AUTOMATED KERATOCONUS SCREENING WITH CORNEAL TOPOGRAPHY ANALYSIS

Purpose. Although visual inspection of corneal topography maps by trai ned experts can be powerful, this method is inherently subjective. Qua ntitative classification methods that can detect and classify abnormal topographic patterns would be useful. An automated system was develop ed to differentiate keratoconus patterns from other conditions using c omputer-assisted videokeratoscopy. Methods. This system combined a cla ssification tree with a linear discriminant function derived from disc riminant analysis of eight indices obtained from TMS-1 videokeratoscop e data. One hundred corneas with a variety of diagnoses (keratoconus, normal, keratoplasty, epikeratophakia, excimer laser photorefractive k eratectomy, radial keratotomy, contact lens-induced warpage, and other s) were used for training, and a validation set of 100 additional corn eas was used to evaluate the results. Results. In the training set, al l 22 cases of clinically diagnosed keratoconus were detected with thre e false-positive cases (sensitivity 100%, specificity 96%, and accurac y 97%). With the validation set, 25 out of 28 keratoconus cases were d etected with one false-positive case, which was a transplanted cornea (sensitivity 89%, specificity 99%, and accuracy 96%). Conclusions. Thi s system can be used as a screening procedure to distinguish clinical keratoconus from other corneal topographies. This quantitative classif ication method may also aid in refining the clinical interpretation of topographic maps.