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

Machine Learning (1962) Clinical Information and Decision Making (1805) Ophthalmology (65) Imaging Informatics (280) mHealth in a Clinical Setting (717)

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 (31) Tweetations (1) Metrics

Journals

  1. Oke I, VanderVeen D. Machine Learning Applications in Pediatric Ophthalmology. Seminars in Ophthalmology 2021;36(4):210 View
  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
  3. 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
  4. 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
  5. 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
  6. Han X, Liu C, Chen Y, He M. Myopia prediction: a systematic review. Eye 2022;36(5):921 View
  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
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  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
  16. 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
  17. Yew S, Lei X, Chen Y, Goh J, Pushpanathan K, Xue C, Wang Y, Jonas J, Sabanayagam C, Koh V, Xu X, Liu Y, Cheng C, Tham Y. Deep Imbalanced Regression Model for Predicting Refractive Error from Retinal Photos. Ophthalmology Science 2025;5(2):100659 View
  18. Stuermer L, Braga S, Martin R, Wolffsohn J. Artificial intelligence virtual assistants in primary eye care practice. Ophthalmic and Physiological Optics 2025;45(2):437 View
  19. Zuo H, Huang B, He J, Fang L, Huang M. Machine Learning Approaches in High Myopia: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2025;27:e57644 View
  20. Wang J, Zheng T, Zhang Y, Zheng T, Fu W. Comparative Feature-Guided Regression Network with a Model-Eye Pretrained Model for Online Refractive Error Screening. Future Internet 2025;17(4):160 View
  21. Nguyen T, Ong J, Jonnakuti V, Masalkhi M, Waisberg E, Aman S, Zaman N, Sarker P, Teo Z, Ting D, Ting D, Tavakkoli A, Lee A. Artificial intelligence in the diagnosis and management of refractive errors. European Journal of Ophthalmology 2025;35(4):1456 View
  22. Vijendran S, Alok Y, Kuzhuppilly N, Bhat J, Kamath Y. Effectiveness of smartphone technology for detection of paediatric ocular diseases—a systematic review. BMC Ophthalmology 2025;25(1) View
  23. Syauqie M, Patria H, Hastono S, Siregar K, Moeloek N. Deep learning for vision screening in resource-limited settings: development of multi-branch CNN for refractive error detection based on smartphone image. Frontiers in Computer Science 2025;7 View
  24. Salmeron‐Campillo R, Martinez‐Ros G, Diaz‐Guirado J, Travel‐Alarcon C, Jaskulski M, Lopez‐Gil N. Accuracy and precision of a sphero‐cylindrical over‐refraction app for smartphones. Ophthalmic and Physiological Optics 2025 View
  25. Cheung M, Yang Z, Zhai X, Fu E, Ngai G, Leong H, Chan L, Du B, Wei R, Do C. Refractive error detection in smartphone images via convolutional neural network. International Journal of Medical Informatics 2026;205:106083 View

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
  3. Lanca C, Emamian M, Grzybowski A. Artificial Intelligence in Ophthalmology. View

Conference Proceedings

  1. Yang Z, Fu E, Ngai G, Leong H, Do C, Chan L. Proceedings of the 18th International Conference on Advances in Mobile Computing & Multimedia. Screening for refractive error with low-quality smartphone images View
  2. Suresh Kumar K, T A, KN K A, S N. 2022 6th International Conference on Electronics, Communication and Aerospace Technology. An Innovative Approach to Predict Refractive Error of a Human Eye using Machine Learning View
  3. Hao X, Zhu W, Chen X. 2024 IEEE International Symposium on Biomedical Imaging (ISBI). RER-Net: Refractive Error Regression Network for Multi-Angle Eccentric Photorefraction Pupil Images View