Artificial Intelligence-Assisted Estimation of the Center of Rotation of Angulation (CORA) in Canine Forelimb Radiographs

초록

Angular limb deformity (ALD) in the canine forelimbs requires precise quantitative evaluation for accurate diagnosis and optimal surgical planning. The Center of Rotation of Angulation (CORA) methodology, though accurate, is limited by its time consumption and inter-observer variability; therefore, we developed an AI-based model to automatically predict the CORA point and six angular parameters using antebrachial radiographs. A total of 504 radiographs from 126 dogs (54 chondrodystrophic and 72 non-chondrodystrophic; mean age: 8.62 years) taken between January 2018 and July 2024 were retrospectively analyzed. Radius segmentation was performed using a VGG11-based U-Net11 model, and key points for joint orientation lines (JOLs) were identified using a High-Resolution Network (HRNet). A rule-based algorithm estimated the proximal and distal radial anatomical axes (RAAs) and defined the CORA point. The deep learning model performance was evaluated using the Intersection over Union (IoU), Dice Similarity Coefficient (DSC), and Normalized Mean Error (NME). The segmentation accuracy reached IoUs of 0.926 (frontal) and 0.919 (sagittal), with DSCs of 0.961 and 0.958, respectively. The NME values were 0.0061 (frontal) and 0.0047 (sagittal). The CORA predictions closely matched those of veterinarians, particularly in chondrodystrophic breeds. Intraclass correlation coefficients (ICC) exceeded 0.9 for most angle measurements. The average inference time was 1.19 seconds per image. This automated approach offers a fast and consistent evaluation of the CORA and related angles, supporting the clinical assessment of ALD while minimizing inter-observer variability.

키워드