Abstract
Currently, a diagnostic tool to identify cochlear synaptopathy in humans is unavailable. However, several features of electrophysiological responses evoked by auditory stimuli have been evaluated that may serve as useful indicators of synapse pathology. This study is part of a larger project whose goal is to develop a statistical model designed to accurately and reliably detect cochlear synaptopathy in humans. Univariate logistic regression analyses were employed to identify electrophysiological outcomes that predict synapse pathology in a guinea pig model and the relative performance of each model was evaluated. Previous efforts to assess model performance included area under the Receiver Operating Characteristic curve. In this report, metrics, such as F1-score and Matthews Correlation Coefficient (MCC), were included. Expectations are that analyses that incorporate true negatives, as does MCC, will more completely describe the performance of the binary classification of interest here: synapse pathology or synapse normalcy. Findings will be presented in the context of a non-human mammalian model with the ultimate purpose of developing a statistical model that can be used to optimize the diagnosis of synapse pathology in humans. [Work supported by the Department of Defense Award #W81XWH-19-1-0862.]
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call