Eye (London, England) · 2022
A clinical decision support system for diagnosing and determining severity of dry eye disease.
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Ebrahimi Farzad, Ayatollahi Haleh, Aghaei Hossein
Аннотация
INTRODUCTION: Dry 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.
METHODS: This 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).
RESULTS: The 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.
CONCLUSION: It 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.
METHODS: This 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).
RESULTS: The 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.
CONCLUSION: It 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.