Related Experiment Video
Updated: Jul 16, 2025
Measurement of Dynamic Scapular Kinematics Using an Acromion Marker Cluster to Minimize Skin Movement Artifact
Published on: February 10, 2015
Deep learning for automated measurement of CSA related acromion morphological parameters on anteroposterior
Yamuhanmode Alike1, Cheng Li1, Jingyi Hou1
1Department of Orthopaedic Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Background:
The Critical Shoulder Angle Related Acromion Morphological Parameter (CSA- RAMP) is a valuable tool in the analyzing the etiology of the rotator cuff tears (RCTs). However, its clinical application has been limited by the time-consuming and prone to inter- and intra-user variability of the measurement process.
Objectives:
To develop and validate a deep learning algorithm for fully automated assessment of shoulder anteroposterior radiographs associated with RCTs and calculation of CSA-RAMP.
Methods:
Retrospective analysis was conducted on radiographs obtained from computed tomography (CT) scans and X-rays performed between 2018 and 2020 at our institution. The development of the system involved the utilization of digitally reconstructed radiographs (DRRs) generated from each CT scan. The system's performance was evaluated by comparing it with manual and semiautomated measurements on two separate test datasets: dataset I (DRRs) and dataset II (X-rays). Standard metrics, including mean average precision (AP), were utilized to assess the segmentation performance. Additionally, the consistency among fully automated, semiautomated, and manual measurements was comprehensively evaluated using the Pearson correlation coefficient and Bland-Altman analysis.
Results:
A total of 1080 DRRs generated from 120 consecutive CT scans and 159 X-ray films were included in the study. The algorithm demonstrated excellent segmentation performance, with a mean AP of 57.67 and an AP50 of 94.31. Strong inter-group correlations were observed for all CSA-RAMP measurements in both test datasets I (automated versus manual, automated versus semiautomated, and semiautomated versus manual; r = [0.990---0.997], P < 0.001) and dataset II (r = [0.984---0.995], P < 0.001). Bland-Altman analysis revealed low bias for all CSA-RAMP measurements in both test datasets I and II, except for CD (with a maximum bias of 2.49%).
Conclusions:
We have successfully developed a fully automated algorithm capable of rapidly and accurately measuring CSA-RAMP on shoulder anteroposterior radiographs. A consistent automated CSA- RAMP measurement system may accelerate powerful and precise studies of disease biology in future large cohorts of RCTs patients.
