Automated determination of hip arthrosis on the Kellgren–Lawrence scale in pelvic digital radiographs scans using machine learning

Background and Objective: Automated analysis of digital radiographs of the pelvis to determine the hip arthrosis state in concordance with the Kellgren–Lawrence scale could facilitate and standardize radiogram descriptions. Methods: This research evaluates and compares the applicability of the traditional machine-learning approach based on the textural features fed to the classifier and the deep-learning networks of various architectures. Results: The investigation performed for the binary problem, where the healthy and the most severe state of hip arthrosis were considered, proved that the distinction of these classes is possible for both considered approaches. However, the outcomes recorded for deep-learning methods overcome significantly other approaches, resulting in a correct classification ratio equal to 0.98. When all five classes are considered, the results drop, primarily due to the underrepresentation of such cases. Conclusions: The influence of data pre-processing was investigated, showing that it is insignificant for deep-learning models and that the statistical dominance analysis approach dominates for traditional models. The evaluation also indicates that the deep-learning models must be trained on the selected region of interest. Otherwise, they lack precision and have problems determining the significant changes depicting the arthrosis.