future Fund (MRFAI00035) and the NHMRC Investigator Grant (APP1175405). The contents of the published material are solely the responsibility of the Administering Institution, a participating institution or individual authors and do not reflect the views of the NHMRC. This work was supported by the Global STEM Professorship Scheme (P0046113), the Fundamental Research Funds of the State Key Laboratory of Ophthalmology, Project of Investigation on Health Status of Employees in Financial Industry in Guangzhou, China (Z012014075). The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian State Government. W.H. is supported by the Melbourne Research Scholarship established by the University of Melbourne. The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Collapse
Affiliation(s)
- Wenyi Hu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Sanil Joseph
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Rui Li
- Central Clinical School, Faculty of Medicine, Monash University, Melbourne, VIC, Australia
- Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC, Australia
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, 710061, PR China
| | - Ekaterina Woods
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Jason Sun
- Eyetelligence Pty Ltd., Melbourne, Australia
| | - Mingwang Shen
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, 710061, PR China
| | - Catherine Lingxue Jan
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Zhuoting Zhu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Mingguang He
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong, China
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Lei Zhang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Clinical Medical Research Center, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210008, China
- Central Clinical School, Faculty of Medicine, Monash University, Melbourne, VIC, Australia
- Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC, Australia
| |
Collapse
|
103
|
Talcott KE, Valentim CCS, Perkins SW, Ren H, Manivannan N, Zhang Q, Bagherinia H, Lee G, Yu S, D'Souza N, Jarugula H, Patel K, Singh RP. Automated Detection of Abnormal Optical Coherence Tomography B-scans Using a Deep Learning Artificial Intelligence Neural Network Platform. Int Ophthalmol Clin 2024; 64:115-127. [PMID: 38146885 DOI: 10.1097/iio.0000000000000519] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
|
104
|
ElSayed NA, Aleppo G, Bannuru RR, Bruemmer D, Collins BS, Ekhlaspour L, Gibbons CH, Giurini JM, Hilliard ME, Johnson EL, Khunti K, Lingvay I, Matfin G, McCoy RG, Perry ML, Pilla SJ, Polsky S, Prahalad P, Pratley RE, Segal AR, Seley JJ, Silva PS, Stanton RC, Gabbay RA. 12. Retinopathy, Neuropathy, and Foot Care: Standards of Care in Diabetes-2024. Diabetes Care 2024; 47:S231-S243. [PMID: 38078577 PMCID: PMC10725803 DOI: 10.2337/dc24-s012] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.
Collapse
|
105
|
Yu W, Yang B, Xu S, Gao Y, Huang Y, Wang Z. Diabetic Retinopathy and Cardiovascular Disease: A Literature Review. Diabetes Metab Syndr Obes 2023; 16:4247-4261. [PMID: 38164419 PMCID: PMC10758178 DOI: 10.2147/dmso.s438111] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 12/21/2023] [Indexed: 01/03/2024] Open
Abstract
Diabetic complications can be divided into macrovascular complications such as cardiovascular disease and cerebrovascular disease and microvascular complications such as diabetic retinopathy, diabetic nephropathy and diabetic neuropathy. Among them, cardiovascular disease (CVD) is an important cause of death in diabetic patients. Diabetes retinopathy (DR) is one of the main reasons for the increasing disability rate of diabetes. In recent years, some studies have found that because DR and CVD have a common pathophysiological basis, the occurrence of DR and CVD are inseparable, and to a certain extent, DR can predict the occurrence of CVD. With the development of technology, the fundus parameters of DR can be quantitatively analyzed as an independent risk factor of CVD. In addition, the cytokines related to DR can also be used for early screening of DR. Although many advances have been made in the treatment of CVD, its situation of prevention and treatment is still not optimistic. This review hopes to discuss the feasibility of DR in predicting CVD from the common pathophysiological mechanism of DR and CVD, the new progress of diagnostic techniques for DR, and the biomarkers for early screening of DR.
Collapse
Affiliation(s)
- Wenhua Yu
- Department of Cardiology, Affiliated Hospital of Jiangsu University, Zhenjiang, People’s Republic of China
| | - Bo Yang
- Department of Cardiology, Affiliated Hospital of Jiangsu University, Zhenjiang, People’s Republic of China
| | - Siting Xu
- Department of Cardiology, Affiliated Hospital of Jiangsu University, Zhenjiang, People’s Republic of China
| | - Yun Gao
- Department of Pathology, Affiliated Hospital of Jiangsu University, Zhenjiang, People’s Republic of China
| | - Yan Huang
- Department of Ophthalmology, Affiliated Hospital of Jiangsu University, Zhenjiang, People’s Republic of China
| | - Zhongqun Wang
- Department of Cardiology, Affiliated Hospital of Jiangsu University, Zhenjiang, People’s Republic of China
| |
Collapse
|
106
|
Huang X, Islam MR, Akter S, Ahmed F, Kazami E, Serhan HA, Abd-Alrazaq A, Yousefi S. Artificial intelligence in glaucoma: opportunities, challenges, and future directions. Biomed Eng Online 2023; 22:126. [PMID: 38102597 PMCID: PMC10725017 DOI: 10.1186/s12938-023-01187-8] [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: 08/09/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.
Collapse
Affiliation(s)
- Xiaoqin Huang
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA
| | - Md Rafiqul Islam
- Business Information Systems, Australian Institute of Higher Education, Sydney, Australia
| | - Shanjita Akter
- School of Computer Science, Taylors University, Subang Jaya, Malaysia
| | - Fuad Ahmed
- Department of Computer Science & Engineering, Islamic University of Technology (IUT), Gazipur, Bangladesh
| | - Ehsan Kazami
- Ophthalmology, General Hospital of Mahabad, Urmia University of Medical Sciences, Urmia, Iran
| | - Hashem Abu Serhan
- Department of Ophthalmology, Hamad Medical Corporations, Doha, Qatar
| | - Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Siamak Yousefi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA.
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, USA.
| |
Collapse
|
107
|
Liu L, Li M, Lin D, Yun D, Lin Z, Zhao L, Pang J, Li L, Wu Y, Shang Y, Lin H, Wu X. Protocol to analyze fundus images for multidimensional quality grading and real-time guidance using deep learning techniques. STAR Protoc 2023; 4:102565. [PMID: 37733597 PMCID: PMC10519839 DOI: 10.1016/j.xpro.2023.102565] [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: 07/07/2023] [Revised: 08/09/2023] [Accepted: 08/18/2023] [Indexed: 09/23/2023] Open
Abstract
Data quality issues have been acknowledged as one of the greatest obstacles in medical artificial intelligence research. Here, we present DeepFundus, which employs deep learning techniques to perform multidimensional classification of fundus image quality and provide real-time guidance for on-site image acquisition. We describe steps for data preparation, model training, model inference, model evaluation, and the visualization of results using heatmaps. This protocol can be implemented in Python using either the suggested dataset or a customized dataset. For complete details on the use and execution of this protocol, please refer to Liu et al.1.
Collapse
Affiliation(s)
- Lixue Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Mingyuan Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Duoru Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Dongyuan Yun
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Zhenzhe Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Lanqin Zhao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Jianyu Pang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Longhui Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Yuxuan Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Yuanjun Shang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China; Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Haikou, Hainan, China; Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.
| | - Xiaohang Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China.
| |
Collapse
|
108
|
Wong TY, Tan TE. The Diabetic Retinopathy "Pandemic" and Evolving Global Strategies: The 2023 Friedenwald Lecture. Invest Ophthalmol Vis Sci 2023; 64:47. [PMID: 38153754 PMCID: PMC10756246 DOI: 10.1167/iovs.64.15.47] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 07/30/2023] [Indexed: 12/29/2023] Open
Affiliation(s)
- Tien Yin Wong
- Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore
- Duke-National University of Singapore, Singapore
- Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Tien-En Tan
- Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore
- Duke-National University of Singapore, Singapore
| |
Collapse
|
109
|
Howard T, Ahluwalia R, Papanas N. The Advent of Artificial Intelligence in Diabetic Foot Medicine: A New Horizon, a New Order, or a False Dawn? INT J LOW EXTR WOUND 2023; 22:635-640. [PMID: 34488463 DOI: 10.1177/15347346211041866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In a world where automation is becoming increasingly common, easier collection of mass of data and powerful computer processing has meant a transformation in the field of artificial intelligence (AI). The diabetic foot is a multifactorial problem; its issues render it suitable for analysis, interrogation, and development of AI. The latter has the potential to deliver many solutions to issues of delayed diagnosis, compliance, and defining preventative treatments. We describe the use of AI and the development of artificial neural networks that may supplement the failed networks in the diabetic foot. The potential of this technology, current developing applications, and their limitations for diabetic foot care are suggested.
Collapse
Affiliation(s)
| | - Raju Ahluwalia
- King's College Hospital, London, UK
- King's Diabetic Foot Clinic, King's College Hospital, London, UK
| | - Nikolas Papanas
- Democritus University of Thrace, University Hospital of Alexandroupolis, Alexandroupoli, Greece
| |
Collapse
|
110
|
Dow ER, Khan NC, Chen KM, Mishra K, Perera C, Narala R, Basina M, Dang J, Kim M, Levine M, Phadke A, Tan M, Weng K, Do DV, Moshfeghi DM, Mahajan VB, Mruthyunjaya P, Leng T, Myung D. AI-Human Hybrid Workflow Enhances Teleophthalmology for the Detection of Diabetic Retinopathy. OPHTHALMOLOGY SCIENCE 2023; 3:100330. [PMID: 37449051 PMCID: PMC10336195 DOI: 10.1016/j.xops.2023.100330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 05/04/2023] [Accepted: 05/08/2023] [Indexed: 07/18/2023]
Abstract
Objective Detection of diabetic retinopathy (DR) outside of specialized eye care settings is an important means of access to vision-preserving health maintenance. Remote interpretation of fundus photographs acquired in a primary care or other nonophthalmic setting in a store-and-forward manner is a predominant paradigm of teleophthalmology screening programs. Artificial intelligence (AI)-based image interpretation offers an alternative means of DR detection. IDx-DR (Digital Diagnostics Inc) is a Food and Drug Administration-authorized autonomous testing device for DR. We evaluated the diagnostic performance of IDx-DR compared with human-based teleophthalmology over 2 and a half years. Additionally, we evaluated an AI-human hybrid workflow that combines AI-system evaluation with human expert-based assessment for referable cases. Design Prospective cohort study and retrospective analysis. Participants Diabetic patients ≥ 18 years old without a prior DR diagnosis or DR examination in the past year presenting for routine DR screening in a primary care clinic. Methods Macula-centered and optic nerve-centered fundus photographs were evaluated by an AI algorithm followed by consensus-based overreading by retina specialists at the Stanford Ophthalmic Reading Center. Detection of more-than-mild diabetic retinopathy (MTMDR) was compared with in-person examination by a retina specialist. Main Outcome Measures Sensitivity, specificity, accuracy, positive predictive value, and gradability achieved by the AI algorithm and retina specialists. Results The AI algorithm had higher sensitivity (95.5% sensitivity; 95% confidence interval [CI], 86.7%-100%) but lower specificity (60.3% specificity; 95% CI, 47.7%-72.9%) for detection of MTMDR compared with remote image interpretation by retina specialists (69.5% sensitivity; 95% CI, 50.7%-88.3%; 96.9% specificity; 95% CI, 93.5%-100%). Gradability of encounters was also lower for the AI algorithm (62.5%) compared with retina specialists (93.1%). A 2-step AI-human hybrid workflow in which the AI algorithm initially rendered an assessment followed by overread by a retina specialist of MTMDR-positive encounters resulted in a sensitivity of 95.5% (95% CI, 86.7%-100%) and a specificity of 98.2% (95% CI, 94.6%-100%). Similarly, a 2-step overread by retina specialists of AI-ungradable encounters improved gradability from 63.5% to 95.6% of encounters. Conclusions Implementation of an AI-human hybrid teleophthalmology workflow may both decrease reliance on human specialist effort and improve diagnostic accuracy. Financial Disclosures Proprietary or commercial disclosure may be found after the references.
Collapse
Affiliation(s)
- Eliot R. Dow
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
- Veterans Affairs Palo Alto Health Care System, Palo Alto, California
| | - Nergis C. Khan
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Karen M. Chen
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Kapil Mishra
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Chandrashan Perera
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Ramsudha Narala
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Marina Basina
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Jimmy Dang
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Michael Kim
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Marcie Levine
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Anuradha Phadke
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Marilyn Tan
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Kirsti Weng
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Diana V. Do
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Darius M. Moshfeghi
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Vinit B. Mahajan
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
- Veterans Affairs Palo Alto Health Care System, Palo Alto, California
| | - Prithvi Mruthyunjaya
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Theodore Leng
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - David Myung
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| |
Collapse
|
111
|
Bora A, Tiwari R, Bavishi P, Virmani S, Huang R, Traynis I, Corrado GS, Peng L, Webster DR, Varadarajan AV, Pattanapongpaiboon W, Chopra R, Ruamviboonsuk P. Risk Stratification for Diabetic Retinopathy Screening Order Using Deep Learning: A Multicenter Prospective Study. Transl Vis Sci Technol 2023; 12:11. [PMID: 38079169 PMCID: PMC10715315 DOI: 10.1167/tvst.12.12.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 10/23/2023] [Indexed: 12/18/2023] Open
Abstract
Purpose Real-world evaluation of a deep learning model that prioritizes patients based on risk of progression to moderate or worse (MOD+) diabetic retinopathy (DR). Methods This nonrandomized, single-arm, prospective, interventional study included patients attending DR screening at four centers across Thailand from September 2019 to January 2020, with mild or no DR. Fundus photographs were input into the model, and patients were scheduled for their subsequent screening from September 2020 to January 2021 in order of predicted risk. Evaluation focused on model sensitivity, defined as correctly ranking patients that developed MOD+ within the first 50% of subsequent screens. Results We analyzed 1,757 patients, of which 52 (3.0%) developed MOD+. Using the model-proposed order, the model's sensitivity was 90.4%. Both the model-proposed order and mild/no DR plus HbA1c had significantly higher sensitivity than the random order (P < 0.001). Excluding one major (rural) site that had practical implementation challenges, the remaining sites included 567 patients and 15 (2.6%) developed MOD+. Here, the model-proposed order achieved 86.7% versus 73.3% for the ranking that used DR grade and hemoglobin A1c. Conclusions The model can help prioritize follow-up visits for the largest subgroups of DR patients (those with no or mild DR). Further research is needed to evaluate the impact on clinical management and outcomes. Translational Relevance Deep learning demonstrated potential for risk stratification in DR screening. However, real-world practicalities must be resolved to fully realize the benefit.
Collapse
Affiliation(s)
| | | | | | | | | | - Ilana Traynis
- Work done at Google via Advanced Clinical, Deerfield, IL, USA
| | | | | | | | | | | | | | - Paisan Ruamviboonsuk
- Department of Ophthalmology, College of Medicine, Rangsit University, Rajavithi Hospital, Bangkok, Thailand
| |
Collapse
|
112
|
Drezga-Kleiminger M, Demaree-Cotton J, Koplin J, Savulescu J, Wilkinson D. Should AI allocate livers for transplant? Public attitudes and ethical considerations. BMC Med Ethics 2023; 24:102. [PMID: 38012660 PMCID: PMC10683249 DOI: 10.1186/s12910-023-00983-0] [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: 09/04/2023] [Accepted: 11/14/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Allocation of scarce organs for transplantation is ethically challenging. Artificial intelligence (AI) has been proposed to assist in liver allocation, however the ethics of this remains unexplored and the view of the public unknown. The aim of this paper was to assess public attitudes on whether AI should be used in liver allocation and how it should be implemented. METHODS We first introduce some potential ethical issues concerning AI in liver allocation, before analysing a pilot survey including online responses from 172 UK laypeople, recruited through Prolific Academic. FINDINGS Most participants found AI in liver allocation acceptable (69.2%) and would not be less likely to donate their organs if AI was used in allocation (72.7%). Respondents thought AI was more likely to be consistent and less biased compared to humans, although were concerned about the "dehumanisation of healthcare" and whether AI could consider important nuances in allocation decisions. Participants valued accuracy, impartiality, and consistency in a decision-maker, more than interpretability and empathy. Respondents were split on whether AI should be trained on previous decisions or programmed with specific objectives. Whether allocation decisions were made by transplant committee or AI, participants valued consideration of urgency, survival likelihood, life years gained, age, future medication compliance, quality of life, future alcohol use and past alcohol use. On the other hand, the majority thought the following factors were not relevant to prioritisation: past crime, future crime, future societal contribution, social disadvantage, and gender. CONCLUSIONS There are good reasons to use AI in liver allocation, and our sample of participants appeared to support its use. If confirmed, this support would give democratic legitimacy to the use of AI in this context and reduce the risk that donation rates could be affected negatively. Our findings on specific ethical concerns also identify potential expectations and reservations laypeople have regarding AI in this area, which can inform how AI in liver allocation could be best implemented.
Collapse
Affiliation(s)
- Max Drezga-Kleiminger
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
- Oxford Uehiro Centre for Practical Ethics, Faculty of Philosophy, University of Oxford, Oxford, OX1 2JD, UK
| | - Joanna Demaree-Cotton
- Oxford Uehiro Centre for Practical Ethics, Faculty of Philosophy, University of Oxford, Oxford, OX1 2JD, UK
| | - Julian Koplin
- Monash Bioethics Centre, Monash University, Melbourne, Australia
| | - Julian Savulescu
- Oxford Uehiro Centre for Practical Ethics, Faculty of Philosophy, University of Oxford, Oxford, OX1 2JD, UK
- Murdoch Children's Research Institute, Melbourne, Australia
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Dominic Wilkinson
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
- Oxford Uehiro Centre for Practical Ethics, Faculty of Philosophy, University of Oxford, Oxford, OX1 2JD, UK.
- Murdoch Children's Research Institute, Melbourne, Australia.
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- John Radcliffe Hospital, Oxford, UK.
| |
Collapse
|
113
|
Spear J, Ehrenfeld JM, Miller BJ. Applications of Artificial Intelligence in Health Care Delivery. J Med Syst 2023; 47:121. [PMID: 37975946 PMCID: PMC10656306 DOI: 10.1007/s10916-023-02018-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: 09/09/2023] [Accepted: 11/07/2023] [Indexed: 11/19/2023]
Abstract
Health care costs now comprise nearly one-fifth of the United States' gross domestic product, with the last 25 years marked by rising administrative costs, a lack of labor productivity growth, and rising patient and physician dissatisfaction. Policy experts have responded with a series of reforms that have - ironically - increased patient and physician administrative burden with little meaningful effect on cost and quality. Artificial intelligence (AI), a topic of great consternation, can serve as the "wheat thresher" for health care delivery, empowering and freeing both patients and physicians by decreasing administrative burden and improving labor productivity. In this Viewpoint, we discuss three principal areas where AI poses an unprecedented opportunity to reduce cost, improve care, and markedly enhance the patient and physician experience: (1) automation of administrative process, (2) augmentation of clinical practice, and (3) automation of elements of clinical practice.
Collapse
Affiliation(s)
- Joseph Spear
- University of Oklahoma College of Medicine, Oklahoma City, OK, USA
| | - Jesse M Ehrenfeld
- The Medical College of Wisconsin, Milwaukee, WI, USA
- The Advancing a Healthier Wisconsin Endowment, Milwaukee, WI, USA
| | - Brian J Miller
- Division of Hospital Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, 600 N. Wolfe Street Meyer 8-143, Baltimore, MD, 21287, USA.
- American Enterprise Institute, Washington, DC, USA.
| |
Collapse
|
114
|
Higuchi M, Nagata T, Iwabuchi K, Sano A, Maekawa H, Idaka T, Yamasaki M, Seko C, Sato A, Suzuki J, Anzai Y, Yabuki T, Saito T, Suzuki H. Development of a novel artificial intelligence algorithm to detect pulmonary nodules on chest radiography. Fukushima J Med Sci 2023; 69:177-183. [PMID: 37853640 PMCID: PMC10694515 DOI: 10.5387/fms.2023-14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 09/15/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND In this study, we aimed to develop a novel artificial intelligence (AI) algorithm to support pulmonary nodule detection, which will enable physicians to efficiently interpret chest radiographs for lung cancer diagnosis. METHODS We analyzed chest X-ray images obtained from a health examination center in Fukushima and the National Institutes of Health (NIH) Chest X-ray 14 dataset. We categorized these data into two types: type A included both Fukushima and NIH datasets, and type B included only the Fukushima dataset. We also demonstrated pulmonary nodules in the form of a heatmap display on each chest radiograph and calculated the positive probability score as an index value. RESULTS Our novel AI algorithms had a receiver operating characteristic (ROC) area under the curve (AUC) of 0.74, a sensitivity of 0.75, and a specificity of 0.60 for the type A dataset. For the type B dataset, the respective values were 0.79, 0.72, and 0.74. The algorithms in both the type A and B datasets were superior to the accuracy of radiologists and similar to previous studies. CONCLUSIONS The proprietary AI algorithms had a similar accuracy for interpreting chest radiographs when compared with previous studies and radiologists. Especially, we could train a high quality AI algorithm, even with our small type B data set. However, further studies are needed to improve and further validate the accuracy of our AI algorithm.
Collapse
Affiliation(s)
- Mitsunori Higuchi
- Department of Thoracic Surgery, Aizu Medical Center, Fukushima Medical University
| | - Takeshi Nagata
- University of Tsukuba School of Integrative and Global Majors
- Mizuho Research and Technologies, Ltd.
| | | | | | | | | | | | | | - Atsushi Sato
- Fukushima Preservative Service Association of Health
| | - Junzo Suzuki
- Fukushima Preservative Service Association of Health
| | | | | | - Takuro Saito
- Department of Surgery, Aizu Medical Center, Fukushima Medical University
| | - Hiroyuki Suzuki
- Department of Chest Surgery, Fukushima Medical University School of Medicine
| |
Collapse
|
115
|
McCradden MD, Joshi S, Anderson JA, London AJ. A normative framework for artificial intelligence as a sociotechnical system in healthcare. PATTERNS (NEW YORK, N.Y.) 2023; 4:100864. [PMID: 38035190 PMCID: PMC10682751 DOI: 10.1016/j.patter.2023.100864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
Artificial intelligence (AI) tools are of great interest to healthcare organizations for their potential to improve patient care, yet their translation into clinical settings remains inconsistent. One of the reasons for this gap is that good technical performance does not inevitably result in patient benefit. We advocate for a conceptual shift wherein AI tools are seen as components of an intervention ensemble. The intervention ensemble describes the constellation of practices that, together, bring about benefit to patients or health systems. Shifting from a narrow focus on the tool itself toward the intervention ensemble prioritizes a "sociotechnical" vision for translation of AI that values all components of use that support beneficial patient outcomes. The intervention ensemble approach can be used for regulation, institutional oversight, and for AI adopters to responsibly and ethically appraise, evaluate, and use AI tools.
Collapse
Affiliation(s)
- Melissa D. McCradden
- Department of Bioethics, The Hospital for Sick Children, Toronto, ON, Canada
- Genetics & Genome Biology Research Program, Peter Gilgan Center for Research & Learning, Toronto, ON, Canada
- Division of Clinical & Public Health, Dalla Lana School of Public Health, Toronto, ON, Canada
| | - Shalmali Joshi
- Department of Biomedical Informatics, Department of Computer Science (Affliate), Data Science Institute, Columbia University, New York, NY, USA
| | - James A. Anderson
- Department of Bioethics, The Hospital for Sick Children, Toronto, ON, Canada
- Institute for Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Alex John London
- Department of Philosophy and Center for Ethics and Policy, Carnegie Mellon University, Pittsburgh, PA, USA
| |
Collapse
|
116
|
Phatak S, Chakraborty S, Goel P. Computer vision detects inflammatory arthritis in standardized smartphone photographs in an Indian patient cohort. Front Med (Lausanne) 2023; 10:1280462. [PMID: 38020147 PMCID: PMC10666644 DOI: 10.3389/fmed.2023.1280462] [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: 08/20/2023] [Accepted: 10/12/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction Computer vision extracts meaning from pixelated images and holds promise in automating various clinical tasks. Convolutional neural networks (CNNs), a deep learning network used therein, have shown promise in analyzing X-ray images and joint photographs. We studied the performance of a CNN on standardized smartphone photographs in detecting inflammation in three hand joints and compared it to a rheumatologist's diagnosis. Methods We enrolled 100 consecutive patients with inflammatory arthritis with an onset period of less than 2 years, excluding those with deformities. Each patient was examined by a rheumatologist, and the presence of synovitis in each joint was recorded. Hand photographs were taken in a standardized manner, anonymized, and cropped to include joints of interest. A ResNet-101 backbone modified for two class outputs (inflamed or not) was used for training. We also tested a hue-augmented dataset. We reported accuracy, sensitivity, and specificity for three joints: wrist, index finger proximal interphalangeal (IFPIP), and middle finger proximal interphalangeal (MFPIP), taking the rheumatologist's opinion as the gold standard. Results The cohort consisted of 100 individuals, of which 22 of them were men, with a mean age of 49.7 (SD 12.9) years. The majority of the cohort (n = 68, 68%) had rheumatoid arthritis. The wrist (125/200, 62.5%), MFPIP (94/200, 47%), and IFPIP (83/200, 41.5%) were the three most commonly inflamed joints. The CNN achieved the highest accuracy, sensitivity, and specificity in detecting synovitis in the MFPIP (83, 77, and 88%, respectively), followed by the IFPIP (74, 74, and 75%, respectively) and the wrist (62, 90, and 21%, respectively). Discussion We have demonstrated that computer vision was able to detect inflammation in three joints of the hand with reasonable accuracy on standardized photographs despite a small dataset. Feature engineering was not required, and the CNN worked despite a diversity in clinical diagnosis. Larger datasets are likely to improve accuracy and help explain the basis of classification. These data suggest a potential use of computer vision in screening and follow-up of inflammatory arthritis.
Collapse
Affiliation(s)
| | | | - Pranay Goel
- Indian Institute of Science, Education and Research, Pune, India
| |
Collapse
|
117
|
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.
Collapse
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)
Collapse
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
| |
Collapse
|
118
|
Daich Varela M, Sen S, De Guimaraes TAC, Kabiri N, Pontikos N, Balaskas K, Michaelides M. Artificial intelligence in retinal disease: clinical application, challenges, and future directions. Graefes Arch Clin Exp Ophthalmol 2023; 261:3283-3297. [PMID: 37160501 PMCID: PMC10169139 DOI: 10.1007/s00417-023-06052-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/20/2023] [Accepted: 03/24/2023] [Indexed: 05/11/2023] Open
Abstract
Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans.
Collapse
Affiliation(s)
- Malena Daich Varela
- UCL Institute of Ophthalmology, London, UK
- Moorfields Eye Hospital, London, UK
| | | | | | | | - Nikolas Pontikos
- UCL Institute of Ophthalmology, London, UK
- Moorfields Eye Hospital, London, UK
| | | | - Michel Michaelides
- UCL Institute of Ophthalmology, London, UK.
- Moorfields Eye Hospital, London, UK.
| |
Collapse
|
119
|
Hasan SU, Siddiqui MAR. Diagnostic accuracy of smartphone-based artificial intelligence systems for detecting diabetic retinopathy: A systematic review and meta-analysis. Diabetes Res Clin Pract 2023; 205:110943. [PMID: 37805002 DOI: 10.1016/j.diabres.2023.110943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 07/28/2023] [Accepted: 10/05/2023] [Indexed: 10/09/2023]
Abstract
AIMS Diabetic retinopathy (DR) is a major cause of blindness globally, early detection is critical to prevent vision loss. Traditional screening that, rely on human experts are, however, costly, and time-consuming. The purpose of this systematic review is to assess the diagnostic accuracy of smartphone-based artificial intelligence(AI) systems for DR detection. METHODS Literature review was conducted on MEDLINE, Embase, Scopus, CINAHL Plus, and Cochrane from inception to December 2022. We included diagnostic test accuracy studies evaluating the use of smartphone-based AI algorithms for DR screening in patients with diabetes, with expert human grader as the reference standard. Random-effects model was used to pool sensitivity and specificity. Any DR(ADR) and referable DR(RDR) were analyzed separately. RESULTS Out of 968 identified articles, six diagnostic test accuracy studies met our inclusion criteria, comprising 3,931 patients. Four of these studies used the Medios AI algorithm. The pooled sensitivity and specificity for diagnosis of ADR were 88 % and 91.5 % respectively and for diagnosis of RDR were 98.2 % and 81.2 % respectively. The overall risk of bias across the studies was low. CONCLUSIONS Smartphone-based AI algorithms show high diagnostic accuracy for detecting DR. However, more high-quality comparative studies are needed to evaluate the effectiveness in real-world clinical settings.
Collapse
Affiliation(s)
- S Umar Hasan
- Department of Ophthalmology and Visual Sciences, Aga Khan University Hospital, National Stadium Road, Karachi, Pakistan
| | - M A Rehman Siddiqui
- Department of Ophthalmology and Visual Sciences, Aga Khan University Hospital, National Stadium Road, Karachi, Pakistan.
| |
Collapse
|
120
|
Blair JPM, Rodriguez JN, Lasagni Vitar RM, Stadelmann MA, Abreu-González R, Donate J, Ciller C, Apostolopoulos S, Bermudez C, De Zanet S. Development of LuxIA, a Cloud-Based AI Diabetic Retinopathy Screening Tool Using a Single Color Fundus Image. Transl Vis Sci Technol 2023; 12:38. [PMID: 38032322 PMCID: PMC10691390 DOI: 10.1167/tvst.12.11.38] [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: 03/03/2023] [Accepted: 10/23/2023] [Indexed: 12/01/2023] Open
Abstract
Purpose Diabetic retinopathy (DR) is the leading cause of vision impairment in working-age adults. Automated screening can increase DR detection at early stages at relatively low costs. We developed and evaluated a cloud-based screening tool that uses artificial intelligence (AI), the LuxIA algorithm, to detect DR from a single fundus image. Methods Color fundus images that were previously graded by expert readers were collected from the Canarian Health Service (Retisalud) and used to train LuxIA, a deep-learning-based algorithm for the detection of more than mild DR. The algorithm was deployed in the Discovery cloud platform to evaluate each test set. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were computed using a bootstrapping method to evaluate the algorithm performance and compared through different publicly available datasets. A usability test was performed to assess the integration into a clinical tool. Results Three separate datasets, Messidor-2, APTOS, and a holdout set from Retisalud were evaluated. Mean sensitivity and specificity with 95% confidence intervals (CIs) reached for these three datasets were 0.901 (0.901-0.902) and 0.955 (0.955-0.956), 0.995 (0.995-0.995) and 0.821 (0.821-0.823), and 0.911 (0.907-0.912) and 0.880 (0.879-0.880), respectively. The usability test confirmed the successful integration of LuxIA into Discovery. Conclusions Clinical data were used to train the deep-learning-based algorithm LuxIA to an expert-level performance. The whole process (image uploading and analysis) was integrated into the cloud-based platform Discovery, allowing more patients to have access to expert-level screening tools. Translational Relevance Using the cloud-based LuxIA tool as part of a screening program may give diabetic patients greater access to specialist-level decisions, without the need for consultation.
Collapse
Affiliation(s)
| | - Jose Natan Rodriguez
- Department of Information Technology, Nuestra Señora de la Candelaria University Hospital, Santa Cruz de Tenerife, Canarias, Spain
| | | | | | - Rodrigo Abreu-González
- Department of Ophthalmology, Nuestra Señora de la Candelaria University Hospital, Santa Cruz de Tenerife, Canarias, Spain
| | - Juan Donate
- Department of Ophthalmology, Nuestra Señora de la Candelaria University Hospital, Santa Cruz de Tenerife, Canarias, Spain
| | | | | | - Carlos Bermudez
- Department of Information Technology, Nuestra Señora de la Candelaria University Hospital, Santa Cruz de Tenerife, Canarias, Spain
| | | |
Collapse
|
121
|
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.
Collapse
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
| | | |
Collapse
|
122
|
Winkelman J, Nguyen D, vanSonnenberg E, Kirk A, Lieberman S. Artificial Intelligence (AI) in pediatric endocrinology. J Pediatr Endocrinol Metab 2023; 36:903-908. [PMID: 37589444 DOI: 10.1515/jpem-2023-0287] [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: 06/18/2023] [Accepted: 08/03/2023] [Indexed: 08/18/2023]
Abstract
Artificial Intelligence (AI) is integrating itself throughout the medical community. AI's ability to analyze complex patterns and interpret large amounts of data will have considerable impact on all areas of medicine, including pediatric endocrinology. In this paper, we review and update the current studies of AI in pediatric endocrinology. Specific topics that are addressed include: diabetes management, bone growth, metabolism, obesity, and puberty. Becoming knowledgeable and comfortable with AI will assist pediatric endocrinologists, the goal of the paper.
Collapse
Affiliation(s)
| | - Diep Nguyen
- University of Arizona College of Medicine Phoenix, Phoenix, USA
| | - Eric vanSonnenberg
- University of Arizona College of Medicine Phoenix, Phoenix, USA
- From the Departments of Radiology, University of Arizona College of Medicine Phoenix, Phoenix, USA
- Student Affairs, University of Arizona College of Medicine Phoenix, Phoenix, USA
| | - Alison Kirk
- University of Arizona College of Medicine Phoenix, Phoenix, USA
- Student Affairs, University of Arizona College of Medicine Phoenix, Phoenix, USA
- Pediatrics, University of Arizona College of Medicine Phoenix, Phoenix, USA
| | - Steven Lieberman
- University of Arizona College of Medicine Phoenix, Phoenix, USA
- Internal Medicine (Division of Endocrinology), University of Arizona College of Medicine Phoenix, Phoenix, USA
| |
Collapse
|
123
|
Guan Z, Li H, Liu R, Cai C, Liu Y, Li J, Wang X, Huang S, Wu L, Liu D, Yu S, Wang Z, Shu J, Hou X, Yang X, Jia W, Sheng B. Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. Cell Rep Med 2023; 4:101213. [PMID: 37788667 PMCID: PMC10591058 DOI: 10.1016/j.xcrm.2023.101213] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 08/07/2023] [Accepted: 09/08/2023] [Indexed: 10/05/2023]
Abstract
The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.
Collapse
Affiliation(s)
- Zhouyu Guan
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Huating Li
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Ruhan Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Furong Laboratory, Changsha, Hunan 41000, China
| | - Chun Cai
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Yuexing Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Jiajia Li
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiangning Wang
- Department of Ophthalmology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
| | - Shan Huang
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Liang Wu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Dan Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Shujie Yu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Zheyuan Wang
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jia Shu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xuhong Hou
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Xiaokang Yang
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Weiping Jia
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China.
| | - Bin Sheng
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
| |
Collapse
|
124
|
Li L, Lin D, Lin Z, Li M, Lian Z, Zhao L, Wu X, Liu L, Liu J, Wei X, Luo M, Zeng D, Yan A, Iao WC, Shang Y, Xu F, Xiang W, He M, Fu Z, Wang X, Deng Y, Fan X, Ye Z, Wei M, Zhang J, Liu B, Li J, Ding X, Lin H. DeepQuality improves infant retinopathy screening. NPJ Digit Med 2023; 6:192. [PMID: 37845275 PMCID: PMC10579317 DOI: 10.1038/s41746-023-00943-3] [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: 06/06/2023] [Accepted: 10/05/2023] [Indexed: 10/18/2023] Open
Abstract
Image quality variation is a prominent cause of performance degradation for intelligent disease diagnostic models in clinical applications. Image quality issues are particularly prominent in infantile fundus photography due to poor patient cooperation, which poses a high risk of misdiagnosis. Here, we developed a deep learning-based image quality assessment and enhancement system (DeepQuality) for infantile fundus images to improve infant retinopathy screening. DeepQuality can accurately detect various quality defects concerning integrity, illumination, and clarity with area under the curve (AUC) values ranging from 0.933 to 0.995. It can also comprehensively score the overall quality of each fundus photograph. By analyzing 2,015,758 infantile fundus photographs from real-world settings using DeepQuality, we found that 58.3% of them had varying degrees of quality defects, and large variations were observed among different regions and categories of hospitals. Additionally, DeepQuality provides quality enhancement based on the results of quality assessment. After quality enhancement, the performance of retinopathy of prematurity (ROP) diagnosis of clinicians was significantly improved. Moreover, the integration of DeepQuality and AI diagnostic models can effectively improve the model performance for detecting ROP. This study may be an important reference for the future development of other image-based intelligent disease screening systems.
Collapse
Affiliation(s)
- Longhui Li
- 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
| | - Duoru Lin
- 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.
| | - Zhenzhe Lin
- 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
| | - Mingyuan Li
- 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
| | - Zhangkai Lian
- 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
| | - Lanqin Zhao
- 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
| | - Xiaohang Wu
- 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
| | - Lixue Liu
- 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
| | - Jiali Liu
- 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
| | - Xiaoyue Wei
- 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
| | - Mingjie 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
| | - Danqi Zeng
- 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
| | - Anqi Yan
- 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
| | - Wai Cheng Iao
- 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
| | - Yuanjun Shang
- 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
| | - Fabao Xu
- Department of Ophthalmology, Qilu Hospital, Shandong University, Jinan, Shandong, China
| | - Wei Xiang
- Department of Clinical Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Muchen He
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zhe Fu
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xueyu Wang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yaru Deng
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xinyan Fan
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zhijun Ye
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Meirong Wei
- Department of Ophthalmology, Maternal and Children's Hospital, Liuzhou, Guangxi, China
| | - Jianping Zhang
- Department of Ophthalmology, Maternal and Children's Hospital, Liuzhou, Guangxi, China
| | - Baohai Liu
- Department of Ophthalmology, Maternal and Children's Hospital, Linyi, Shandong, China
| | - Jianqiao Li
- Department of Ophthalmology, Qilu Hospital, Shandong University, Jinan, Shandong, China
| | - Xiaoyan Ding
- 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
| | - Haotian Lin
- 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.
- Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Haikou, Hainan, China.
- Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.
| |
Collapse
|
125
|
Saenz AD, Harned Z, Banerjee O, Abràmoff MD, Rajpurkar P. Autonomous AI systems in the face of liability, regulations and costs. NPJ Digit Med 2023; 6:185. [PMID: 37803209 PMCID: PMC10558567 DOI: 10.1038/s41746-023-00929-1] [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: 05/22/2023] [Accepted: 09/14/2023] [Indexed: 10/08/2023] Open
Abstract
Autonomous AI systems in medicine promise improved outcomes but raise concerns about liability, regulation, and costs. With the advent of large-language models, which can understand and generate medical text, the urgency for addressing these concerns increases as they create opportunities for more sophisticated autonomous AI systems. This perspective explores the liability implications for physicians, hospitals, and creators of AI technology, as well as the evolving regulatory landscape and payment models. Physicians may be favored in malpractice cases if they follow rigorously validated AI recommendations. However, AI developers may face liability for failing to adhere to industry-standard best practices during development and implementation. The evolving regulatory landscape, led by the FDA, seeks to ensure transparency, evaluation, and real-world monitoring of AI systems, while payment models such as MPFS, NTAP, and commercial payers adapt to accommodate them. The widespread adoption of autonomous AI systems can potentially streamline workflows and allow doctors to concentrate on the human aspects of healthcare.
Collapse
Affiliation(s)
- Agustina D Saenz
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Zach Harned
- Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Palo Alto, CA, USA
- Fenwick & West LLP, Mountain View, CA, USA
| | - Oishi Banerjee
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Michael D Abràmoff
- Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, USA
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA
- Digital Diagnostics Inc, Coralville, IA, USA
| | - Pranav Rajpurkar
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
126
|
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.
Collapse
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
| |
Collapse
|
127
|
Tan AH, Donaldson L, Moolla L, Pereira A, Margolin E. Deep learning model to identify homonymous defects on automated perimetry. Br J Ophthalmol 2023; 107:1516-1521. [PMID: 35922127 DOI: 10.1136/bjo-2021-320996] [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: 12/22/2021] [Accepted: 07/19/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND Homonymous visual field (VF) defects are usually an indicator of serious intracranial pathology but may be subtle and difficult to detect. Artificial intelligence (AI) models could play a key role in simplifying the detection of these defects. This study aimed to develop an automated deep learning AI model to accurately identify homonymous VF defects from automated perimetry. METHODS VFs performed on Humphrey field analyser (24-2 algorithm) were collected and run through an in-house optical character recognition program that extracted mean deviation data and prepared it for use in the proposed AI model. The deep learning AI model, Deep Homonymous Classifier, was developed using PyTorch framework and used convolutional neural networks to extract spatial features for binary classification. Total collected dataset underwent 7-fold cross validation for model training and evaluation. To address dataset class imbalance, data augmentation techniques and state-of-the-art loss function that uses complement cross entropy were used to train and enhance the proposed AI model. RESULTS The proposed model was evaluated using 7-fold cross validation and achieved an average accuracy of 87% for detecting homonymous VF defects in previously unseen VFs. Recall, which is a critical value for this model as reducing false negatives is a priority in disease detection, was found to be on average 92%. The calculated F2 score for the proposed model was 0.89 with a Cohen's kappa value of 0.70. CONCLUSION This newly developed deep learning model achieved an overall average accuracy of 87%, making it highly effective in identifying homonymous VF defects on automated perimetry.
Collapse
Affiliation(s)
- Aaron Hao Tan
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Laura Donaldson
- Ophthalmology and Vision Science, University of Toronto, Toronto, Ontario, Canada
| | - Luqmaan Moolla
- College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Austin Pereira
- Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Edward Margolin
- Ophthalmology, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
128
|
Zhang A, Wu Z, Wu E, Wu M, Snyder MP, Zou J, Wu JC. Leveraging physiology and artificial intelligence to deliver advancements in health care. Physiol Rev 2023; 103:2423-2450. [PMID: 37104717 PMCID: PMC10390055 DOI: 10.1152/physrev.00033.2022] [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: 10/17/2022] [Revised: 03/06/2023] [Accepted: 04/25/2023] [Indexed: 04/29/2023] Open
Abstract
Artificial intelligence in health care has experienced remarkable innovation and progress in the last decade. Significant advancements can be attributed to the utilization of artificial intelligence to transform physiology data to advance health care. In this review, we explore how past work has shaped the field and defined future challenges and directions. In particular, we focus on three areas of development. First, we give an overview of artificial intelligence, with special attention to the most relevant artificial intelligence models. We then detail how physiology data have been harnessed by artificial intelligence to advance the main areas of health care: automating existing health care tasks, increasing access to care, and augmenting health care capabilities. Finally, we discuss emerging concerns surrounding the use of individual physiology data and detail an increasingly important consideration for the field, namely the challenges of deploying artificial intelligence models to achieve meaningful clinical impact.
Collapse
Affiliation(s)
- Angela Zhang
- Stanford Cardiovascular Institute, School of Medicine, Stanford University, Stanford, California, United States
- Department of Genetics, School of Medicine, Stanford University, Stanford, California, United States
- Greenstone Biosciences, Palo Alto, California, United States
| | - Zhenqin Wu
- Department of Chemistry, Stanford University, Stanford, California, United States
| | - Eric Wu
- Department of Electrical Engineering, Stanford University, Stanford, California, United States
| | - Matthew Wu
- Greenstone Biosciences, Palo Alto, California, United States
| | - Michael P Snyder
- Department of Genetics, School of Medicine, Stanford University, Stanford, California, United States
| | - James Zou
- Department of Biomedical Informatics, School of Medicine, Stanford University, Stanford, California, United States
- Department of Computer Science, Stanford University, Stanford, California, United States
| | - Joseph C Wu
- Stanford Cardiovascular Institute, School of Medicine, Stanford University, Stanford, California, United States
- Greenstone Biosciences, Palo Alto, California, United States
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, United States
- Department of Radiology, School of Medicine, Stanford University, Stanford, California, United States
| |
Collapse
|
129
|
Akerman M, Choudhary S, Liebmann JM, Cioffi GA, Chen RWS, Thakoor KA. Extracting decision-making features from the unstructured eye movements of clinicians on glaucoma OCT reports and developing AI models to classify expertise. Front Med (Lausanne) 2023; 10:1251183. [PMID: 37841006 PMCID: PMC10571140 DOI: 10.3389/fmed.2023.1251183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 09/14/2023] [Indexed: 10/17/2023] Open
Abstract
This study aimed to investigate the eye movement patterns of ophthalmologists with varying expertise levels during the assessment of optical coherence tomography (OCT) reports for glaucoma detection. Objectives included evaluating eye gaze metrics and patterns as a function of ophthalmic education, deriving novel features from eye-tracking, and developing binary classification models for disease detection and expertise differentiation. Thirteen ophthalmology residents, fellows, and clinicians specializing in glaucoma participated in the study. Junior residents had less than 1 year of experience, while senior residents had 2-3 years of experience. The expert group consisted of fellows and faculty with over 3 to 30+ years of experience. Each participant was presented with a set of 20 Topcon OCT reports (10 healthy and 10 glaucomatous) and was asked to determine the presence or absence of glaucoma and rate their confidence of diagnosis. The eye movements of each participant were recorded as they diagnosed the reports using a Pupil Labs Core eye tracker. Expert ophthalmologists exhibited more refined and focused eye fixations, particularly on specific regions of the OCT reports, such as the retinal nerve fiber layer (RNFL) probability map and circumpapillary RNFL b-scan. The binary classification models developed using the derived features demonstrated high accuracy up to 94.0% in differentiating between expert and novice clinicians. The derived features and trained binary classification models hold promise for improving the accuracy of glaucoma detection and distinguishing between expert and novice ophthalmologists. These findings have implications for enhancing ophthalmic education and for the development of effective diagnostic tools.
Collapse
Affiliation(s)
- Michelle Akerman
- Department of Biomedical Engineering, Columbia University, New York, NY, United States
| | - Sanmati Choudhary
- Department of Computer Science, Columbia University, New York, NY, United States
| | - Jeffrey M. Liebmann
- Edward S. Harkness Eye Institute, Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY, United States
| | - George A. Cioffi
- Edward S. Harkness Eye Institute, Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY, United States
| | - Royce W. S. Chen
- Edward S. Harkness Eye Institute, Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY, United States
| | - Kaveri A. Thakoor
- Department of Biomedical Engineering, Columbia University, New York, NY, United States
- Department of Computer Science, Columbia University, New York, NY, United States
- Edward S. Harkness Eye Institute, Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY, United States
| |
Collapse
|
130
|
Grzybowski A, Rao DP, Brona P, Negiloni K, Krzywicki T, Savoy FM. Diagnostic Accuracy of Automated Diabetic Retinopathy Image Assessment Softwares: IDx-DR and Medios Artificial Intelligence. Ophthalmic Res 2023; 66:1286-1292. [PMID: 37757777 PMCID: PMC10619585 DOI: 10.1159/000534098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023]
Abstract
INTRODUCTION Numerous studies have demonstrated the use of artificial intelligence (AI) for early detection of referable diabetic retinopathy (RDR). A direct comparison of these multiple automated diabetic retinopathy (DR) image assessment softwares (ARIAs) is, however, challenging. We retrospectively compared the performance of two modern ARIAs, IDx-DR and Medios AI. METHODS In this retrospective-comparative study, retinal images with sufficient image quality were run on both ARIAs. They were captured in 811 consecutive patients with diabetes visiting diabetic clinics in Poland. For each patient, four non-mydriatic images, 45° field of view, i.e., two sets of one optic disc and one macula-centered image using Topcon NW400 were captured. Images were manually graded for severity of DR as no DR, any DR (mild non-proliferative diabetic retinopathy [NPDR] or more severe disease), RDR (moderate NPDR or more severe disease and/or clinically significant diabetic macular edema [CSDME]), or sight-threatening DR (severe NPDR or more severe disease and/or CSDME) by certified graders. The ARIA output was compared to manual consensus image grading (reference standard). RESULTS On 807 patients, based on consensus grading, there was no evidence of DR in 543 patients (67%). Any DR was seen in 264 (33%) patients, of which 174 (22%) were RDR and 41 (5%) were sight-threatening DR. The sensitivity of detecting RDR against reference standard grading was 95% (95% CI: 91, 98%) and the specificity was 80% (95% CI: 77, 83%) for Medios AI. They were 99% (95% CI: 96, 100%) and 68% (95% CI: 64, 72%) for IDx-DR, respectively. CONCLUSION Both the ARIAs achieved satisfactory accuracy, with few false negatives. Although false-positive results generate additional costs and workload, missed cases raise the most concern whenever automated screening is debated.
Collapse
Affiliation(s)
- Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland
| | | | - Piotr Brona
- Department of Ophthalmology, Poznan City Hospital, Poznan, Poland
| | - Kalpa Negiloni
- Department of Clinical Research, Remidio Innovative Solutions Pvt Ltd, Bangalore, India,
| | - Tomasz Krzywicki
- Department of Mathematical Methods of Informatics, University of Warmia and Mazury, Olsztyn, Poland
| | - Florian M Savoy
- Department of AI R & D, Medios Technologies, Remidio Innovative Solutions, Singapore, Singapore
| |
Collapse
|
131
|
Nakayama LF, Zago Ribeiro L, Novaes F, Miyawaki IA, Miyawaki AE, de Oliveira JAE, Oliveira T, Malerbi FK, Regatieri CVS, Celi LA, Silva PS. Artificial intelligence for telemedicine diabetic retinopathy screening: a review. Ann Med 2023; 55:2258149. [PMID: 37734417 PMCID: PMC10515659 DOI: 10.1080/07853890.2023.2258149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 08/31/2023] [Indexed: 09/23/2023] Open
Abstract
PURPOSE This study aims to compare artificial intelligence (AI) systems applied in diabetic retinopathy (DR) teleophthalmology screening, currently deployed systems, fairness initiatives and the challenges for implementation. METHODS The review included articles retrieved from PubMed/Medline/EMBASE literature search strategy regarding telemedicine, DR and AI. The screening criteria included human articles in English, Portuguese or Spanish and related to telemedicine and AI for DR screening. The author's affiliations and the study's population income group were classified according to the World Bank Country and Lending Groups. RESULTS The literature search yielded a total of 132 articles, and nine were included after full-text assessment. The selected articles were published between 2004 and 2020 and were grouped as telemedicine systems, algorithms, economic analysis and image quality assessment. Four telemedicine systems that perform a quality assessment, image preprocessing and pathological screening were reviewed. A data and post-deployment bias assessment are not performed in any of the algorithms, and none of the studies evaluate the social impact implementations. There is a lack of representativeness in the reviewed articles, with most authors and target populations from high-income countries and no low-income country representation. CONCLUSIONS Telemedicine and AI hold great promise for augmenting decision-making in medical care, expanding patient access and enhancing cost-effectiveness. Economic studies and social science analysis are crucial to support the implementation of AI in teleophthalmology screening programs. Promoting fairness and generalizability in automated systems combined with telemedicine screening programs is not straightforward. Improving data representativeness, reducing biases and promoting equity in deployment and post-deployment studies are all critical steps in model development.
Collapse
Affiliation(s)
- Luis Filipe Nakayama
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Ophthalmology, São Paulo Federal University, Sao Paulo, Brazil
| | - Lucas Zago Ribeiro
- Department of Ophthalmology, São Paulo Federal University, Sao Paulo, Brazil
| | - Frederico Novaes
- Department of Ophthalmology, São Paulo Federal University, Sao Paulo, Brazil
| | | | | | | | - Talita Oliveira
- Department of Ophthalmology, São Paulo Federal University, Sao Paulo, Brazil
| | | | | | - Leo Anthony Celi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Paolo S. Silva
- Beetham Eye Institute, Joslin Diabetes Centre, Harvard Medical School, Boston, MA, USA
- Philippine Eye Research Institute, University of the Philippines, Manila, Philippines
| |
Collapse
|
132
|
Rao DP, Savoy FM, Tan JZE, Fung BPE, Bopitiya CM, Sivaraman A, Vinekar A. Development and validation of an artificial intelligence based screening tool for detection of retinopathy of prematurity in a South Indian population. Front Pediatr 2023; 11:1197237. [PMID: 37794964 PMCID: PMC10545957 DOI: 10.3389/fped.2023.1197237] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 08/29/2023] [Indexed: 10/06/2023] Open
Abstract
Purpose The primary objective of this study was to develop and validate an AI algorithm as a screening tool for the detection of retinopathy of prematurity (ROP). Participants Images were collected from infants enrolled in the KIDROP tele-ROP screening program. Methods We developed a deep learning (DL) algorithm with 227,326 wide-field images from multiple camera systems obtained from the KIDROP tele-ROP screening program in India over an 11-year period. 37,477 temporal retina images were utilized with the dataset split into train (n = 25,982, 69.33%), validation (n = 4,006, 10.69%), and an independent test set (n = 7,489, 19.98%). The algorithm consists of a binary classifier that distinguishes between the presence of ROP (Stages 1-3) and the absence of ROP. The image labels were retrieved from the daily registers of the tele-ROP program. They consist of per-eye diagnoses provided by trained ROP graders based on all images captured during the screening session. Infants requiring treatment and a proportion of those not requiring urgent referral had an additional confirmatory diagnosis from an ROP specialist. Results Of the 7,489 temporal images analyzed in the test set, 2,249 (30.0%) images showed the presence of ROP. The sensitivity and specificity to detect ROP was 91.46% (95% CI: 90.23%-92.59%) and 91.22% (95% CI: 90.42%-91.97%), respectively, while the positive predictive value (PPV) was 81.72% (95% CI: 80.37%-83.00%), negative predictive value (NPV) was 96.14% (95% CI: 95.60%-96.61%) and the AUROC was 0.970. Conclusion The novel ROP screening algorithm demonstrated high sensitivity and specificity in detecting the presence of ROP. A prospective clinical validation in a real-world tele-ROP platform is under consideration. It has the potential to lower the number of screening sessions required to be conducted by a specialist for a high-risk preterm infant thus significantly improving workflow efficiency.
Collapse
Affiliation(s)
- Divya Parthasarathy Rao
- Artificial Intelligence Research and Development, Remidio Innovative Solutions Inc., Glen Allen, VA, United States
| | - Florian M. Savoy
- Artificial Intelligence Research and Development, Medios Technologies Pvt. Ltd., Singapore, Singapore
| | - Joshua Zhi En Tan
- Artificial Intelligence Research and Development, Medios Technologies Pvt. Ltd., Singapore, Singapore
| | - Brian Pei-En Fung
- Artificial Intelligence Research and Development, Medios Technologies Pvt. Ltd., Singapore, Singapore
| | - Chiran Mandula Bopitiya
- Artificial Intelligence Research and Development, Medios Technologies Pvt. Ltd., Singapore, Singapore
| | - Anand Sivaraman
- Artificial Intelligence Research and Development, Remidio Innovative Solutions Pvt. Ltd., Bangalore, India
| | - Anand Vinekar
- Department of Pediatric Retina, Narayana Nethralaya Eye Institute, Bangalore, India
| |
Collapse
|
133
|
Chen B, Fang XW, Wu MN, Zhu SJ, Zheng B, Liu BQ, Wu T, Hong XQ, Wang JT, Yang WH. Artificial intelligence assisted pterygium diagnosis: current status and perspectives. Int J Ophthalmol 2023; 16:1386-1394. [PMID: 37724272 PMCID: PMC10475638 DOI: 10.18240/ijo.2023.09.04] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 05/24/2023] [Indexed: 09/20/2023] Open
Abstract
Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment. Early and accurate diagnosis is essential for effective management. Recently, artificial intelligence (AI) has shown promising potential in assisting clinicians with pterygium diagnosis. This paper provides an overview of AI-assisted pterygium diagnosis, including the AI techniques used such as machine learning, deep learning, and computer vision. Furthermore, recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection, classification and segmentation were summarized. The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed. The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis, which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.
Collapse
Affiliation(s)
- Bang Chen
- School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, China
| | - Xin-Wen Fang
- School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, China
| | - Mao-Nian Wu
- School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, China
| | - Shao-Jun Zhu
- School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, China
| | - Bo Zheng
- School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, China
| | - Bang-Quan Liu
- College of Digital Technology and Engineering, Ningbo University of Finance & Economics, Ningbo 315000, Zhejiang Province, China
| | - Tao Wu
- Huzhou Institute, Zhejiang University of Technology, Huzhou 313000, Zhejiang Province, China
| | - Xiang-Qian Hong
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China
| | - Jian-Tao Wang
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China
| | - Wei-Hua Yang
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China
| |
Collapse
|
134
|
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
| |
Collapse
|
135
|
Kazemimoghadam M, Yang Z, Chen M, Ma L, Lu W, Gu X. Leveraging global binary masks for structure segmentation in medical images. Phys Med Biol 2023; 68:10.1088/1361-6560/acf2e2. [PMID: 37607564 PMCID: PMC10511220 DOI: 10.1088/1361-6560/acf2e2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 08/22/2023] [Indexed: 08/24/2023]
Abstract
Deep learning (DL) models for medical image segmentation are highly influenced by intensity variations of input images and lack generalization due to primarily utilizing pixels' intensity information for inference. Acquiring sufficient training data is another challenge limiting models' applications. Here, we proposed to leverage the consistency of organs' anatomical position and shape information in medical images. We introduced a framework leveraging recurring anatomical patterns through global binary masks for organ segmentation. Two scenarios were studied: (1) global binary masks were the only input for the U-Net based model, forcing exclusively encoding organs' position and shape information for rough segmentation or localization. (2) Global binary masks were incorporated as an additional channel providing position/shape clues to mitigate training data scarcity. Two datasets of the brain and heart computed tomography (CT) images with their ground-truth were split into (26:10:10) and (12:3:5) for training, validation, and test respectively. The two scenarios were evaluated using full training split as well as reduced subsets of training data. In scenario (1), training exclusively on global binary masks led to Dice scores of 0.77 ± 0.06 and 0.85 ± 0.04 for the brain and heart structures respectively. Average Euclidian distance of 3.12 ± 1.43 mm and 2.5 ± 0.93 mm were obtained relative to the center of mass of the ground truth for the brain and heart structures respectively. The outcomes indicated encoding a surprising degree of position and shape information through global binary masks. In scenario (2), incorporating global binary masks led to significantly higher accuracy relative to the model trained on only CT images in small subsets of training data; the performance improved by 4.3%-125.3% and 1.3%-48.1% for 1-8 training cases of the brain and heart datasets respectively. The findings imply the advantages of utilizing global binary masks for building models that are robust to image intensity variations as well as an effective approach to boost performance when access to labeled training data is highly limited.
Collapse
Affiliation(s)
- Mahdieh Kazemimoghadam
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas TX, 75390 USA
| | - Zi Yang
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas TX, 75390 USA
| | - Mingli Chen
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas TX, 75390 USA
| | - Lin Ma
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas TX, 75390 USA
| | - Weiguo Lu
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas TX, 75390 USA
| | - Xuejun Gu
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas TX, 75390 USA
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305
| |
Collapse
|
136
|
Abràmoff MD, Tarver ME, Loyo-Berrios N, Trujillo S, Char D, Obermeyer Z, Eydelman MB, Maisel WH. Considerations for addressing bias in artificial intelligence for health equity. NPJ Digit Med 2023; 6:170. [PMID: 37700029 PMCID: PMC10497548 DOI: 10.1038/s41746-023-00913-9] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 08/21/2023] [Indexed: 09/14/2023] Open
Abstract
Health equity is a primary goal of healthcare stakeholders: patients and their advocacy groups, clinicians, other providers and their professional societies, bioethicists, payors and value based care organizations, regulatory agencies, legislators, and creators of artificial intelligence/machine learning (AI/ML)-enabled medical devices. Lack of equitable access to diagnosis and treatment may be improved through new digital health technologies, especially AI/ML, but these may also exacerbate disparities, depending on how bias is addressed. We propose an expanded Total Product Lifecycle (TPLC) framework for healthcare AI/ML, describing the sources and impacts of undesirable bias in AI/ML systems in each phase, how these can be analyzed using appropriate metrics, and how they can be potentially mitigated. The goal of these "Considerations" is to educate stakeholders on how potential AI/ML bias may impact healthcare outcomes and how to identify and mitigate inequities; to initiate a discussion between stakeholders on these issues, in order to ensure health equity along the expanded AI/ML TPLC framework, and ultimately, better health outcomes for all.
Collapse
Affiliation(s)
- Michael D Abràmoff
- Departments of Ophthalmology and Visual Sciences, and Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA.
| | - Michelle E Tarver
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Nilsa Loyo-Berrios
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | | | - Danton Char
- Center for Biomedical Ethics, Stanford University School of Medicine, San Francisco, CA, USA
- Department of Anesthesiology, Stanford University School of Medicine, Division of Pediatric Cardiac Anesthesia, San Francisco, CA, USA
| | - Ziad Obermeyer
- School of Public Health, University of California, Berkeley, CA, USA
| | - Malvina B Eydelman
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - William H Maisel
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| |
Collapse
|
137
|
Abdulazeem H, Whitelaw S, Schauberger G, Klug SJ. A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data. PLoS One 2023; 18:e0274276. [PMID: 37682909 PMCID: PMC10491005 DOI: 10.1371/journal.pone.0274276] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 08/29/2023] [Indexed: 09/10/2023] Open
Abstract
With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models' development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer's disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard.
Collapse
Affiliation(s)
- Hebatullah Abdulazeem
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
| | - Sera Whitelaw
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
| | - Gunther Schauberger
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
| | - Stefanie J. Klug
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
| |
Collapse
|
138
|
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.
Collapse
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
| |
Collapse
|
139
|
He S, Bulloch G, Zhang L, Xie Y, Wu W, He Y, Meng W, Shi D, He M. Cross-camera Performance of Deep Learning Algorithms to Diagnose Common Ophthalmic Diseases: A Comparative Study Highlighting Feasibility to Portable Fundus Camera Use. Curr Eye Res 2023; 48:857-863. [PMID: 37246918 DOI: 10.1080/02713683.2023.2215984] [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: 02/22/2023] [Revised: 04/19/2023] [Accepted: 05/14/2023] [Indexed: 05/30/2023]
Abstract
PURPOSE To compare the inter-camera performance and consistency of various deep learning (DL) diagnostic algorithms applied to fundus images taken from desktop Topcon and portable Optain cameras. METHODS Participants over 18 years of age were enrolled between November 2021 and April 2022. Pair-wise fundus photographs from each patient were collected in a single visit; once by Topcon (used as the reference camera) and once by a portable Optain camera (the new target camera). These were analyzed by three previously validated DL models for the detection of diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucomatous optic neuropathy (GON). Ophthalmologists manually analyzed all fundus photos for the presence of DR and these were referred to as the ground truth. Sensitivity, specificity, the area under the curve (AUC) and agreement between cameras (estimated by Cohen's weighted kappa, K) were the primary outcomes of this study. RESULTS A total of 504 patients were recruited. After excluding 12 photographs with matching errors and 59 photographs with low quality, 906 pairs of Topcon-Optain fundus photos were available for algorithm assessment. Topcon and Optain cameras had excellent consistency (Κ=0.80) when applied to the referable DR algorithm, while AMD had moderate consistency (Κ=0.41) and GON had poor consistency (Κ=0.32). For the DR model, Topcon and Optain achieved a sensitivity of 97.70% and 97.67% and a specificity of 97.92% and 97.93%, respectively. There was no significant difference between the two camera models (McNemar's test: x2=0.08, p = .78). CONCLUSION Topcon and Optain cameras had excellent consistency for detecting referable DR, albeit performances for detecting AMD and GON models were unsatisfactory. This study highlights the methods of using pair-wise images to evaluate DL models between reference and new fundus cameras.
Collapse
Affiliation(s)
- Shuang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Gabriella Bulloch
- University of Melbourne, Melbourne, Victoria, Australia
- Centre for Eye Research Australia, Melbourne, Victoria, Australia
| | - Liangxin Zhang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Yiyu Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Weiyu Wu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Yahong He
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Wei Meng
- Eyetelligence Ltd, Melbourne, Victoria, Australia
| | - Danli Shi
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
- 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, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
- University of Melbourne, Melbourne, Victoria, Australia
- Centre for Eye Research Australia, Melbourne, Victoria, Australia
- Eyetelligence Ltd, Melbourne, Victoria, Australia
| |
Collapse
|
140
|
Kale AU, Mills A, Guggenheim E, Gee D, Bodza S, Anumakonda A, Doal R, Williams R, Gallier S, Lee WH, Galsworthy P, Benning M, Fanning H, Keane PA, Denniston AK, Mollan SP. A Datasheet for the INSIGHT Birmingham, Solihull, and Black Country Diabetic Retinopathy Screening Dataset. OPHTHALMOLOGY SCIENCE 2023; 3:100293. [PMID: 37193316 PMCID: PMC10182318 DOI: 10.1016/j.xops.2023.100293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 02/01/2023] [Accepted: 02/21/2023] [Indexed: 02/27/2023]
Abstract
Purpose Diabetic retinopathy (DR) is the most common microvascular complication associated with diabetes mellitus (DM), affecting approximately 40% of this patient population. Early detection of DR is vital to ensure monitoring of disease progression and prompt sight saving treatments as required. This article describes the data contained within the INSIGHT Birmingham, Solihull, and Black Country Diabetic Retinopathy Dataset. Design Dataset descriptor for routinely collected eye screening data. Participants All diabetic patients aged 12 years and older, attending annual digital retinal photography-based screening within the Birmingham, Solihull, and Black Country Eye Screening Programme. Methods The INSIGHT Health Data Research Hub for Eye Health is a National Health Service (NHS)-led ophthalmic bioresource that provides researchers with safe access to anonymized, routinely collected data from contributing NHS hospitals to advance research for patient benefit. This report describes the INSIGHT Birmingham, Solihull, and Black Country DR Screening Dataset, a dataset of anonymized images and linked screening data derived from the United Kingdom's largest regional DR screening program. Main Outcome Measures This dataset consists of routinely collected data from the eye screening program. The data primarily include retinal photographs with the associated DR grading data. Additional data such as corresponding demographic details, information regarding patients' diabetic status, and visual acuity data are also available. Further details regarding available data points are available in the supplementary information, in addition to the INSIGHT webpage included below. Results At the time point of this analysis (December 31, 2019), the dataset comprised 6 202 161 images from 246 180 patients, with a dataset inception date of January 1, 2007. The dataset includes 1 360 547 grading episodes between R0M0 and R3M1. Conclusions This dataset descriptor article summarizes the content of the dataset, how it has been curated, and what its potential uses are. Data are available through a structured application process for research studies that support discovery, clinical evidence analyses, and innovation in artificial intelligence technologies for patient benefit. Further information regarding the data repository and contact details can be found at https://www.insight.hdrhub.org/. Financial Disclosures Proprietary or commercial disclosure may be found after the references.
Collapse
Affiliation(s)
- Aditya U. Kale
- Academic Unit of Ophthalmology, Institute of Inflammation & Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- Ophthalmology Department, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Andrew Mills
- Birmingham Heartlands Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Emily Guggenheim
- Health Informatics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - David Gee
- Health Informatics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Samuel Bodza
- Health Informatics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Aparna Anumakonda
- Health Informatics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Rima Doal
- Health Informatics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Rowena Williams
- Health Informatics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Suzy Gallier
- Health Informatics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Wen Hwa Lee
- INSIGHT Health Data Research Hub for Eye Health, United Kingdom
- Action Against Age-Related Macular Degeneration, London, UK
| | - Paul Galsworthy
- Birmingham Heartlands Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Manjit Benning
- INSIGHT Health Data Research Hub for Eye Health, United Kingdom
- Moorfields Research & Development, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Hilary Fanning
- INSIGHT Health Data Research Hub for Eye Health, United Kingdom
- Research and Development, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Pearse A. Keane
- INSIGHT Health Data Research Hub for Eye Health, United Kingdom
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, London, UK
| | - Alastair K. Denniston
- Academic Unit of Ophthalmology, Institute of Inflammation & Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- Ophthalmology Department, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- INSIGHT Health Data Research Hub for Eye Health, United Kingdom
| | - Susan P. Mollan
- Ophthalmology Department, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- INSIGHT Health Data Research Hub for Eye Health, United Kingdom
| |
Collapse
|
141
|
Chou YB, Kale AU, Lanzetta P, Aslam T, Barratt J, Danese C, Eldem B, Eter N, Gale R, Korobelnik JF, Kozak I, Li X, Li X, Loewenstein A, Ruamviboonsuk P, Sakamoto T, Ting DS, van Wijngaarden P, Waldstein SM, Wong D, Wu L, Zapata MA, Zarranz-Ventura J. Current status and practical considerations of artificial intelligence use in screening and diagnosing retinal diseases: Vision Academy retinal expert consensus. Curr Opin Ophthalmol 2023; 34:403-413. [PMID: 37326222 PMCID: PMC10399944 DOI: 10.1097/icu.0000000000000979] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
PURPOSE OF REVIEW The application of artificial intelligence (AI) technologies in screening and diagnosing retinal diseases may play an important role in telemedicine and has potential to shape modern healthcare ecosystems, including within ophthalmology. RECENT FINDINGS In this article, we examine the latest publications relevant to AI in retinal disease and discuss the currently available algorithms. We summarize four key requirements underlining the successful application of AI algorithms in real-world practice: processing massive data; practicability of an AI model in ophthalmology; policy compliance and the regulatory environment; and balancing profit and cost when developing and maintaining AI models. SUMMARY The Vision Academy recognizes the advantages and disadvantages of AI-based technologies and gives insightful recommendations for future directions.
Collapse
Affiliation(s)
- Yu-Bai Chou
- Department of Ophthalmology, Taipei Veterans General Hospital
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Aditya U. Kale
- Academic Unit of Ophthalmology, Institute of Inflammation & Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Paolo Lanzetta
- Department of Medicine – Ophthalmology, University of Udine
- Istituto Europeo di Microchirurgia Oculare, Udine, Italy
| | - Tariq Aslam
- Division of Pharmacy and Optometry, Faculty of Biology, Medicine and Health, University of Manchester School of Health Sciences, Manchester, UK
| | - Jane Barratt
- International Federation on Ageing, Toronto, Canada
| | - Carla Danese
- Department of Medicine – Ophthalmology, University of Udine
- Department of Ophthalmology, AP-HP Hôpital Lariboisière, Université Paris Cité, Paris, France
| | - Bora Eldem
- Department of Ophthalmology, Hacettepe University, Ankara, Turkey
| | - Nicole Eter
- Department of Ophthalmology, University of Münster Medical Center, Münster, Germany
| | - Richard Gale
- Department of Ophthalmology, York Teaching Hospital NHS Foundation Trust, York, UK
| | - Jean-François Korobelnik
- Service d’ophtalmologie, CHU Bordeaux
- University of Bordeaux, INSERM, BPH, UMR1219, F-33000 Bordeaux, France
| | - Igor Kozak
- Moorfields Eye Hospital Centre, Abu Dhabi, UAE
| | - 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
| | - Xiaoxin Li
- Xiamen Eye Center, Xiamen University, Xiamen, China
| | - Anat Loewenstein
- Division of Ophthalmology, Tel Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Paisan Ruamviboonsuk
- Department of Ophthalmology, College of Medicine, Rangsit University, Rajavithi Hospital, Bangkok, Thailand
| | - Taiji Sakamoto
- Department of Ophthalmology, Kagoshima University, Kagoshima, Japan
| | - Daniel S.W. Ting
- Singapore National Eye Center, Duke-NUS Medical School, Singapore
| | - Peter van Wijngaarden
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | | | - David Wong
- Unity Health Toronto – St. Michael's Hospital, University of Toronto, Toronto, Canada
| | - Lihteh Wu
- Macula, Vitreous and Retina Associates of Costa Rica, San José, Costa Rica
| | | | | |
Collapse
|
142
|
Li L, Zhang W, Tu X, Pang J, Lai IF, Jin C, Cheung CY, Lin H. Application of Artificial Intelligence in Precision Medicine for Diabetic Macular Edema. Asia Pac J Ophthalmol (Phila) 2023; 12:486-494. [PMID: 36650089 DOI: 10.1097/apo.0000000000000583] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 10/06/2022] [Indexed: 01/19/2023] Open
Abstract
Diabetic macular edema (DME) is the primary cause of central vision impairment in patients with diabetes and the leading cause of preventable blindness in working-age people. With the advent of optical coherence tomography and antivascular endothelial growth factor (anti-VEGF) therapy, the diagnosis, evaluation, and treatment of DME were greatly revolutionized in the last decade. However, there is tremendous heterogeneity among DME patients, and 30%-50% of DME patients do not respond well to anti-VEGF agents. In addition, there is no evidence-based and universally accepted administration regimen. The identification of DME patients not responding to anti-VEGF agents and the determination of the optimal administration interval are the 2 major challenges of DME, which are difficult to achieve with the coarse granularity of conventional health care modality. Therefore, more and more retina specialists have pointed out the necessity of introducing precision medicine into the management of DME and have conducted related studies in recent years. One of the most frontier methods is the targeted extraction of individualized disease features from optical coherence tomography images based on artificial intelligence technology, which provides precise evaluation and risk classification of DME. This review aims to provide an overview of the progress of artificial intelligence-enabled precision medicine in automated screening, precise evaluation, prognosis prediction, and follow-up monitoring of DME. Further, the challenges ahead of real-world applications and the future development of precision medicine in DME will be discussed.
Collapse
Affiliation(s)
- Longhui Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong
| | - Weixing Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong
| | - Xueer Tu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong
| | - Jianyu Pang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong
| | | | - Chenjin Jin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong
- Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Haikou, Hainan
- Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| |
Collapse
|
143
|
Thiébaut R, Hejblum B, Mougin F, Tzourio C, Richert L. ChatGPT and beyond with artificial intelligence (AI) in health: Lessons to be learned. Joint Bone Spine 2023; 90:105607. [PMID: 37414138 DOI: 10.1016/j.jbspin.2023.105607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/16/2023] [Accepted: 06/23/2023] [Indexed: 07/08/2023]
Affiliation(s)
- Rodolphe Thiébaut
- Bordeaux Population Health, université Bordeaux, Inserm, U1219, 33000 Bordeaux cedex, France; INRIA, SISTM, 33000 Bordeaux cedex, France; Medical Information Department, CHU de Bordeaux, 33000 Bordeaux cedex, France.
| | - Boris Hejblum
- Bordeaux Population Health, université Bordeaux, Inserm, U1219, 33000 Bordeaux cedex, France; INRIA, SISTM, 33000 Bordeaux cedex, France
| | - Fleur Mougin
- Bordeaux Population Health, université Bordeaux, Inserm, U1219, 33000 Bordeaux cedex, France
| | - Christophe Tzourio
- Bordeaux Population Health, université Bordeaux, Inserm, U1219, 33000 Bordeaux cedex, France; Medical Information Department, CHU de Bordeaux, 33000 Bordeaux cedex, France
| | - Laura Richert
- Bordeaux Population Health, université Bordeaux, Inserm, U1219, 33000 Bordeaux cedex, France; INRIA, SISTM, 33000 Bordeaux cedex, France; Medical Information Department, CHU de Bordeaux, 33000 Bordeaux cedex, France
| |
Collapse
|
144
|
Oganov AC, Seddon I, Jabbehdari S, Uner OE, Fonoudi H, Yazdanpanah G, Outani O, Arevalo JF. Artificial intelligence in retinal image analysis: Development, advances, and challenges. Surv Ophthalmol 2023; 68:905-919. [PMID: 37116544 DOI: 10.1016/j.survophthal.2023.04.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 04/20/2023] [Accepted: 04/24/2023] [Indexed: 04/30/2023]
Abstract
Modern advances in diagnostic technologies offer the potential for unprecedented insight into ophthalmic conditions relating to the retina. We discuss the current landscape of artificial intelligence in retina with respect to screening, diagnosis, and monitoring of retinal pathologies such as diabetic retinopathy, diabetic macular edema, central serous chorioretinopathy, and age-related macular degeneration. We review the methods used in these models and evaluate their performance in both research and clinical contexts and discuss potential future directions for investigation, use of multiple imaging modalities in artificial intelligence algorithms, and challenges in the application of artificial intelligence in retinal pathologies.
Collapse
Affiliation(s)
- Anthony C Oganov
- Department of Ophthalmology, Renaissance School of Medicine, Stony Brook, NY, USA
| | - Ian Seddon
- College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, USA
| | - Sayena Jabbehdari
- Jones Eye Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
| | - Ogul E Uner
- Casey Eye Institute, Department of Ophthalmology, Oregon Health and Science University, Portland, OR, USA
| | - Hossein Fonoudi
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Iranshahr University of Medical Sciences, Iranshahr, Sistan and Baluchestan, Iran
| | - Ghasem Yazdanpanah
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, USA
| | - Oumaima Outani
- Faculty of Medicine and Pharmacy of Rabat, Mohammed 5 University, Rabat, Rabat, Morocco
| | - J Fernando Arevalo
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| |
Collapse
|
145
|
Choi JY, Yoo TK. New era after ChatGPT in ophthalmology: advances from data-based decision support to patient-centered generative artificial intelligence. ANNALS OF TRANSLATIONAL MEDICINE 2023; 11:337. [PMID: 37675304 PMCID: PMC10477620 DOI: 10.21037/atm-23-1598] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 06/28/2023] [Indexed: 09/08/2023]
Affiliation(s)
- Joon Yul Choi
- Department of Biomedical Engineering, Yonsei University, Wonju, South Korea
| | - Tae Keun Yoo
- B&VIIT Eye Center, Seoul, South Korea
- VISUWORKS, Seoul, South Korea
| |
Collapse
|
146
|
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.
Collapse
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
| |
Collapse
|
147
|
Li X, Tan TE, Wong TY, Sun X. Diabetic retinopathy in China: Epidemiology, screening and treatment trends-A review. Clin Exp Ophthalmol 2023; 51:607-626. [PMID: 37381613 DOI: 10.1111/ceo.14269] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 05/26/2023] [Accepted: 06/02/2023] [Indexed: 06/30/2023]
Abstract
Diabetic retinopathy (DR) is the leading cause of vision impairment in the global working-age population. In China, with one-third of the world's diabetes population estimated at 141 million, the blindness prevalence due to DR has increased significantly. The country's geographic variations in socioeconomic status have led to prominent disparities in DR prevalence, screening and management. Reported risk factors for DR in China include the classic ones, such as long diabetes duration, hyperglycaemia, hypertension and rural habitats. There is no national-level DR screening programme in China, but significant pilot efforts are underway for screening innovations. Novel agents with longer durations, noninvasive delivery or multi-target are undergoing clinical trials in China. Although optimised medical insurance policies have enhanced accessibility for expensive therapies like anti-VEGF drugs, further efforts in DR prevention and management in China are required to establish nationwide cost-effective screening programmes, including telemedicine and AI-based solutions, and to improve insurance coverage for related out-of-pocket expenses.
Collapse
Affiliation(s)
- 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
| | - Tien-En Tan
- Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore, Singapore
- Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Tien Y Wong
- Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore, Singapore
- Duke-National University of Singapore Medical School, Singapore, Singapore
- Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Xiaodong Sun
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai, China
- Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China
| |
Collapse
|
148
|
Faes L, Maloca PM, Hatz K, Wolfensberger TJ, Munk MR, Sim DA, Bachmann LM, Schmid MK. Transforming ophthalmology in the digital century-new care models with added value for patients. Eye (Lond) 2023; 37:2172-2175. [PMID: 36460858 PMCID: PMC9735073 DOI: 10.1038/s41433-022-02313-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/31/2022] [Accepted: 11/10/2022] [Indexed: 12/04/2022] Open
Abstract
Ophthalmology faces many challenges in providing effective and meaningful eye care to an ever-increasing group of people. Even health systems that have so far been able to cope with the quantitative patient increase, due to their funding and the availability of highly qualified professionals, and improvements in practice routine efficiency, will be pushed to their limits. Further pressure on care will also be caused by new active substances for the largest group of patients with AMD, the so-called dry form. Treatment availability for this so far untreated group will increase the volume of patients 2-3 times. Without the adaptation of the care structures, this quantitative and qualitative expansion in therapy will inevitably lead to an undersupply.There is increasing scientific evidence that significant efficiency gains in the care of chronic diseases can be achieved through better networking of stakeholders in the healthcare system and greater patient involvement. Digitalization can make an important contribution here. Many technological solutions have been developed in recent years and the time is now ready to exploit this potential. The exceptional setting during the SARS-CoV-2 pandemic has shown many that new technology is available safely, quickly, and effectively. The emergency has catalyzed innovation processes and shown for post-pandemic time after that we are equipped to tackle the challenges in ophthalmic healthcare - ultimately for the benefit of patients and society.
Collapse
Affiliation(s)
- Livia Faes
- Moorfields Eye Hospital, 162 City Rd, London, EC1V 2PD, UK
| | - Peter M Maloca
- Moorfields Eye Hospital NHS Foundation Trust, 162 City Road, London, EC1V 2PD, UK
- Institute of Molecular and Clinical Ophthalmology (IOB), Basel, Switzerland
- OCTlab, University Basel, Mittlere Strasse 91, CH-4056, Basel, Switzerland
- Hirslanden St. Anna im Bahnhof Luzern, Lucerne, Switzerland
| | - Katja Hatz
- Vista Eye Clinic Binningen, Hauptstrasse 55, CH-4102, Binningen, Switzerland
- Faculty of Medicine, University of Basel, Basel, Switzerland
| | | | - Marion R Munk
- Ophthalmology, Inselspital, University Hospital Bern, Bern, Switzerland
- Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | - Dawn A Sim
- Moorfields Ophthalmic Reading Centre and Artificial Intelligence Lab, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Medical Retina Service, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Ophthalmology, University College London, London, UK
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, England
| | - Lucas M Bachmann
- Medignition AG, Engelstrasse 6, 8004, Zurich, Switzerland.
- University of Zurich, CH-8091, Zurich, Switzerland.
| | - Martin K Schmid
- Eye Clinic, Lucerne Cantonal Hospital LUKS, 6000 16, Lucerne, Switzerland
| |
Collapse
|
149
|
Nakayama LF, Mitchell WG, Ribeiro LZ, Dychiao RG, Phanphruk W, Celi LA, Kalua K, Santiago APD, Regatieri CVS, Moraes NSB. Fairness and generalisability in deep learning of retinopathy of prematurity screening algorithms: a literature review. BMJ Open Ophthalmol 2023; 8:e001216. [PMID: 37558406 PMCID: PMC10414056 DOI: 10.1136/bmjophth-2022-001216] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 07/04/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Retinopathy of prematurity (ROP) is a vasoproliferative disease responsible for more than 30 000 blind children worldwide. Its diagnosis and treatment are challenging due to the lack of specialists, divergent diagnostic concordance and variation in classification standards. While artificial intelligence (AI) can address the shortage of professionals and provide more cost-effective management, its development needs fairness, generalisability and bias controls prior to deployment to avoid producing harmful unpredictable results. This review aims to compare AI and ROP study's characteristics, fairness and generalisability efforts. METHODS Our review yielded 220 articles, of which 18 were included after full-text assessment. The articles were classified into ROP severity grading, plus detection, detecting treatment requiring, ROP prediction and detection of retinal zones. RESULTS All the article's authors and included patients are from middle-income and high-income countries, with no low-income countries, South America, Australia and Africa Continents representation.Code is available in two articles and in one on request, while data are not available in any article. 88.9% of the studies use the same retinal camera. In two articles, patients' sex was described, but none applied a bias control in their models. CONCLUSION The reviewed articles included 180 228 images and reported good metrics, but fairness, generalisability and bias control remained limited. Reproducibility is also a critical limitation, with few articles sharing codes and none sharing data. Fair and generalisable ROP and AI studies are needed that include diverse datasets, data and code sharing, collaborative research, and bias control to avoid unpredictable and harmful deployments.
Collapse
Affiliation(s)
- Luis Filipe Nakayama
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Ophthalmology, Sao Paulo Federal University, Sao Paulo, Brazil
| | - William Greig Mitchell
- Department of Ophthalmology, The Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Lucas Zago Ribeiro
- Department of Ophthalmology, Sao Paulo Federal University, Sao Paulo, Brazil
| | - Robyn Gayle Dychiao
- University of the Philippines Manila College of Medicine, Manila, Philippines
| | | | - Leo Anthony Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biostatistics, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
| | - Khumbo Kalua
- Department of Ophthalmology, Blantyre Institute for Community Ophthalmology, BICO, Blantyre, Malawi
| | | | | | | |
Collapse
|
150
|
Uzir MUH, Bukari Z, Al Halbusi H, Lim R, Wahab SN, Rasul T, Thurasamy R, Jerin I, Chowdhury MRK, Tarofder AK, Yaakop AY, Hamid ABA, Haque A, Rauf A, Eneizan B. Applied artificial intelligence: Acceptance-intention-purchase and satisfaction on smartwatch usage in a Ghanaian context. Heliyon 2023; 9:e18666. [PMID: 37560680 PMCID: PMC10407215 DOI: 10.1016/j.heliyon.2023.e18666] [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: 01/28/2023] [Revised: 07/19/2023] [Accepted: 07/24/2023] [Indexed: 08/11/2023] Open
Abstract
Technology and its continuous advancement facilitate human beings to get rid of their criticality and limitation. Applied artificial intelligence (AAI) is one of the latest forms that delimited the limitation of human beings. Smartwatch acts as an applied artificial intelligence to assist various patients to check medical care without going to hospital and physicians. This (three) multiple-study research focused on the intention to use, purchase, and their satisfaction and spread positive word of mouth among others in the Ghanaian. To investigate these issues two renowned theories were underpinned- TAM theory and the Stimulus-Organism-Response (S-O-R). Total 550, 320, and 170 respondents were interviewed with Google forms due to COVID-19 using social media. AI-enabled smartwatch considering Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Perceived Credibility (PC), Perceived Self-Efficacy (PSE), and Perceived Financial Cost (PFC) were significant on intention to adoption and adoption intention on actual purchase. The final study showed device quality, its service level, their usage experience, perceived value, and the extent to which the satisfied customers made positive word of mouth to their friends and family, colleagues and followers. This research is significant in understanding the usage of AI-enabled smartwatches as a device doctor or electronic doctor (e-doctor).
Collapse
Affiliation(s)
- Md Uzir Hossain Uzir
- Marketing Department, Lincoln University College, Petaling Jaya, Selangor, Malaysia
- Marketing Department, Faculty of Business, Economics, and Social Development, University Malaysia Terengganu, Kuala Terengganu, Malaysia
| | - Zakari Bukari
- Department of Marketing and Customer Management, University of Professional Studies, Accra, Ghana
| | - Hussam Al Halbusi
- Department of Management at Ahmed Bin Mohammad Military College, Doha, Qatar
| | - Rodney Lim
- Marketing and E-Commerce, Swinburne University of Technology, Sarawak Campus, Hawthorn, 3122, Australia
| | - Siti Norida Wahab
- Faculty of Business and Management, Universiti Teknologi MARA, 42300, Bandar Puncak Alam, Selangor, Malaysia
| | - Tareq Rasul
- Department of Marketing, Australian Institute of Business (AIB), Adelaide, Australia
| | - Ramayah Thurasamy
- School of Management, Universiti Sains Malaysia, Minden, 11800, Penang, Malaysia
- Department of Information Technology & Management, Daffodil International University, Birulia, Bangladesh
- Department of Management, Sunway University Business School, 47500, Petaling Jaya, Selangor, Malaysia
- University Center for Research & Development (UCRD), Chandigarh University, Ludhiana, 140413, Punjab, India
- Fakulti Ekonomi Dan Pengurusan (FEP), Universiti Kebangsaan Malaysia (UKM), Hulu Langat, Malaysia
- Faculty of Economics and Business, Universitas Indonesia (UI), Depok City, West Java, 16424, Indonesia
- Azman Hashim International Business School, Universiti Teknologi Malaysia (UTM), Iskandar Puteri, Malaysia
- Faculty of Business, Economics and Social Development, Universiti Malaysia Terengganu (UMT), 21300, Kuala Terengganu, Malaysia
| | - Ishraq Jerin
- Putra Business School (PBS), Universiti Putra Malaysia (UPM), 43400, Seri Kembangan, Selangor, Malaysia
| | - M Rezaul Karim Chowdhury
- Faculty of Maritime Studies, Universiti Malaysia Terengganu, 21300, Kuala Terengganu, Terengganu, Malaysia
| | - Arun Kumar Tarofder
- Faculty of Business and Professional Studies, Management and Science University Malaysia, 40100, Shah Alam, Selangor, Malaysia
| | - Azizul Yadi Yaakop
- Faculty of Business, Economics and Social Development, Universiti Malaysia Terengganu, 21300, Kuala Terengganu, Terengganu, Malaysia
| | | | - Ahasanul Haque
- Department of Business Administration, International Islamic University Malaysia, Box No. 10, 50728, Kuala Lumpur, Malaysia
| | | | - Bilal Eneizan
- Business School, Jadara University, Irbid, Jordan
- College of Science and Humanities Studies, Prince Sattam Bin Abdulaziz University, Sulayyil, Saudi Arabia
| |
Collapse
|