Classification and Assessment of Hand Arthritis Stage using Support Vector Machine.

Arthritis is one of the most common health problems affecting people around the world. The goal of the work presented work is to classify and categorizing hand arthritis stages for patients, who may be developing or have developed hand arthritis, using machine learning. Stage classification was done using finger border detection, developed curvature analysis, principal components analysis, support vector machine and K-nearest neighbor algorithms. The outcome of this work showed that the proposed method can classify subject finger proximal interphalangeal joints (PIP) and distal interphalangeal joints (DIP) into stage classes with promising accuracy, especially for binary classification.

Toward development of mobile application for hand arthritis screening.

Arthritis is one of the most common health problems affecting people throughout the world. The goal of the work presented in this paper is to provide individuals, who may be developing or have developed arthritis, with a mobile application to assess and monitor the progress of their disease using their smartphone. The image processing algorithm includes finger border detection algorithm to monitor joint thickness and angular deviation abnormalities, which are common symptoms of arthritis. In this work, we have analyzed and compared gradient, thresholding and Canny algorithms for border detection. The effect of image spatial resolution (down-sampling) is also investigated. The results calculated based on 36 joint measurements show that the mean errors for gradient, thresholding, and Canny methods are 0.20, 2.13, and 2.03 mm, respectively. In addition, the average error for different image resolutions is analyzed and the minimum required resolution is determined for each method. The results confirm that recent smartphone imaging capabilities can provide enough accuracy for hand border detection and finger joint analysis based on gradient method.