Auditory brainstem response (ABR) is widely used in clinical practice to assess hearing, with thresholds typically determined by clinicians via visual inspection-a process prone to human bias. Various statistical methods have therefore been developed to assist clinicians with this task and improve test accuracy and efficiency. However, existing methods primarily focus on determining the presence or absence of an ABR at a single stimulus level, thereby neglecting inter-level correlations that hold valuable information. This work proposes the Dynamic Time Warping (DTW)-Aware Series-Temporal Transformer (DTWA-STformer), a novel deep learning framework for automated ABR threshold estimation from a series of ABR waveforms. DTWA-STformer employs a DTW similarity-aware Series Transformer to capture inter-waveform dependencies, enhanced by stimulus level-informed positional encodings. A hierarchical multi-scale Temporal Transformer then extracts rich temporal representations, followed by a multi-class classifier for threshold prediction. The model was trained and evaluated on three pre-recorded datasets, including two large-scale human datasets (Dataset I: 8,350 subjects; Dataset II: 136 subjects) and a public mouse dataset (8,259 mice). The model achieved exact-match/±10 dB accuracies of 92.08%/99.31% (Dataset I), 90.35%/98.75% (Dataset II), 73.45%/99.27% (mouse click ABRs), and 60.85%/98.82% (mouse tone-pip ABRs), outperforming existing state-of-the-art methods for threshold estimation across both human and mouse data. Results show that DTWA-STformer provides accurate and objective threshold estimation from pre-recorded waveforms and may serve as a post hoc tool for verifying examiner-estimated thresholds. In future work, the approach could be integrated with active learning rules for dynamically selecting stimulus levels and more efficiently homing in on hearing threshold.
Liu et al. (Thu,) studied this question.