This paper is in the following e-collection/theme issue:

Machine Learning (1501) Clinical Information and Decision Making (1434) Ophthalmology (47) Imaging Informatics (200) mHealth in a Clinical Setting (624)

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

Deep Learning–Based Prediction of Refractive Error Using Photorefraction Images Captured by a Smartphone: Model Development and Validation Study

Authors of this article:

Jaehyeong Chun1 Author Orcid Image ;   Youngjun Kim2 Author Orcid Image ;   Kyoung Yoon Shin2 Author Orcid Image ;   Sun Hyup Han2 Author Orcid Image ;   Sei Yeul Oh2 Author Orcid Image ;   Tae-Young Chung2 Author Orcid Image ;   Kyung-Ah Park2 Author Orcid Image ;   Dong Hui Lim2, 3 Author Orcid Image
Article Authors Cited by (22) Tweetations (1) Metrics

Journals

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  2. 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
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  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. Kumar V, Paul K. Fundus Imaging-Based Healthcare: Present and Future. ACM Transactions on Computing for Healthcare 2023;4(3):1 View
  14. 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
  15. 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
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Books/Policy Documents

  1. Vadhera R, Sharma M. Proceedings of the Second International Conference on Information Management and Machine Intelligence. View
  2. Ichhpujani P, Kalra G. Artificial Intelligence and Ophthalmology. View