Use of machine learning to achieve keratoconus detection skills of a corneal expert.
Artificial intelligence
Detection
Dual Scheimpflug/Placido
Galilei
Keratoconus
Machine learning
Random forest
Journal
International ophthalmology
ISSN: 1573-2630
Titre abrégé: Int Ophthalmol
Pays: Netherlands
ID NLM: 7904294
Informations de publication
Date de publication:
Dec 2022
Dec 2022
Historique:
received:
28
11
2021
accepted:
13
06
2022
pubmed:
12
8
2022
medline:
2
11
2022
entrez:
11
8
2022
Statut:
ppublish
Résumé
To construct an automatic machine-learning derived algorithm discriminating between normal corneas and suspect irregular or keratoconic corneas. A total of 8526 corneal tomography images of 4904 eyes obtained between November 2010 and July 2017 using a combined Scheimpflug/Placido tomographer were retrospectively evaluated. Each image was evaluated for acquisition quality and was labeled as normal, suspect irregular or keratoconic by a cornea specialist. Two algorithms were built. The first was based on 94 instrument-derived output parameters, and the second integrated keratoconus prediction indices of the device with the 94 instrument-derived output parameters. Both models were compared with the tomographer's keratoconus detection algorithms. Out of the 8526 images evaluated, 7104 images of 3787 eyes had sufficient acquisition quality. Of those, 5904 examinations were randomly chosen for construction of the models using the random forest algorithm. The models were then validated using the remaining 1200 examinations. Both RF algorithms had a larger AUC compared with any of the tomographer's KC detection algorithms (p < 10 Using the RF machine-learning algorithm, accuracy of discrimination between normal, suspect irregular and keratoconic corneas approximates that of an experienced corneal expert. Applying machine learning to corneal tomography can facilitate keratoconus screening in large populations as well as off-site screening of refractive surgery candidates.
Identifiants
pubmed: 35953576
doi: 10.1007/s10792-022-02404-4
pii: 10.1007/s10792-022-02404-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3837-3847Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature B.V.
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