Audio event classification has recently emerged as a promising approach in medical applications. In total hip arthroplasty (THA), intra-operative hammering acoustics provide critical cues for assessing the initial stability of the femoral stem, yet variability due to femoral morphology, implant size, and surgical technique constrains conventional assessment methods. We propose the first deep learning framework for this task, employing a TimeMIL model trained on Log-Mel Spectrogram features and enhanced with pseudo-labeling. On intra-operative recordings, the method achieved 91.17 % +/- 2.79 % accuracy, demonstrating reliable estimation of stem stability. Comparative experiments further show that reducing the diversity of femoral stem brands improves model performance, although limited dataset size remains a bottleneck. These results establish deep learning-based audio event classification as a feasible approach for intra-operative stability assessment in THA.
@article{arxiv.2511.18725,
title = {First Deep Learning Approach to Hammering Acoustics for Stem Stability Assessment in Total Hip Arthroplasty},
author = {Dongqi Zhu and Zhuwen Xu and Youyuan Chen and Minghao Jin and Wan Zheng and Yi Zhou and Huiwu Li and Yongyun Chang and Feng Hong and Zanjing Zhai},
journal= {arXiv preprint arXiv:2511.18725},
year = {2025}
}
Comments
The manuscript, including both the title and the main text, contains issues with clarity and precision in its overall presentation, necessitating a complete withdrawal for revision