Artificial intelligence to automatically measure glenoid inclination, humeral alignment, and the lateralization and distalization shoulder angles on postoperative radiographs after reverse shoulder arthroplasty

• Published in: Seminars in arthroplasty Vol. 34; no. 3; pp. 779 - 788
• Main Authors: Yang, Linjun, de Marinis, Rodrigo, Yu, Kristin, Marigi, Erick, Oeding, Jacob F., Sperling, John W., Sanchez-Sotelo, Joaquin
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2024
• Subjects: Artificial intelligence; Distalization; Glenoid inclination; Neck-shaft angle; Overall lateralization; Reverse shoulder arthroplasty; Glenoid inclination; Overall lateralization; Distalization; Neck-shaft angle; Retrospective Diagnostic Agreement Study; Artificial intelligence; Level III; Reverse shoulder arthroplasty
• ISSN: 1045-4527
• DOI: 10.1053/j.sart.2024.05.002

Radiographic evaluation of the implant configuration after reverse shoulder arthroplasty (RSA) is time-consuming and subject to interobserver disagreement. The final configuration is a combination of implant features and surgical execution. Artificial intelligence (AI) algorithms have been shown to perform accurate and efficient analysis of images. The purpose of this study was to develop an AI algorithm to automatically measure glenosphere inclination, humeral component inclination, and the lateralization and distalization shoulder angles (DSAs) on postoperative anteroposterior radiographs after RSA. The Digital Imaging and Communications in Medicine files corresponding to postoperative anteroposterior radiographs obtained after implantation of 143 RSAs were retrieved and used in this study. Four angles were analyzed: (1) glenoid inclination angle (GIA, between the central fixation feature of the glenoid and the floor of the supraspinatus fossa), (2) humeral alignment angle (HAA, between the long axis of the humeral shaft and a perpendicular to the metallic bearing of the prosthesis), (3) DSA, and (4) lateralization shoulder angle (LSA). A UNet segmentation model was trained to segment bony and implant elements using manually segmented training (n = 89) and validation (n = 22) images. Then, an image-processing–based pipeline was developed to measure all 4 angles using AI-segmented images. Measures performed by 3 physician observers and the AI algorithm were then completed in 32 additional images. The agreements among human observers and between observers and the AI algorithm were evaluated using intraclass correlation coefficients (ICCs) and absolute differences in degree. The ICCs (95% confidence interval) for manual measurements of LSA, DSA, GIA, and HAA were 0.79 (0.55, 0.90), 0.90 (0.80, 0.95), 0.96 (0.93, 0.98), and 0.99 (0.97, 0.99), respectively. The AI algorithm measured the 32 images in the test set in less than 2 minutes. The agreement between observers and the AI algorithm was lowest when measuring the LSA for observer 2, with an ICC of 0.77 (0.52, 0.89), and an absolute difference in degrees (median [interquartile range]) of 5 (4). Better agreements were found between the AI measurements and the average manual measurements: absolute differences in degree for LSA, DSA, GIA, and HAA were 3 (5), 2 (3), 2 (2), and 2 (1), respectively; ICCs for LSA, DSA, GIA, and HAA were 0.89 (0.79, 0.95), 0.96 (0.93, 0.98), 0.85 (0.68, 0.93), and 0.98 (0.95, 0.99), respectively. The AI algorithm developed in this study can automatically measure the GIA, HAA, LSA, and DSA on postoperative anteroposterior radiographs obtained after implantation on RSA.