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Osteoarthritis severity of the hip by computer-aided grading of radiographic images
I Boniatis1, L Costaridou, D Cavouras
1Department of Medical Physics, School of Medicine, University of Patras, 265 00, Patras, Greece.
Insights
A new computer-aided system accurately classifies hip osteoarthritis (OA) severity from X-rays. It distinguishes normal from osteoarthritic hips with 100% accuracy and differentiates mild/moderate from severe OA with 95.7% accuracy.
Area of Science:
- Medical Imaging
- Orthopedics
- Artificial Intelligence in Medicine
Background:
- Osteoarthritis (OA) is a degenerative joint disease impacting hip function.
- Accurate assessment of OA severity is crucial for effective treatment planning.
- Radiographic analysis is a standard method for OA diagnosis and grading.
Purpose of the Study:
- To develop and evaluate a computer-aided classification system for hip osteoarthritis (OA) severity.
- To assess the system's ability to differentiate normal hips from osteoarthritic hips.
- To evaluate the system's capability in distinguishing between mild/moderate and severe OA.
Main Methods:
- Digitization and enhancement of 64 hip radiographic images (normal and osteoarthritic).
- Manual segmentation of Hip Joint Spaces (ROIs) and generation of novel textural features.
- Implementation of a two-level classification scheme using an ensemble of three classifiers.
Main Results:
- The system achieved 100% accuracy in discriminating normal hips from osteoarthritic hips.
- The system demonstrated 95.7% accuracy in discriminating between Mild/Moderate and Severe OA.
- The classification was based on features extracted from segmented hip joint spaces.
Conclusions:
- The developed computer-aided system shows high accuracy in classifying hip OA severity.
- This system can serve as a valuable decision-supporting tool for diagnosis in clinical practice.
- The textural features extracted are effective for OA grading using radiographic images.
Abstract:
A computer-aided classification system was developed for the assessment of the severity of hip osteoarthritis (OA). Sixty-four radiographic images of normal and osteoarthritic hips were digitized and enhanced. Employing the Kellgren and Lawrence scale, the hips were grouped by three experienced orthopaedists into three OA-severity categories: Normal, Mild/Moderate and Severe. Utilizing custom-developed software, 64 ROIs corresponding to the radiographic Hip Joint Spaces were manually segmented and novel textural features were generated. These features were used in the design of a two-level classification scheme for characterizing hips as normal or osteoarthritic (1st level) and as of Mild/Moderate or Severe OA (2nd level). At each classification level, an ensemble of three classifiers was implemented. The proposed classification scheme discriminated correctly all normal hips from osteoarthritic hips (100% accuracy), while the discrimination accuracy between Mild/Moderate and Severe osteoarthritic hips was 95.7%. The proposed system could be used as a diagnosis decision-supporting tool.
