Machine learning models can define clinically relevant bone density subgroups based on patient-specific calibrated computed tomography scans in patients undergoing reverse shoulder arthroplasty.

Background: Reduced bone density is recognized as a predictor for potential complications in reverse shoulder arthroplasty (RSA). While humeral and glenoid planning based on preoperative computed tomography (CT) scans assist in implant selection and position, reproducible methods for quantifying the patients' bone density are currently not available. The purpose of this study was to perform bone density analyses including patient-specific calibration in an RSA cohort based on preoperative CT imaging. It was hypothesized that preoperative CT bone density measures would provide objective quantification of the patients' humeral bone quality.

Methods: This study consisted of 3 parts, (1) analysis of a patient-specific calibration method in cadaveric CT scans, (2) retrospective application in a clinical RSA cohort, and (3) clustering and classification with machine learning (ML) models. Forty cadaveric shoulders were scanned in a clinical CT and compared regarding calibration with density phantoms, air muscle, and fat (patient-specific) or standard Hounsfield unit. Postscan patient-specific calibration was used to improve the extraction of 3-dimensional regions of interest for retrospective bone density analysis in a clinical RSA cohort (n = 345). ML models were used to improve the clustering (Hierarchical Ward) and classification (support vector machine) of low bone densities in the respective patients.

Results: The patient-specific calibration method demonstrated improved accuracy with excellent intraclass correlation coefficients for cylindrical cancellous bone densities (intraclass correlation coefficient >0.75). Clustering partitioned the training data set into a high-density subgroup consisting of 96 patients and a low-density subgroup consisting of 146 patients, showing significant differences between these groups. The support vector machine showed optimized prediction accuracy of low and high bone densities compared to conventional statistics in the training (accuracy = 91.2%; area under curve = 0.967) and testing (accuracy = 90.5%; area under curve = 0.958) data set.

Conclusion: Preoperative CT scans can be used to quantify the proximal humeral bone quality in patients undergoing RSA. The use of ML models and patient-specific calibration on bone mineral density demonstrated that multiple three-dimensional bone density scores improved the accuracy of objective preoperative bone quality assessment. The trained model could provide preoperative information to surgeons treating patients with potentially poor bone quality.