Objective: To develop and validate a fully automated deep learning workflow that localizes key anatomical landmarks on standard canine hindlimb lateral radiographs, derives the tibial plateau angle (TPA), and recommends a saw blade size for tibial plateau leveling osteotomy (TPLO) preoperative planning.
Study design: Retrospective validation study.
Animals: Two hundred annotated lateral radiographs obtained from 130 dogs representing 14 breeds, with body weights ranging from 2.4 to 38.0 kg.
Methods: A customized four-stage U-Net was trained using three complementary grayscale representations (normalized, contrast-enhanced, and gamma-adjusted images) to detect five TPLO-related landmarks. A deterministic geometric module then calculated TPA and mapped the derived osteotomy geometry to the nearest clinically available saw blade class.
Results: The mean absolute error for TPA prediction was 1.34 ± 1.73°, and the median absolute error was 0.75°. Overall, 164/200 cases (82.0%) were within 2° and 188/200 cases (94.0%) were within 4.8° of the surgeon reference. Mean bias was -0.39°, the 95% limits of agreement ranged from -4.62° to 3.85°, and Pearson's correlation coefficient was 0.87. For saw blade size prediction, mean absolute error was 0.32 ± 0.85 mm, exact agreement was achieved in 175/200 cases (87.5%), and all predictions remained within one adjacent class.
Conclusions: The proposed pipeline provided clinically useful automated estimates of TPA and saw blade size from routine lateral radiographs. However, occasional high-impact landmark failures remained, indicating that the system should be positioned as an interpretable decision-support tool that requires surgeon verification rather than as an unsupervised autonomous planning system.
