Premium
Automation of dry eye disease quantitative assessment: A review
Author(s) -
Brahim Ikram,
Lamard Mathieu,
Benyoussef AnasAlexis,
Quellec Gwenolé
Publication year - 2022
Publication title -
clinical and experimental ophthalmology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.3
H-Index - 74
eISSN - 1442-9071
pISSN - 1442-6404
DOI - 10.1111/ceo.14119
Subject(s) - medicine , automation , disease , medical physics , artificial intelligence , optometry , computer science , pathology , engineering , mechanical engineering
Dry eye disease (DED) is a common eye condition worldwide and a primary reason for visits to the ophthalmologist. DED diagnosis is performed through a combination of tests, some of which are unfortunately invasive, non‐reproducible and lack accuracy. The following review describes methods that diagnose and measure the extent of eye dryness, enabling clinicians to quantify its severity. Our aim with this paper is to review classical methods as well as those that incorporate automation. For only four ways of quantifying DED, we take a deeper look into what main elements can benefit from automation and the different ways studies have incorporated it. Like numerous medical fields, Artificial Intelligence (AI) appears to be the path towards quality DED diagnosis. This review categorises diagnostic methods into the following: classical, semi‐automated and promising AI‐based automated methods.