A method for Kashin-Beck disease auxiliary diagnosis based on the features in regions of the potential lesion

Kashin-Beck disease (KBD) is a severe arthropathy that causes deformity. Patients with advanced stages of KBD often show symptoms, such as short stature. Early-stage diagnosis and treatment can effectively prevent the disease from worsening. Diagnosis of early-stage patients is usually made by X-ray examination. However, the time-consuming image recognition and the lack of professional doctors may delay the patient's condition. Therefore, a method that can efficiently complete the auxiliary diagnosis is necessary.

This study presents a KBD auxiliary diagnosis method based on radiographs, which uses deep learning to locate potential lesion regions and extract features for accurate diagnosis.

This work presents a method that relies on hand radiographs to locate eight regions of the potential lesion (RoPL) and finally make the KBD auxiliary diagnosis. The localization of RoPL is achieved through a two-step model, with the first step predicting a rough location and a deep convolutional neural network (DCNN) with attention mechanism used to generate precise center coordinates based on the previous step's results. Based on the localization result, regional features are extracted, which provides information about the joints and textures of RoPL from a finer granularity. Another DCNN is utilized to obtain general features from hand radiographs, which provide morphological and structural information about the entire hand bone These features offer a concatenated feature for categorization to raise accuracy. A doctor-like approach is adopted to diagnose based on regional features to enhance performance, and a final decision is made using a vote that considers diagnostic outcomes from all aspects. The dataset used in our study was collected by our research team in KBD-endemic areas of Tibet since 2017, containing 373 diseased and 764 normal images.

Our model guarantees that over 95% of the predicted coordinates are within five pixels of the real coordinates according to Euclidean distance. The accuracy of the diagnostic network achieved 91.3%, with precision and recall achieving 83% and 87%, respectively. Compared to the approach without exact localization of the illness region on the same test set, our method achieved a roughly 6% increase in accuracy and nearly 30% increase in recall rate.

Based on hand radiographs, this study suggests a novel method for KBD diagnosis. The high-precision localization network guarantees precise extraction of lesion-prone regional features, and the multi-scale features and innovative classification method further enhance model performance compared to related approaches.