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

Key Points

• The classification accuracy achieved was over 90% in distinguishing osteoarthritis cases from healthy samples.
• This study utilized deep transfer learning to analyze knee acoustic emissions for classification.
• Analysis focused on the effectiveness of neural networks in feature extraction from acoustic signals.
• These findings highlight the potential for AI-driven tools in improving osteoarthritis diagnosis.