Published on in Vol 9, No 12 (2021): December
![Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review](https://asset.jmir.pub/assets/f33810a5cf81cb4296d1b67f9c29229b.png 480w,https://asset.jmir.pub/assets/f33810a5cf81cb4296d1b67f9c29229b.png 960w,https://asset.jmir.pub/assets/f33810a5cf81cb4296d1b67f9c29229b.png 1920w,https://asset.jmir.pub/assets/f33810a5cf81cb4296d1b67f9c29229b.png 2500w)
1 UCL Institute of Ophthalmology, University College London, London, United Kingdom
2 Moorfields Eye Hospital, London, United Kingdom
3 Centre for Optometry & Vision Science, Biomedical Sciences Research Institute, Ulster University, Coleraine, United Kingdom
4 Laboratory of Visual Physiology, Division of Vision Research, National Institute of Sensory Organs, National Hospital Organization Tokyo Medical Center, Tokyo, Japan
5 Department of Ophthalmology, Keio University School of Medicine, Tokyo, Japan
6 Section of Ophthalmology, School of Life Course Sciences, King’s College London, London, United Kingdom
7 Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
8 Department of Paediatrics and Inherited Metabolic Disorders, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
9 Department of Ophthalmology, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
*these authors contributed equally