Published on in Vol 9, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27363, first published .
Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review

Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review

Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review

Howard Maile   1 * , MSc ;   Ji-Peng Olivia Li   2 * , MA, FRCOphth ;   Daniel Gore   2 , MD, FRCOphth ;   Marcello Leucci   2 , BA ;   Padraig Mulholland   1, 2, 3 , PhD ;   Scott Hau   2 , MSc ;   Anita Szabo   1 , MSci ;   Ismail Moghul   2 , PhD ;   Konstantinos Balaskas   2 , BS, MD ;   Kaoru Fujinami   1, 2, 4, 5 , MD, PhD ;   Pirro Hysi   6, 7 , MD, PhD ;   Alice Davidson   1 , PhD ;   Petra Liskova   8, 9 , MD, PhD ;   Alison Hardcastle   1 , PhD ;   Stephen Tuft   1, 2 , MD, FRCOphth ;   Nikolas Pontikos   1, 2 , PhD

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 Department of Ophthalmology, Keio University School of Medicine, Tokyo, Japan

5 Laboratory of Visual Physiology, Division of Vision Research, National Institute of Sensory Organs, National Hospital Organization Tokyo Medical Center, Tokyo, Japan

6 Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom

7 Section of Ophthalmology, School of Life Course Sciences, King’s College London, London, United Kingdom

8 Department of Ophthalmology, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic

9 Department of Paediatrics and Inherited Metabolic Disorders, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic

*these authors contributed equally

Corresponding Author: