Tien-En Tan1, Ayesha Anees2, Cheng Chen2, Shaohua Li2, Xinxing Xu2, Zengxiang Li2, Zhe Xiao2, Yechao Yang2, Xiaofeng Lei2, Marcus Ang1, Audrey Chia1, Shu Yen Lee1, Edmund Yick Mun Wong1, Ian Yew San Yeo1, Yee Ling Wong3, Quan V Hoang4, Ya Xing Wang5, Mukharram M Bikbov5, Vinay Nangia6, Jost B Jonas7, Yen-Po Chen8, Wei-Chi Wu8, Kyoko Ohno-Matsui9, Tyler Hyungtaek Rim1, Yih-Chung Tham10, Rick Siow Mong Goh2, Haotian Lin11, Hanruo Liu12, Ningli Wang12, Weihong Yu13, Donald Tiang Hwee Tan1, Leopold Schmetterer14, Ching-Yu Cheng1, Youxin Chen13, Chee Wai Wong1, Gemmy Chui Ming Cheung1, Seang-Mei Saw15, Tien Yin Wong1, Yong Liu2, Daniel Shu Wei Ting16. 1. Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, Singapore. 2. Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore. 3. Saw Swee Hock School of Public Health, National University of Singapore, Singapore; Essilor International, Singapore. 4. Singapore 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, Singapore. 5. Ufa Eye Research Institute, Ufa, Bashkortostan, Russia. 6. Suraj Eye Institute, Nagpur, India. 7. Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University, Germany. 8. Department of Ophthalmology, Chang Gung Memorial Hospital, Taoyuan, Taiwan. 9. Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan. 10. Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore. 11. Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China. 12. Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China. 13. Peking Union Medical College Hospital, Beijing, China. 14. Singapore 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, Switzerland. 15. Singapore 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, Singapore. 16. Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore; Duke-National University of Singapore Medical School, Singapore. Electronic address: daniel.ting.s.w@singhealth.com.sg.
Abstract
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.
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.
Authors: James O'Donovan; Ken Kahn; MacKenzie MacRae; Allan Saul Namanda; Rebecca Hamala; Ken Kabali; Anne Geniets; Alice Lakati; Simon M Mbae; Niall Winters Journal: Hum Resour Health Date: 2022-03-16
Authors: Zhenquan Wu; Wenjia Cai; Hai Xie; Shida Chen; Yanbing Wang; Baiying Lei; Yingfeng Zheng; Lin Lu Journal: Front Med (Lausanne) Date: 2022-03-03