13 Dec Deep Learning System Can Screen For Diabetic Retinopathy, Glaucoma and Macular Degeneration
MedicalResearch.com Interview with:
Dr. Tien Yin Wong MD PhD
Singapore Eye Research Institute, Singapore National Eye Center,
Duke-NUS Medical School, National University of Singapore
MedicalResearch.com: What is the background for this study? What are the main findings?
Response: Currently, annual screening for diabetic retinopathy (DR) is a universally accepted practice and recommended by American Diabetes Association and the International Council of Ophthalmology (ICO) to prevent vision loss. However, implementation of diabetic retinopathy screening programs across the world require human assessors (ophthalmologists, optometrists or professional technicians trained to read retinal photographs). Such screening programs are thus challenged by issues related to a need for significant human resources and long-term financial sustainability.
To address these challenges, we developed an AI-based software using a deep learning, a new machine learning technology. This deep learning system (DLS) utilizes representation-learning methods to process large data and extract meaningful patterns. In our study, we developed and validated this using about 500,000 retinal images in a “real world screening program” and 10 external datasets from global populations. The results suggest excellent accuracy of the deep learning system with sensitivity of 90.5% and specificity of 91.6%, for detecting referable levels of DR and 100% sensitivity and 91.1% specificity for vision-threatening levels of DR (which require urgent referral and should not be missed). In addition, the performance of the deep learning system was also high for detecting referable glaucoma suspects and referable age-related macular degeneration (which also require referral if detected).
The deep learning system was tested in 10 external datasets comprising different ethnic groups: Caucasian whites, African-Americans, Hispanics, Chinese, Indians and Malaysians
MedicalResearch.com: What should clinicians and patients take away from your report?
Response: First, it will be easier to set up diabetic retinopathy screening programs in communities in the future which could largely be done automatically by deep learning system.
Second, it will safe cost and improve efficiency of healthcare system by allowing ophthalmologists and optometrists to concentrate on treating only diabetic retinopathy cases that require treatment.
MedicalResearch.com: What recommendations do you have for future research as a result of this study?
Response: We are now beta testing this in the national Singapore screening program side-by-side with human assessors so once the comparison is adequate, it will be implemented.
Disclosures: I am an co-inventor of the DLS algorithm.
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Daniel Shu Wei Ting, Carol Yim-Lui Cheung, Gilbert Lim, Gavin Siew Wei Tan, Nguyen D. Quang, Alfred Gan, Haslina Hamzah, Renata Garcia-Franco, Ian Yew San Yeo, Shu Yen Lee, Edmund Yick Mun Wong, Charumathi Sabanayagam, Mani Baskaran, Farah Ibrahim, Ngiap Chuan Tan, Eric A. Finkelstein, Ecosse L. Lamoureux, Ian Y. Wong, Neil M. Bressler, Sobha Sivaprasad, Rohit Varma, Jost B. Jonas, Ming Guang He, Ching-Yu Cheng, Gemmy Chui Ming Cheung, Tin Aung, Wynne Hsu, Mong Li Lee, Tien Yin Wong. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. JAMA. 2017;318(22):2211–2223. doi:10.1001/jama.2017.18152
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