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 , GB

2 Moorfields Eye Hospital , London , GB

3 Centre for Optometry & Vision Science, Biomedical Sciences Research Institute, Ulster University, Coleraine , GB

4 Department of Ophthalmology, Keio University School of Medicine , Tokyo , JP

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

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

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

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

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

*these authors contributed equally

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