PACS-Integrated Artificial Intelligence-Based Software Tool for Rapid and Fully Automatic Bone Fracture Assessment of the Tibial Plateau

Background/Objectives: Tibial plateau fractures (TPF) are complex intra-articular injuries requiring accurate reduction and stable fixation; inadequate planning can prolong recovery and increase osteoarthritis risk. Although CT-based 3D reconstructions are routine, automated 3D identification and separation of individual fracture fragments is uncommon. We evaluated a PACS-integrated AI workflow for automated 3D segmentation of intact bones, fracture detection, and fracture-fragment delineation.

Methods

This single-center retrospective study analyzed preoperative TPF CT scans acquired on 64- and 128-slice scanners. Femur, patella, tibia, fibula, and fracture components were manually segmented in Visage 7 by a radiologist (9 years of experience) and an orthopedic surgeon (4 years). Segmentations were exported as NIfTI and used to train a 3D nnU-Net model with default preprocessing and parameters on 20 annotated TPF cases using a two-phase training strategy (initial training followed by retraining with refined labels). The trained model was integrated into Visage 7 and executed via an interactive PACS button for fully automated segmentation. Two independent readers rated outputs on a five-point Likert scale for intact bone segmentation, fracture detection, and fragment delineation; inter-reader association was assessed using Spearman and Kendall correlations.