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Noninvasive Machine Learning Screening Model for Dacryocystitis Based on Ocular Surface Indicators
Xuefei Song1,2, Lunhao Li1,2, Fuchang Han3
1Department of Ophthalmology, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine.
Background:
Dacryocystitis is an orbital disease that can be easily misdiagnosed. The most common diagnostic tools for dacryocystitis are computed tomography, lacrimal duct angiography, and lacrimal tract irrigation. Yet, those are invasive methods, which are not conducive to extensive screening.
Objective:
To explore the significance of ocular surface indicators and demographic data in the screening of dacryocystitis.
Materials And Methods:
Data were prospectively collected from 56 patients with dacryocystitis (56 eyes) and 56 healthy individuals. Collected indicators included demographic information (gender, age), ocular surface data of tear meniscus height, objective scatter index (OSI), and clinical diagnosis. The model features were screened out by machine learning to establish a dacryocystitis screening model.
Results:
Tear meniscus height, OSI_maximum Lyapunov exponent, basic OSI, median of OSI, mean of OSI, slope coefficient of OSI linear regression, coefficient of variation in OSI, interquartile range of OSI, and other 8 parameters were used as model parameters to establish a dacryocystitis screening model with an overall detection accuracy of 85.71%.
Conclusions:
This new screening model that is based on ocular surface indicators provides a new option for noninvasive screening of dacryocystitis.
