Unlike prior methods focusing on waveform-level detection of the presence or absence of an ABR, this study presents the first deep learning model for automatic thresholding of ABRs from multi-level waveform stacks. It demonstrates strong generalizability across centers, species, and stimulus types, highlighting its potential for clinically applicable, efficient, and automated threshold estimation. Future work should focus on broader external validation and the integration of the model into real-time ABR acquisition workflows, enabling concurrent threshold estimation during ongoing measurements for improved clinical utility.
Liu et al. (Thu,) studied this question.