Abstract
Abstract
Preoperative computed tomography (CT) is routinely acquired for planning robotic-assisted total knee arthroplasty (TKA), yet osteophyte burden is not quantitatively characterized. We developed automated CT-based osteophyte quantification and evaluated it against blinded de novo expert-consensus annotations and an intraoperative accessible resected osteophyte volume reference. A human-in-the-loop 3-dimensional U-Net was developed using 387 knee CT scans. The frozen model was evaluated against blinded de novo expert-consensus annotations in two independent external cohorts (100 knees each). Spatial and volume agreement were assessed using Dice coefficient, surface Dice at 1-mm tolerance, 95th-percentile Hausdorff distance (HD95), intraclass correlation coefficient (ICC), mean absolute error, and Bland–Altman analysis. In a prospective cohort of 66 robotic-assisted TKAs, CT-derived total osteophyte volume was compared with the water-displacement volume of accessible osteophytes that were resected and recovered intraoperatively (accessible resected osteophyte volume reference). In the external cohorts, median total Dice was 0.913 and 0.909, surface Dice was 0.944 and 0.945, HD95 was 1.16 and 1.09 mm, and volume ICC was 0.977 and 0.972, respectively. Automated estimates showed conservative volume bias relative to expert consensus (− 0.499 and − 0.365 cc). In the surgical cohort, automated CT-derived volume showed cohort-level agreement with the accessible resected osteophyte volume reference (ICC, 0.876; mean absolute error, 1.224 cc; mean difference, + 0.401 cc; 95% limits of agreement, − 2.785 to + 3.586 cc). Automated CT-based osteophyte quantification showed consistent spatial and volumetric agreement with blinded de novo expert-consensus annotations across two external cohorts and cohort-level agreement with the accessible resected osteophyte volume reference. This scalable method provides a preoperative measure of osteophyte burden for arthroplasty research.