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Artificial Intelligence in Dry Eye Disease: Benefits, Challenges and Future Directions

Carolyn YTW · Open Access Journal of Ophthalmology · 2024

Current Diagnostic Challenges in Dry Eye Disease Dry eye disease (DED) is a challenging condition to pin down, given the various probable aetiologies, signs, and symptoms. DED is characterised by its loss of tear-film homeostasis, ocular surface inflammation, hyperosmolarity, eye discomfort, and visual abnormalities [1]. However, the signs of DED are occasionally inconsistent with the symptoms stated by patients [1]. There is presently no one clinical test that can uniformly pinpoint DED [1]. DED is diagnosed using a variety of subjective tests and symptom questions, including tear breakup time (TBUT), Schirmer's test (ST), fluorescein and lissamine green staining of the corneal (CSS1) and conjunctival surface (CSS2), and the ocular surface disease index (OSDI) [1]. Furthermore, the differentiation of different tear film breakup patterns is thought to be at the centre of a tear film-oriented diagnosis, which helps elucidate the pathophysiology of DED (i.e., identify the insufficient component of the tear film or of the corneal surface epithelium responsible for TFBU), sub classify DED, and select the optimal topical therapy (decide on the most appropriate treatment) [2]. Additional

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