Automatic Assessment of Radiological Parameters of the Distal Radius Using a Hybrid Approach Combining Deep Learning and a Computer-Aided Diagnostic Algorithm

Abstract

Backgroud: The use of deep learning algorithms in medical imaging has increased rapidly. This study aimed to develop an automated, hybrid approach combining a deep learning architecture and a conventional computer-aided diagnostic method to detect anatomical landmarks and measure radiological parameters in wrist radiography. Methods: ). Results: = 0.78). Conclusions: This novel automated hybrid method can accurately identify landmarks on wrist radiographs and automatically generate the radiological parameters of the distal radius. This method saves time and reduces human labor in creating datasets for training segmentation models and developing image processing algorithms.

Ähnliche Arbeiten