Published on in Vol 8, No 5 (2020): May
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/16225, first published
.
Deep Learning–Based Prediction of Refractive Error Using Photorefraction Images Captured by a Smartphone: Model Development and Validation Study
Deep Learning–Based Prediction of Refractive Error Using Photorefraction Images Captured by a Smartphone: Model Development and Validation Study
Journals
- Oke I, VanderVeen D. Machine Learning Applications in Pediatric Ophthalmology. Seminars in Ophthalmology 2021;36(4):210 View
- Dong L, Hu X, Yan Y, Zhang Q, Zhou N, Shao L, Wang Y, Xu J, Lan Y, Li Y, Xiong J, Liu C, Ge Z, Jonas J, Wei W. Deep Learning-Based Estimation of Axial Length and Subfoveal Choroidal Thickness From Color Fundus Photographs. Frontiers in Cell and Developmental Biology 2021;9 View
- Shi Z, Wang T, Huang Z, Xie F, Song G. A method for the automatic detection of myopia in Optos fundus images based on deep learning. International Journal for Numerical Methods in Biomedical Engineering 2021;37(6) View
- Wu Z, Lin Z, Li L, Pan H, Chen G, Fu Y, Qiu Q. Deep Learning for Classification of Pediatric Otitis Media. The Laryngoscope 2021;131(7) View
- Campbell J, Mathenge C, Cherwek H, Balaskas K, Pasquale L, Keane P, Chiang M. Artificial Intelligence to Reduce Ocular Health Disparities: Moving From Concept to Implementation. Translational Vision Science & Technology 2021;10(3):19 View
- Han X, Liu C, Chen Y, He M. Myopia prediction: a systematic review. Eye 2022;36(5):921 View
- Pawar N, Maheshwari D, Meenakshi R. COVID-19 myopia, myopia of pandemic: Are we heading towards unpredictable high myopic era?. Indian Journal of Ophthalmology 2022;70(8):3158 View
- Espinosa J, Pérez J, Villanueva A. Prediction of Subjective Refraction From Anterior Corneal Surface, Eye Lengths, and Age Using Machine Learning Algorithms. Translational Vision Science & Technology 2022;11(4):8 View
- Xu D, Ding S, Zheng T, Zhu X, Gu Z, Ye B, Fu W. Deep learning for predicting refractive error from multiple photorefraction images. BioMedical Engineering OnLine 2022;21(1) View
- Zou H, Shi S, Yang X, Ma J, Fan Q, Chen X, Wang Y, Zhang M, Song J, Jiang Y, Li L, He X, Jhanji V, Wang S, Song M, Wang Y. Identification of ocular refraction based on deep learning algorithm as a novel retinoscopy method. BioMedical Engineering OnLine 2022;21(1) View
- Xu Z, Xu J, Shi C, Xu W, Jin X, Han W, Jin K, Grzybowski A, Yao K. Artificial Intelligence for Anterior Segment Diseases: A Review of Potential Developments and Clinical Applications. Ophthalmology and Therapy 2023;12(3):1439 View
- Zou H, Shi S, Yang X, Ma J, Chen X, Wang Y, Zhang M, Song J, Jiang Y, Li L, He X, Wang S, Song M, Wang Y. Development and Validation of Novel Digital Retinoscopy to Analyze Total Refraction of the Eye. SSRN Electronic Journal 2022 View
- Kumar V, Paul K. Fundus Imaging-Based Healthcare: Present and Future. ACM Transactions on Computing for Healthcare 2023;4(3):1 View
- Linde G, Chalakkal R, Zhou L, Huang J, O’Keeffe B, Shah D, Davidson S, Hong S. Automatic Refractive Error Estimation Using Deep Learning-Based Analysis of Red Reflex Images. Diagnostics 2023;13(17):2810 View
- Yew S, Chen Y, Goh J, Chen D, Chun Jin Tan M, Cheng C, Teck Chang Koh V, Tham Y. Ocular image-based deep learning for predicting refractive error: A systematic review. Advances in Ophthalmology Practice and Research 2024;4(3):164 View
- Srivastava O, Tennant M, Grewal P, Rubin U, Seamone M. Artificial intelligence and machine learning in ophthalmology: A review. Indian Journal of Ophthalmology 2023;71(1):11 View