Abstract
Background/Objective: Accurate assessment of the posterior tibial slope (PTS) is essential for optimal alignment and kinematic restoration in unicompartmental knee arthroplasty (UKA). This study aimed to evaluate the accuracy of three commonly used radiographic PTS measurement techniques-the anterior tibial cortex (ATC), tibial proximal anatomical axis (TPAA), and posterior tibial cortex (PTC)-by comparing them with the intraoperatively achieved tibial resection slope, using digital volume tomography (DVT) of intraoperative tibial resectates as an executed resection reference. Methods: In this retrospective study, 39 patients undergoing medial UKA were analyzed. Standardized lateral knee radiographs were used to measure the complement angle β using ATC, TPAA, and PTC reference axes. Intraoperatively obtained tibial resectates were scanned using DVT to provide a high-resolution three-dimensional reference. The conventional posterior tibial slope was defined as α (PTS) = 90° - β (measured angle). Agreement and systematic bias between radiographic and DVT measurements were assessed using Wilcoxon signed-rank tests and Bland-Altman analyses. Results: The mean DVT-derived β was 86.48° ± 1.62° (α 3.52°). ATC 79.69° ± 3.14° (α 10.31°) and TPAA 82.50° ± 2.95° (α 7.50°) yielded significantly lower β values than DVT (both p < 0.0001), whereas PTC (86.24° ± 2.51°; α 3.76°) showed no significant difference (p = 0.419). Bland-Altman analyses demonstrated minimal bias for PTC (-0.25°) compared with larger negative biases for ATC (-6.79°) and TPAA (-3.99°) (negative bias indicates lower β and therefore higher conventional posterior tibial slope α). Conclusions: Among the evaluated methods, the PTC technique most accurately reflects the intraoperatively achieved tibial resection slope when benchmarked against DVT measurements. Incorporating the PTC method into preoperative planning may improve the radiographic estimation and standardization of the achieved tibial cut in UKA. Further studies should assess its impact on clinical outcomes and explore integration into automated measurement workflows.