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Accuracy of machine learning classifiers using bilateral data from a Scheimpflug camera for identifying eyes with
Illés Kovács1, Kata Miháltz1, Kinga Kránitz1
1From the Department of Ophthalmology (Kovács, Kránitz, Juhász, Takács, Dienes, Gergely, Nagy), Semmelweis University, Budapest, Hungary; the Karl Landsteiner Institute of Process Optimization and QM in Cataract Surgery (Mihált), Vienna, Austria.
Purpose:
To describe the topographic and tomographic characteristics of normal fellow eyes of unilateral keratoconus cases and to evaluate the accuracy of machine learning classifiers in discriminating healthy corneas from the normal fellow corneas.
Setting:
Department of Ophthalmology, Semmelweis University, Budapest, Hungary.
Design:
Retrospective case-control study.
Methods:
Patients with bilateral keratoconus (keratoconus group), clinically and according to the keratoconus indices of the Pentacam HR Scheimpflug camera; normal fellow eyes of patients with unilateral keratoconus (fellow-eye group); and eyes of refractive surgery candidates (control group) were compared. Tomographic data, topographic data, and keratoconus indices were measured in both eyes using the Scheimpflug camera. Receiver operating characteristic (ROC) analysis was used to assess the performance of automated classifiers trained on bilateral data as well as individual parameters to discriminate fellow eyes of patients with keratoconus from control eyes.
Results:
Keratometry, elevation, and keratoconus indices values were significantly higher and pachymetry values were significantly lower in keratoconus eyes than in fellow eyes of unilateral keratoconus cases (P < .001). These fellow eyes had significantly higher keratometry, elevation, and keratoconus index values and significantly lower pachymetry values than control eyes (P < .001). Automated classifiers trained on bilateral data of index of height decentration had higher accuracy than the unilateral single parameter in discriminating fellow eyes of patients with keratoconus from control eyes (area under ROC 0.96 versus 0.88).
Conclusion:
Automatic classifiers trained on bilateral data were better than single parameters in discriminating fellow eyes of patients with unilateral keratoconus with preclinical signs of keratoconus from normal eyes.
Financial Disclosure:
No author has a financial or proprietary interest in any material or method mentioned.
