Combined evaluation of frequency doubling technology perimetry and scanning laser ophthalmoscopy for XXXX detection using automated classification.

@entity1 @entity2788 @entity726 @entity227
PURPOSE: To develop a diagnostic setup with classification rules for combined analysis of morphology [ @entity2788 ( @entity2788 )] and function [frequency doubling technology (FDT) perimetry] measurements. METHODS: We used 2 independent case-control studies from the Erlangen eye department as learning and test data for automated classification using random forests. One eye of 334 open @entity726 @entity1 and 254 controls entered the study. All individuals underwent @entity2788 scanning tomography of the optic disc, FDT screening, conventional perimetry, and evaluation of fundus photographs. Random forests were learned on individuals of the @entity726 registry (102 preperimetric @entity1 , 130 perimetric @entity1 , 161 controls). The classification performances of random forests and built-in classifiers were examined by receiver operator characteristic analysis on an independent second cohort of individuals (47 preperimetric @entity1 , 55 perimetric @entity1 , 93 controls). RESULTS: @entity2788 measurements had a higher diagnostic power for @entity726 and FDT perimetry for @entity726 @entity1 with @entity227 . A combination of all parameters using automated classification was superior to single tests in comparison to the diagnostic instrument with the higher diagnostic power in the respective group. Highest sensitivities at a fixed specificity (95%) in the @entity1 of the present test population were: @entity2788 =32%, FDT=19%, combined analysis=47% in preperimetric @entity1 and @entity2788 =76%, FDT=89%, combined analysis=96% in perimetric @entity1 . CONCLUSIONS: The feasibility of machine learning for medical diagnostic assistance could be demonstrated in @entity1 from 2 independent study populations. A predictive model using automated classification is able to combine the advantages of morphology and function, resulting in a higher diagnostic power for @entity726 detection.
