Morphological Classification of Elbow Joints Based on Deep Learning with CT Images

The morphological classification of elbow joints plays a critical role in formulating precise orthopedic trauma treatment strategies. Traditional methods for morphological classification of the skeletal system involve labor-intensive processes of 3D modeling and feature extraction, leading to inefficiencies. To address this challenge, our study leverages deep learning techniques to develop a novel automated classification framework that directly operates on elbow joint CT images for morphological classification. Initially, the study preprocesses CT images to extract essential anatomical features and utilizes cluster analysis to generate traditional classification labels for each elbow joint sample. Subsequently, these labels, along with the original image data, are employed to train a deep learning network. Through experiment on 62,109 CT images from 120 healthy volunteers, the framework achieves a high classification accuracy of 99.6%. This research not only represents an innovative advancement in the field of automatic CT image classification but also provides a more efficient diagnostic tool for clinical treatment.