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Toward development of mobile application for hand arthritis screening
Summary
This study developed a mobile app using smartphone imaging for arthritis monitoring. The gradient method achieved high accuracy in detecting joint changes, offering a promising tool for disease assessment.
Area of Science:
- Biomedical Engineering
- Medical Imaging
- Mobile Health
Background:
- Arthritis is a prevalent global health issue.
- Monitoring disease progression is crucial for patient management.
- Existing methods for arthritis assessment can be cumbersome.
Purpose of the Study:
- To develop a mobile application for arthritis assessment and monitoring.
- To utilize smartphone imaging for analyzing joint abnormalities.
- To evaluate image processing algorithms for finger joint analysis.
Main Methods:
- Comparison of gradient, thresholding, and Canny algorithms for finger border detection.
- Analysis of image spatial resolution effects on accuracy.
- Validation using 36 joint measurements.
Main Results:
- The gradient method demonstrated the lowest mean error (0.20 mm) in joint measurements.
- Thresholding and Canny methods showed higher mean errors (2.13 mm and 2.03 mm, respectively).
- Smartphone imaging capabilities are sufficient for accurate hand border detection using the gradient method.
Conclusions:
- A mobile application utilizing smartphone imaging can effectively monitor arthritis progression.
- The gradient-based image processing algorithm offers high accuracy for joint analysis.
- This technology provides a convenient and accessible tool for arthritis patients.
