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An algorithmic approach to reducing unexplained pain disparities in underserved populations.

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A deep learning model predicting osteoarthritis pain from X-rays significantly reduced racial disparities in pain assessment. This AI approach better captures patient pain, potentially improving treatment access for underserved populations.

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Area of Science:

  • Orthopedics
  • Artificial Intelligence
  • Health Disparities

Background:

  • Underserved populations report higher pain levels, even with similar objective osteoarthritis severity.
  • Existing pain disparities suggest external factors like stress may contribute to pain, beyond radiographic evidence.

Purpose of the Study:

  • To develop and evaluate a deep learning (DL) model using knee X-rays to predict patient-experienced pain.
  • To assess if DL-based osteoarthritis severity measures can reduce unexplained racial and socioeconomic disparities in pain.

Main Methods:

  • A deep learning algorithm was trained on knee X-rays to predict patient-reported pain levels.
  • The model's ability to account for racial disparities in pain was compared to traditional radiologist grading.
  • The influence of training data diversity on the algorithm's performance in reducing disparities was analyzed.

Main Results:

  • The DL approach explained 43% of racial disparities in pain, a 4.7x improvement over standard radiographic measures (which explained only 9%).
  • Similar reductions in unexplained disparities were observed for lower-income and less-educated patients.
  • The algorithm's effectiveness in reducing disparities was linked to the diversity of its training dataset.

Conclusions:

  • Deep learning models can more accurately reflect patient-experienced pain in osteoarthritis, particularly for underserved groups.
  • Algorithmic severity measures, trained on diverse data, can identify knee-specific pain factors missed by human graders.
  • This approach holds potential for reducing treatment access disparities, such as for knee arthroplasty.