用深度学习分析敲击声，判断人工髋关节植入稳定性。
First Deep Learning Approach to Hammering Acoustics for Stem Stability Assessment in Total Hip Arthroplasty
- 用时频谱+伪标签训练深度模型识别敲击声
- 手术中准确率达91.17%，可稳定评估植入物固定情况
- 适合骨科手术辅助系统开发，尤其关注声学分析
音频事件分类在医疗应用中崭露头角。在全髋关节置换术（THA）中，术中敲击声为评估股骨柄初始稳定性提供了关键线索，但因股骨形态、假体尺寸和手术技术差异导致的变异性限制了传统评估方法。本文提出首个针对该任务的深度学习框架，采用基于对数梅尔频谱图特征的TimeMIL模型，并引入伪标签增强训练。在术中录音数据上，模型准确率达到91.17% ± 2.79%，展现了对假体稳定性可靠估计的能力。对比实验进一步表明，减少股骨柄品牌多样性可提升模型性能，但数据集规模有限仍是主要瓶颈。这些结果证实，基于深度学习的音频事件分类是实现THA术中稳定性评估的可行方案。
原文摘要 · Abstract (English)
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.
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