Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort study
Summary: Background: By 2050, almost 5 billion people globally are projected to have myopia, of whom 20% are likely to have high myopia with clinically significant risk of sight-threatening complications such as myopic macular degeneration. These are diagnoses that typically require specialist asse...
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-05-01
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| Series: | The Lancet: Digital Health |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589750021000558 |
| _version_ | 1818728840168472576 |
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| author | Tien-En Tan, FRCOphth Ayesha Anees, MSc Cheng Chen, MSc Shaohua Li, PhD Xinxing Xu, PhD Zengxiang Li, PhD Zhe Xiao, PhD Yechao Yang, BSc Xiaofeng Lei, MSc Marcus Ang, FRCS (Ed) Audrey Chia, FRANZCO Shu Yen Lee, FRCS (Ed) Edmund Yick Mun Wong, FRCS (Ed) Ian Yew San Yeo, ProfFRCS (Ed) Yee Ling Wong, PhD Quan V Hoang, MD Ya Xing Wang, MD Mukharram M Bikbov, MD Vinay Nangia, MD Jost B Jonas, ProfMD Yen-Po Chen, MD Wei-Chi Wu, ProfMD Kyoko Ohno-Matsui, ProfMD Tyler Hyungtaek Rim, MD Yih-Chung Tham, PhD Rick Siow Mong Goh, PhD Haotian Lin, ProfMD Hanruo Liu, MD Ningli Wang, ProfMD Weihong Yu, ProfMD Donald Tiang Hwee Tan, ProfFRCS (Ed) Leopold Schmetterer, ProfPhD Ching-Yu Cheng, ProfMD Youxin Chen, ProfMD Chee Wai Wong, MBBS Gemmy Chui Ming Cheung, ProfFRCOphth Seang-Mei Saw, ProfPhD Tien Yin Wong, ProfMD Yong Liu, PhD Daniel Shu Wei Ting, MD |
| author_facet | Tien-En Tan, FRCOphth Ayesha Anees, MSc Cheng Chen, MSc Shaohua Li, PhD Xinxing Xu, PhD Zengxiang Li, PhD Zhe Xiao, PhD Yechao Yang, BSc Xiaofeng Lei, MSc Marcus Ang, FRCS (Ed) Audrey Chia, FRANZCO Shu Yen Lee, FRCS (Ed) Edmund Yick Mun Wong, FRCS (Ed) Ian Yew San Yeo, ProfFRCS (Ed) Yee Ling Wong, PhD Quan V Hoang, MD Ya Xing Wang, MD Mukharram M Bikbov, MD Vinay Nangia, MD Jost B Jonas, ProfMD Yen-Po Chen, MD Wei-Chi Wu, ProfMD Kyoko Ohno-Matsui, ProfMD Tyler Hyungtaek Rim, MD Yih-Chung Tham, PhD Rick Siow Mong Goh, PhD Haotian Lin, ProfMD Hanruo Liu, MD Ningli Wang, ProfMD Weihong Yu, ProfMD Donald Tiang Hwee Tan, ProfFRCS (Ed) Leopold Schmetterer, ProfPhD Ching-Yu Cheng, ProfMD Youxin Chen, ProfMD Chee Wai Wong, MBBS Gemmy Chui Ming Cheung, ProfFRCOphth Seang-Mei Saw, ProfPhD Tien Yin Wong, ProfMD Yong Liu, PhD Daniel Shu Wei Ting, MD |
| author_sort | Tien-En Tan, FRCOphth |
| collection | DOAJ |
| description | Summary: Background: By 2050, almost 5 billion people globally are projected to have myopia, of whom 20% are likely to have high myopia with clinically significant risk of sight-threatening complications such as myopic macular degeneration. These are diagnoses that typically require specialist assessment or measurement with multiple unconnected pieces of equipment. Artificial intelligence (AI) approaches might be effective for risk stratification and to identify individuals at highest risk of visual loss. However, unresolved challenges for AI medical studies remain, including paucity of transparency, auditability, and traceability. Methods: In this retrospective multicohort study, we developed and tested retinal photograph-based deep learning algorithms for detection of myopic macular degeneration and high myopia, using a total of 226 686 retinal images. First we trained and internally validated the algorithms on datasets from Singapore, and then externally tested them on datasets from China, Taiwan, India, Russia, and the UK. We also compared the performance of the deep learning algorithms against six human experts in the grading of a randomly selected dataset of 400 images from the external datasets. As proof of concept, we used a blockchain-based AI platform to demonstrate the real-world application of secure data transfer, model transfer, and model testing across three sites in Singapore and China. Findings: The deep learning algorithms showed robust diagnostic performance with areas under the receiver operating characteristic curves [AUC] of 0·969 (95% CI 0·959–0·977) or higher for myopic macular degeneration and 0·913 (0·906–0·920) or higher for high myopia across the external testing datasets with available data. In the randomly selected dataset, the deep learning algorithms outperformed all six expert graders in detection of each condition (AUC of 0·978 [0·957–0·994] for myopic macular degeneration and 0·973 [0·941–0·995] for high myopia). We also successfully used blockchain technology for data transfer, model transfer, and model testing between sites and across two countries. Interpretation: Deep learning algorithms can be effective tools for risk stratification and screening of myopic macular degeneration and high myopia among the large global population with myopia. The blockchain platform developed here could potentially serve as a trusted platform for performance testing of future AI models in medicine. Funding: None. |
| first_indexed | 2024-12-17T22:36:23Z |
| format | Article |
| id | doaj.art-7d027a4f3e5c45e0ac72665e314891ef |
| institution | Directory Open Access Journal |
| issn | 2589-7500 |
| language | English |
| last_indexed | 2024-12-17T22:36:23Z |
| publishDate | 2021-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | The Lancet: Digital Health |
| spelling | doaj.art-7d027a4f3e5c45e0ac72665e314891ef2022-12-21T21:30:04ZengElsevierThe Lancet: Digital Health2589-75002021-05-0135e317e329Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort studyTien-En Tan, FRCOphth0Ayesha Anees, MSc1Cheng Chen, MSc2Shaohua Li, PhD3Xinxing Xu, PhD4Zengxiang Li, PhD5Zhe Xiao, PhD6Yechao Yang, BSc7Xiaofeng Lei, MSc8Marcus Ang, FRCS (Ed)9Audrey Chia, FRANZCO10Shu Yen Lee, FRCS (Ed)11Edmund Yick Mun Wong, FRCS (Ed)12Ian Yew San Yeo, ProfFRCS (Ed)13Yee Ling Wong, PhD14Quan V Hoang, MD15Ya Xing Wang, MD16Mukharram M Bikbov, MD17Vinay Nangia, MD18Jost B Jonas, ProfMD19Yen-Po Chen, MD20Wei-Chi Wu, ProfMD21Kyoko Ohno-Matsui, ProfMD22Tyler Hyungtaek Rim, MD23Yih-Chung Tham, PhD24Rick Siow Mong Goh, PhD25Haotian Lin, ProfMD26Hanruo Liu, MD27Ningli Wang, ProfMD28Weihong Yu, ProfMD29Donald Tiang Hwee Tan, ProfFRCS (Ed)30Leopold Schmetterer, ProfPhD31Ching-Yu Cheng, ProfMD32Youxin Chen, ProfMD33Chee Wai Wong, MBBS34Gemmy Chui Ming Cheung, ProfFRCOphth35Seang-Mei Saw, ProfPhD36Tien Yin Wong, ProfMD37Yong Liu, PhD38Daniel Shu Wei Ting, MD39Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, SingaporeInstitute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), SingaporeInstitute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), SingaporeInstitute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), SingaporeInstitute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), SingaporeInstitute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), SingaporeInstitute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), SingaporeInstitute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), SingaporeInstitute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), SingaporeSingapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, SingaporeSingapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, SingaporeSingapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, SingaporeSingapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, SingaporeSingapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, SingaporeSaw Swee Hock School of Public Health, National University of Singapore, Singapore; Essilor International, SingaporeSingapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, Singapore; Department of Ophthalmology, Columbia University, New York, NY, USA; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, SingaporeUfa Eye Research Institute, Ufa, Bashkortostan, RussiaUfa Eye Research Institute, Ufa, Bashkortostan, RussiaSuraj Eye Institute, Nagpur, IndiaDepartment of Ophthalmology, Medical Faculty Mannheim, Heidelberg University, GermanyDepartment of Ophthalmology, Chang Gung Memorial Hospital, Taoyuan, TaiwanDepartment of Ophthalmology, Chang Gung Memorial Hospital, Taoyuan, TaiwanDepartment of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, JapanSingapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, SingaporeSingapore Eye Research Institute, Singapore, Singapore National Eye Centre, SingaporeInstitute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), SingaporeZhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, ChinaBeijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, ChinaBeijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, ChinaPeking Union Medical College Hospital, Beijing, ChinaSingapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, SingaporeSingapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, Singapore; Department of Clinical Pharmacology and Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Austria; Institute of Molecular and Clinical Ophthalmology, Basel, SwitzerlandSingapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, SingaporePeking Union Medical College Hospital, Beijing, ChinaSingapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, SingaporeSingapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, SingaporeSingapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, Singapore; Saw Swee Hock School of Public Health, National University of Singapore, SingaporeSingapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, SingaporeInstitute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), SingaporeSingapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, Singapore; Correspondence to: Dr Daniel Shu Wei Ting, Singapore National Eye Centre, 168751 SingaporeSummary: Background: By 2050, almost 5 billion people globally are projected to have myopia, of whom 20% are likely to have high myopia with clinically significant risk of sight-threatening complications such as myopic macular degeneration. These are diagnoses that typically require specialist assessment or measurement with multiple unconnected pieces of equipment. Artificial intelligence (AI) approaches might be effective for risk stratification and to identify individuals at highest risk of visual loss. However, unresolved challenges for AI medical studies remain, including paucity of transparency, auditability, and traceability. Methods: In this retrospective multicohort study, we developed and tested retinal photograph-based deep learning algorithms for detection of myopic macular degeneration and high myopia, using a total of 226 686 retinal images. First we trained and internally validated the algorithms on datasets from Singapore, and then externally tested them on datasets from China, Taiwan, India, Russia, and the UK. We also compared the performance of the deep learning algorithms against six human experts in the grading of a randomly selected dataset of 400 images from the external datasets. As proof of concept, we used a blockchain-based AI platform to demonstrate the real-world application of secure data transfer, model transfer, and model testing across three sites in Singapore and China. Findings: The deep learning algorithms showed robust diagnostic performance with areas under the receiver operating characteristic curves [AUC] of 0·969 (95% CI 0·959–0·977) or higher for myopic macular degeneration and 0·913 (0·906–0·920) or higher for high myopia across the external testing datasets with available data. In the randomly selected dataset, the deep learning algorithms outperformed all six expert graders in detection of each condition (AUC of 0·978 [0·957–0·994] for myopic macular degeneration and 0·973 [0·941–0·995] for high myopia). We also successfully used blockchain technology for data transfer, model transfer, and model testing between sites and across two countries. Interpretation: Deep learning algorithms can be effective tools for risk stratification and screening of myopic macular degeneration and high myopia among the large global population with myopia. The blockchain platform developed here could potentially serve as a trusted platform for performance testing of future AI models in medicine. Funding: None.http://www.sciencedirect.com/science/article/pii/S2589750021000558 |
| spellingShingle | Tien-En Tan, FRCOphth Ayesha Anees, MSc Cheng Chen, MSc Shaohua Li, PhD Xinxing Xu, PhD Zengxiang Li, PhD Zhe Xiao, PhD Yechao Yang, BSc Xiaofeng Lei, MSc Marcus Ang, FRCS (Ed) Audrey Chia, FRANZCO Shu Yen Lee, FRCS (Ed) Edmund Yick Mun Wong, FRCS (Ed) Ian Yew San Yeo, ProfFRCS (Ed) Yee Ling Wong, PhD Quan V Hoang, MD Ya Xing Wang, MD Mukharram M Bikbov, MD Vinay Nangia, MD Jost B Jonas, ProfMD Yen-Po Chen, MD Wei-Chi Wu, ProfMD Kyoko Ohno-Matsui, ProfMD Tyler Hyungtaek Rim, MD Yih-Chung Tham, PhD Rick Siow Mong Goh, PhD Haotian Lin, ProfMD Hanruo Liu, MD Ningli Wang, ProfMD Weihong Yu, ProfMD Donald Tiang Hwee Tan, ProfFRCS (Ed) Leopold Schmetterer, ProfPhD Ching-Yu Cheng, ProfMD Youxin Chen, ProfMD Chee Wai Wong, MBBS Gemmy Chui Ming Cheung, ProfFRCOphth Seang-Mei Saw, ProfPhD Tien Yin Wong, ProfMD Yong Liu, PhD Daniel Shu Wei Ting, MD Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort study The Lancet: Digital Health |
| title | Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort study |
| title_full | Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort study |
| title_fullStr | Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort study |
| title_full_unstemmed | Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort study |
| title_short | Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort study |
| title_sort | retinal photograph based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research a retrospective multicohort study |
| url | http://www.sciencedirect.com/science/article/pii/S2589750021000558 |
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