Artificial intelligence to automatically measure on radiographs the postoperative positions of the glenosphere and pivot point after reverse total shoulder arthroplasty

Abstract

Background

Radiographic evaluation of the implant configuration after reverse total shoulder arthroplasty (rTSA) is a time-consuming task that is frequently subject to interobserver disagreement. Deep learning (DL) artificial intelligence algorithms have previously demonstrated high accuracy when analyzing relevant angles to determine rTSA distalization and lateralization, as well as glenoid inclination and humeral alignment. The goal of this study is to build on this existing work to automatically measure the postoperative radiographic location of the glenosphere center of rotation (GCR) and the pivot point (PP) in reference to the scapula.

Methods

A total of 417 primary rTSA postoperative anteroposterior radiographs were retrieved and utilized for this study. Five measurements were designed and manually performed by 3 observers: (1) the medial position and (2) the inferior position of the geometric center of rotation of the glenosphere (glenosphere center of rotation medialization [GCRm] and glenosphere center of rotation inferiorization [GCRi], respectively) relative to the most lateral aspect of the inferior acromion, as well as (3) the projection of the PP to GCR vector on the fossa line (PP projection), (4) the distance between GCR and glenoid (GCR-glenoid distance), and (5) the overall glenoid lateral offset (GLO). Subsequently, a DL algorithm was developed to automatically segment the radiograph and perform the same measurements described above. All measurements were corrected for radiographic magnification using the known glenosphere diameter for each shoulder. Intraclass Correlation Coefficients (ICCs) were calculated to assess interobserver and DL-human agreements on all measurements.

Results

The DL algorithm achieved an average Dice Coefficient of 0.86, indicating good segmentation accuracy. The ICCs (95% confidence interval) amongst human observers were 0.86 (0.81-0.90) for the GCRm, 0.93 (0.9-0.95) for the GCRi, 0.95 (0.92-0.96) for the PP projection, 0.85 (0.79-0.89) for GCR-glenoid distance, and 0.92 (0.88-0.95) for GLO. The ICCs between the DL-derived measurements and the average of manual measurements were 0.95 (0.92-0.96) for the GCRm, 0.90 (0.84-0.93) for the GCRi, 0.96 (0.94-0.98) for the PP projection, 0.91 (0.87-0.94) for GCR-glenoid distance, and 0.92 (0.88-0.95) for GLO. The DL algorithm automatically analyzed each testing image in 2 seconds.

Conclusion

The developed DL algorithm can automatically measure the location of the glenosphere geometric center of rotation and the location of the PP on postoperative radiographs obtained after primary rTSA. Agreement between DL-derived measures and those from human observers was high. This DL algorithm adds to the armamentarium of tools available for automatic assessment of final implant position on radiographs after rTSA.