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Corneal deformation amplitude analysis for keratoconus detection through compensation for intraocular pressure and integration with horizontal thickness profile

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BACKGROUND The Corvis ST provides measurements of intraocular pressure (IOP) and a biomechanically-corrected IOP (bIOP). IOP influences corneal deflection amplitude (DA), which may affect the diagnosis of keratoconus. Compensating for… Click to show full abstract

BACKGROUND The Corvis ST provides measurements of intraocular pressure (IOP) and a biomechanically-corrected IOP (bIOP). IOP influences corneal deflection amplitude (DA), which may affect the diagnosis of keratoconus. Compensating for IOP in DA values may improve the detection of keratoconus. METHODS 195 healthy eyes and 136 eyes with keratoconus were included for developing different approaches to distinguish normal and keratoconic corneas using attribute selection and discriminant function. The IOP compensation is proposed by dividing the DA by the IOP values. The first approaches include DA compensated for either IOP or bIOP and other parameters from the deformation corneal response (DCR). Another approach integrated the horizontal corneal thickness profile (HCTP). The best classifiers developed were applied in a validation database of 156 healthy eyes and 87 eyes with keratoconus. Results were compared with the current Corvis Biomechanical Index (CBI). RESULTS The best biomechanical approach used the DA values compensated by IOP (Approach 2) using a linear discriminant function and reached AUC 0.954, with a sensitivity of 88.2% and a specificity of 97.4%. When thickness horizontal profile data was integrated (Approach 4), the best function was the diagquadratic, resulting in an AUC of 0.960, with a sensitivity of 89.7% and a specificity of 96.4%. There was no significant difference in the results between approaches 2 and 4 with the CBI in the training and validation databases. CONCLUSIONS By compensating for the IOP, and with the horizontal thickness profile included or excluded, it was possible to generate a classifier based only on biomechanical information with a similar result to the CBI.

Keywords: keratoconus; thickness; horizontal thickness; thickness profile; intraocular pressure

Journal Title: Computers in biology and medicine
Year Published: 2019

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