Forecasting Progressive Trends in Keratoconus by Means of a Time Delay Neural Network.

ABSTRACT: Early and accurate detection of keratoconus progression is particularly important for the prudent, cost-effective use of corneal cross-linking and judicious timing of clinical follow-up visits. The aim of this study was to verify whether a progression could be predicted based on two prior tomography measurements and to verify the accuracy of the system when labelling the eye as stable or suspect progressive. Data from 743 patients measured by Pentacam (Oculus, Wetzlar, Germany) were available, and they were filtered and preprocessed to data quality needs. The time delay neural network received six features as input, measured in two consecutive examinations, predicted the future values, and determined the classification (stable or suspect progressive) based on the significance of the change

SUBMITTER: Jimenez-Garcia M 

PROVIDER: S-EPMC8347247 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature