Automated diagnosis of keratoconus from corneal topography

Abstract

Purpose: Although visual inspection of corneal topography maps by trained experts can be powerful, this method is inherently subjective. Quantitative classification methods that can detect and classify abnormal topographic patterns would be useful. An automated system was developed to differentiate keratoconus patterns from other conditions using computer-assisted videokeratoscopy. Methods: This system combined a classification tree with a linear discriminant function derived from discriminant analysis of eight indices obtained from TMS-1 videokeratoscope data. One hundred corneas with a variety of diagnoses (keratoconus, normal, keratoplasty, epikeratophakia, excimer laser photorefractive keratectomy, radial keratotomy, contact lens-induced warpage, and others) were used for training, and a validation set of 100 additional corneas was used to evaluate the results. Results: In the training set, all 22 cases of clinically diagnosed keratoconus were detected with three false-positive cases (sensitivity 100%, specificity 96%, and accuracy 97%). With the validation set, 25 out of 28 keratoconus cases were detected with one false-positive case, which was a transplanted cornea (sensitivity 89%, specificity 99%, and accuracy 96%). Conclusions: This system can be used as a screening procedure to distinguish clinical keratoconus from other corneal topographies. This quantitative classification method may also aid in refining the clinical interpretation of topographic maps.