Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort study
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...
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| Vydáno v: | The Lancet Digital Health Ročník 3; číslo 5; s. e317 - e329 |
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| Hlavní autoři: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
England
Elsevier Ltd
01.05.2021
Elsevier BV Elsevier |
| Témata: | |
| ISSN: | 2589-7500, 2589-7500 |
| On-line přístup: | Získat plný text |
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| Abstract | 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.
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.
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.
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.
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| AbstractList | 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.
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.
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.
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.
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. 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.BACKGROUNDBy 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.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.METHODSIn 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.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.FINDINGSThe 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.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.INTERPRETATIONDeep 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.None.FUNDINGNone. SummaryBackgroundBy 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. MethodsIn 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. FindingsThe 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. InterpretationDeep 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. FundingNone. |
| Author | Tan, Tien-En Yu, Weihong Cheung, Gemmy Chui Ming Ohno-Matsui, Kyoko Lei, Xiaofeng Wang, Ya Xing Wang, Ningli Xiao, Zhe Chen, Yen-Po Lin, Haotian Wong, Chee Wai Ting, Daniel Shu Wei Yang, Yechao Rim, Tyler Hyungtaek Liu, Hanruo Nangia, Vinay Anees, Ayesha Li, Shaohua Lee, Shu Yen Chen, Youxin Wong, Yee Ling Chen, Cheng Xu, Xinxing Schmetterer, Leopold Chia, Audrey Hoang, Quan V Ang, Marcus Bikbov, Mukharram M Jonas, Jost B Wu, Wei-Chi Cheng, Ching-Yu Wong, Edmund Yick Mun Goh, Rick Siow Mong Yeo, Ian Yew San Li, Zengxiang Tan, Donald Tiang Hwee Tham, Yih-Chung Saw, Seang-Mei Liu, Yong Wong, Tien Yin |
| Author_xml | – sequence: 1 givenname: Tien-En surname: Tan fullname: Tan, Tien-En organization: Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore – sequence: 2 givenname: Ayesha surname: Anees fullname: Anees, Ayesha organization: Institute of High Performance Computing, Agency for Science, Technology and Research (ASTAR), Singapore – sequence: 3 givenname: Cheng surname: Chen fullname: Chen, Cheng organization: Institute of High Performance Computing, Agency for Science, Technology and Research (ASTAR), Singapore – sequence: 4 givenname: Shaohua surname: Li fullname: Li, Shaohua organization: Institute of High Performance Computing, Agency for Science, Technology and Research (ASTAR), Singapore – sequence: 5 givenname: Xinxing surname: Xu fullname: Xu, Xinxing organization: Institute of High Performance Computing, Agency for Science, Technology and Research (ASTAR), Singapore – sequence: 6 givenname: Zengxiang surname: Li fullname: Li, Zengxiang organization: Institute of High Performance Computing, Agency for Science, Technology and Research (ASTAR), Singapore – sequence: 7 givenname: Zhe surname: Xiao fullname: Xiao, Zhe organization: Institute of High Performance Computing, Agency for Science, Technology and Research (ASTAR), Singapore – sequence: 8 givenname: Yechao surname: Yang fullname: Yang, Yechao organization: Institute of High Performance Computing, Agency for Science, Technology and Research (ASTAR), Singapore – sequence: 9 givenname: Xiaofeng surname: Lei fullname: Lei, Xiaofeng organization: Institute of High Performance Computing, Agency for Science, Technology and Research (ASTAR), Singapore – sequence: 10 givenname: Marcus surname: Ang fullname: Ang, Marcus organization: Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore – sequence: 11 givenname: Audrey surname: Chia fullname: Chia, Audrey organization: Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore – sequence: 12 givenname: Shu Yen surname: Lee fullname: Lee, Shu Yen organization: Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore – sequence: 13 givenname: Edmund Yick Mun surname: Wong fullname: Wong, Edmund Yick Mun organization: Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore – sequence: 14 givenname: Ian Yew San surname: Yeo fullname: Yeo, Ian Yew San organization: Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore – sequence: 15 givenname: Yee Ling surname: Wong fullname: Wong, Yee Ling organization: Saw Swee Hock School of Public Health, National University of Singapore, Singapore – sequence: 16 givenname: Quan V surname: Hoang fullname: Hoang, Quan V organization: Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore – sequence: 17 givenname: Ya Xing surname: Wang fullname: Wang, Ya Xing organization: Ufa Eye Research Institute, Ufa, Bashkortostan, Russia – sequence: 18 givenname: Mukharram M surname: Bikbov fullname: Bikbov, Mukharram M organization: Ufa Eye Research Institute, Ufa, Bashkortostan, Russia – sequence: 19 givenname: Vinay surname: Nangia fullname: Nangia, Vinay organization: Suraj Eye Institute, Nagpur, India – sequence: 20 givenname: Jost B surname: Jonas fullname: Jonas, Jost B organization: Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University, Germany – sequence: 21 givenname: Yen-Po surname: Chen fullname: Chen, Yen-Po organization: Department of Ophthalmology, Chang Gung Memorial Hospital, Taoyuan, Taiwan – sequence: 22 givenname: Wei-Chi surname: Wu fullname: Wu, Wei-Chi organization: Department of Ophthalmology, Chang Gung Memorial Hospital, Taoyuan, Taiwan – sequence: 23 givenname: Kyoko surname: Ohno-Matsui fullname: Ohno-Matsui, Kyoko organization: Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan – sequence: 24 givenname: Tyler Hyungtaek surname: Rim fullname: Rim, Tyler Hyungtaek organization: Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore – sequence: 25 givenname: Yih-Chung surname: Tham fullname: Tham, Yih-Chung organization: Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore – sequence: 26 givenname: Rick Siow Mong surname: Goh fullname: Goh, Rick Siow Mong organization: Institute of High Performance Computing, Agency for Science, Technology and Research (ASTAR), Singapore – sequence: 27 givenname: Haotian surname: Lin fullname: Lin, Haotian organization: Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China – sequence: 28 givenname: Hanruo surname: Liu fullname: Liu, Hanruo organization: Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China – sequence: 29 givenname: Ningli surname: Wang fullname: Wang, Ningli organization: Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China – sequence: 30 givenname: Weihong surname: Yu fullname: Yu, Weihong organization: Peking Union Medical College Hospital, Beijing, China – sequence: 31 givenname: Donald Tiang Hwee surname: Tan fullname: Tan, Donald Tiang Hwee organization: Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore – sequence: 32 givenname: Leopold surname: Schmetterer fullname: Schmetterer, Leopold organization: Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore – sequence: 33 givenname: Ching-Yu surname: Cheng fullname: Cheng, Ching-Yu organization: Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore – sequence: 34 givenname: Youxin surname: Chen fullname: Chen, Youxin organization: Peking Union Medical College Hospital, Beijing, China – sequence: 35 givenname: Chee Wai surname: Wong fullname: Wong, Chee Wai organization: Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore – sequence: 36 givenname: Gemmy Chui Ming surname: Cheung fullname: Cheung, Gemmy Chui Ming organization: Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore – sequence: 37 givenname: Seang-Mei surname: Saw fullname: Saw, Seang-Mei organization: Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore – sequence: 38 givenname: Tien Yin surname: Wong fullname: Wong, Tien Yin organization: Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore – sequence: 39 givenname: Yong surname: Liu fullname: Liu, Yong organization: Institute of High Performance Computing, Agency for Science, Technology and Research (ASTAR), Singapore – sequence: 40 givenname: Daniel Shu Wei surname: Ting fullname: Ting, Daniel Shu Wei email: daniel.ting.s.w@singhealth.com.sg organization: Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore |
| BackLink | https://cir.nii.ac.jp/crid/1872835442338597632$$DView record in CiNii https://www.ncbi.nlm.nih.gov/pubmed/33890579$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Contributor | OPHTHALMOLOGY DEAN'S OFFICE (DUKE-NUS MEDICAL SCHOOL) DUKE-NUS MEDICAL SCHOOL |
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| Title | Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort study |
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