Clinical Prediction Performance of Glaucoma Progression Using Two-Dimensional Continuous Time Hidden Markov Model with Structural and Functional Measurements

摘要

PurposePreviously we introduced a state based two-dimensional continuous time hidden Markov model (2D CT-HMM) to model the pattern of glaucoma detected changes using structural and functional information simultaneously. The purpose of this study was to evaluate the glaucoma detected change prediction performance of the model in a real clinical setting using a retrospective longitudinal dataset.