Deep-learning model measures shoulder kinematics on DDR

In this study, the goal was to develop a DL algorithm that can automatically determine the SHR, which normally requires complex and time-consuming manual calculations by clinicians and thus is rarely performed in routine clinical cases, he said.The prototype DL algorithm was trained on 447 images from 267 cases to recognize the humerus and scapula positions, calculate the required scapulothoracic and glenohumeral angles, and thus determine the SHR across the complete range of abduction.The performance of the algorithm was then evaluated by comparing its measurements with manual human reader measurements on a unique data set of 73 shoulder exams (23 normal controls, 41 rotator cuff tears, and nine adhesive capsulitis cases), with the AI and human measurements compared using intra class correlations (ICC).A total of 219 measurements were made by both the AI algorithm and manual physician methods. The inter-rater reliability among two experts who manually measured the SHR was excellent with an ICC of 0.87. This indicates that the SHR is an effective method for differentiating between injured shoulders and normal cases, Sabol noted.In addition, the correlation between the AI algorithm and the human results was moderately reliable with an ICC of 0.58, according to the analysis.“DDR with automated tools may be a practical and efficient means for routine analysis of scapular humeral rhythm and scapular kinematics in a typical patient population,” Sabol said.He noted that further...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: Digital X-Ray RSNA 2023 Source Type: news