ArticleEye (London, England)2023
A clinical decision support system for diagnosing and determining severity of dry eye disease.
Article in Eye (London, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Article
- Electronics-Free Visual Monitoring of Dry Eye Disease-Related Tear Biomarkers Using Smart Scleral Lenses.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Diagnostic methods for managing dry eyes.World journal of methodology · 2025Article
- Artificial intelligence virtual assistants in primary eye care practice.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2025Article
- Review
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
introductionDry eye and its related symptoms are the most common causes of referrals to the ophthalmology centers. Since people with dry eye may suffer from different levels of the disease severity, this study aimed to develop a clinical decision support system for diagnosing and determining severity of dry eye disease.
methodsThis research was carried out in two phases in 2020. In the first phase, a questionnaire was designed to identify the most important diagnostic parameters from the cornea specialists' perspectives (n = 37). In the second phase of the research, a clinical decision support system was designed and implemented by using MATLAB software. Finally, the system was evaluated using patient data which were collected in a teaching hospital (n = 50).
resultsThe diagnostic parameters for dry eye disease were filamentary keratitis, meibomian gland dysfunction, score of ocular surface disease index, Schirmer's test result, tear meniscus height, tear breakup time, and fluorescein staining score. The system output variables were the diagnosis and severity of dry eye disease at four levels for the right and left eyes, separately. The results of the evaluation study showed that the accuracy, sensitivity and specificity of the system were 96.9%, 97.5%, and 93.7%, respectively.
conclusionIt seems that the system designed in this study can help ophthalmologists to diagnose dry eye disease more accurately and quickly. However, it is recommended to conduct more evaluation studies and include more patients in the future research.
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