Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review.

Autor:	Maile H; UCL Institute of Ophthalmology, University College London, London, United Kingdom., Li JO; Moorfields Eye Hospital, London, United Kingdom., Gore D; Moorfields Eye Hospital, London, United Kingdom., Leucci M; Moorfields Eye Hospital, London, United Kingdom., Mulholland P; UCL Institute of Ophthalmology, University College London, London, United Kingdom.; Moorfields Eye Hospital, London, United Kingdom.; Centre for Optometry & Vision Science, Biomedical Sciences Research Institute, Ulster University, Coleraine, United Kingdom., Hau S; Moorfields Eye Hospital, London, United Kingdom., Szabo A; UCL Institute of Ophthalmology, University College London, London, United Kingdom., Moghul I; Moorfields Eye Hospital, London, United Kingdom., Balaskas K; Moorfields Eye Hospital, London, United Kingdom., Fujinami K; UCL Institute of Ophthalmology, University College London, London, United Kingdom.; Moorfields Eye Hospital, London, United Kingdom.; Laboratory of Visual Physiology, Division of Vision Research, National Institute of Sensory Organs, National Hospital Organization Tokyo Medical Center, Tokyo, Japan.; Department of Ophthalmology, Keio University School of Medicine, Tokyo, Japan., Hysi P; Section of Ophthalmology, School of Life Course Sciences, King's College London, London, United Kingdom.; Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom., Davidson A; UCL Institute of Ophthalmology, University College London, London, United Kingdom., Liskova P; Department of Paediatrics and Inherited Metabolic Disorders, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic.; Department of Ophthalmology, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic., Hardcastle A; UCL Institute of Ophthalmology, University College London, London, United Kingdom., Tuft S; UCL Institute of Ophthalmology, University College London, London, United Kingdom.; Moorfields Eye Hospital, London, United Kingdom., Pontikos N; UCL Institute of Ophthalmology, University College London, London, United Kingdom.; Moorfields Eye Hospital, London, United Kingdom.
Jazyk:	angličtina
Zdroj:	JMIR medical informatics [JMIR Med Inform] 2021 Dec 13; Vol. 9 (12), pp. e27363. Date of Electronic Publication: 2021 Dec 13.
DOI:	10.2196/27363
Abstrakt:	Background: Keratoconus is a disorder characterized by progressive thinning and distortion of the cornea. If detected at an early stage, corneal collagen cross-linking can prevent disease progression and further visual loss. Although advanced forms are easily detected, reliable identification of subclinical disease can be problematic. Several different machine learning algorithms have been used to improve the detection of subclinical keratoconus based on the analysis of multiple types of clinical measures, such as corneal imaging, aberrometry, or biomechanical measurements. Objective: The aim of this study is to survey and critically evaluate the literature on the algorithmic detection of subclinical keratoconus and equivalent definitions. Methods: For this systematic review, we performed a structured search of the following databases: MEDLINE, Embase, and Web of Science and Cochrane Library from January 1, 2010, to October 31, 2020. We included all full-text studies that have used algorithms for the detection of subclinical keratoconus and excluded studies that did not perform validation. This systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations. Results: We compared the measured parameters and the design of the machine learning algorithms reported in 26 papers that met the inclusion criteria. All salient information required for detailed comparison, including diagnostic criteria, demographic data, sample size, acquisition system, validation details, parameter inputs, machine learning algorithm, and key results are reported in this study. Conclusions: Machine learning has the potential to improve the detection of subclinical keratoconus or early keratoconus in routine ophthalmic practice. Currently, there is no consensus regarding the corneal parameters that should be included for assessment and the optimal design for the machine learning algorithm. We have identified avenues for further research to improve early detection and stratification of patients for early treatment to prevent disease progression. (©Howard Maile, Ji-Peng Olivia Li, Daniel Gore, Marcello Leucci, Padraig Mulholland, Scott Hau, Anita Szabo, Ismail Moghul, Konstantinos Balaskas, Kaoru Fujinami, Pirro Hysi, Alice Davidson, Petra Liskova, Alison Hardcastle, Stephen Tuft, Nikolas Pontikos. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 13.12.2021.)
Databáze:	MEDLINE
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