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

Journals

  1. Oke I, VanderVeen D. Machine Learning Applications in Pediatric Ophthalmology. Seminars in Ophthalmology 2021:1 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 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 2020 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

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