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Chronic shoulder pain ranks among the most frequently occurring health issues in India. Astonishingly, more than 75% of MSK pathology patients were not appropriately diagnosed. This paper aimed at filling this lacuna by developing an ML model based on the CNN architectural structure named DenseNet-121, for classifying MSK shoulder pain pathologies from ultrasound images. In this research study, more than 70 ultrasound images showing conditions like tenosynovitis and tendon tears were analyzed. The model, which was optimized sufficiently for hyperparameters, reflected an overall accuracy of 82%. Thus, the integration of CNN-based ML into clinical settings will certainly enhance diagnostic performances and progress further to improve both care and outcomes in treatment. Our approach is a promising advancement in the diagnosis of chronic shoulder pain and provides a reliable and user-friendly tool that is amenable for use in clinical applications.

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Machine-Learning Approach for Classifying Shoulder Pain Pathologies Using Ultrasound Imaging

  • 会议论文

  • 发表日期：

    2026年

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  • Garima Shukla,
  • Khushi Bhagat,
  • Vedant Chaudhari,
  • E. Navaneeth Krishnan

摘要

Chronic shoulder pain ranks among the most frequently occurring health issues in India. Astonishingly, more than 75% of MSK pathology patients were not appropriately diagnosed. This paper aimed at filling this lacuna by developing an ML model based on the CNN architectural structure named DenseNet-121, for classifying MSK shoulder pain pathologies from ultrasound images. In this research study, more than 70 ultrasound images showing conditions like tenosynovitis and tendon tears were analyzed. The model, which was optimized sufficiently for hyperparameters, reflected an overall accuracy of 82%. Thus, the integration of CNN-based ML into clinical settings will certainly enhance diagnostic performances and progress further to improve both care and outcomes in treatment. Our approach is a promising advancement in the diagnosis of chronic shoulder pain and provides a reliable and user-friendly tool that is amenable for use in clinical applications.