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<p>Recent sensing advances have enabled the use of knee acoustic emissions (KAEs)—sounds generated during flexion and extension—as non-invasive biomarkers for detecting knee osteoarthritis (OA). Most existing KAE-based OA classifiers use hand-crafted features with conventional machine learning, which can work on small datasets but often generalize poorly across devices and recording conditions and provide limited insight into the acoustic patterns driving predictions. We propose a robust and interpretable deep transfer learning framework that classifies OA directly from raw KAE signals. The method learns discriminative time-frequency representations, leverages transfer learning to mitigate data sparsity, and incorporates explainable artificial intelligence (XAI) to confirm that decisions rely on physiologically plausible acoustic components. Using a clinically representative KAE dataset that, for the first time, includes a substantial group of healthy participants with high body mass index, we systematically compare the proposed approach with several benchmark algorithms. Our method consistently attains an average accuracy of about 89% for distinguishing OA from healthy knees across multiple initializations and dataset splits, indicating strong performance and stability. XAI visualizations further highlight the key time-frequency regions that influence the model’s predictions. This work demonstrates the promise of deep transfer learning for accurate, interpretable, and scalable OA assessment and home monitoring.</p>

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A robust and interpretable deep transfer learning framework on knee acoustic emissions for osteoarthritis classification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月2日

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  • Onur Selim Kilic,
  • Ahmet Rasim Emirdagi,
  • Christopher J. Nichols,
  • Quentin Goossens,
  • Dave Ewart,
  • Omer T. Inan

摘要

Recent sensing advances have enabled the use of knee acoustic emissions (KAEs)—sounds generated during flexion and extension—as non-invasive biomarkers for detecting knee osteoarthritis (OA). Most existing KAE-based OA classifiers use hand-crafted features with conventional machine learning, which can work on small datasets but often generalize poorly across devices and recording conditions and provide limited insight into the acoustic patterns driving predictions. We propose a robust and interpretable deep transfer learning framework that classifies OA directly from raw KAE signals. The method learns discriminative time-frequency representations, leverages transfer learning to mitigate data sparsity, and incorporates explainable artificial intelligence (XAI) to confirm that decisions rely on physiologically plausible acoustic components. Using a clinically representative KAE dataset that, for the first time, includes a substantial group of healthy participants with high body mass index, we systematically compare the proposed approach with several benchmark algorithms. Our method consistently attains an average accuracy of about 89% for distinguishing OA from healthy knees across multiple initializations and dataset splits, indicating strong performance and stability. XAI visualizations further highlight the key time-frequency regions that influence the model’s predictions. This work demonstrates the promise of deep transfer learning for accurate, interpretable, and scalable OA assessment and home monitoring.