PACS-Integrated Artificial Intelligence-Based Software Tool for Rapid and Fully Automatic Bone Fracture Assessment of the Tibial Plateau.
Authors
Affiliations (7)
Affiliations (7)
- Center for Musculoskeletal Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany.
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany.
- Julius Wolff Institute, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany.
- Institute of Medical Informatics, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany.
- Learning and Intelligent Systems, Technische Universität Berlin, 10587 Berlin, Germany.
- Visage Imaging GmbH, 12163 Berlin, Germany.
- Department of Diagnostic and Interventional Radiology, University of Tübingen, 72076 Tübingen, Germany.
Abstract
<b>Background/Objectives:</b> 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. <b>Methods:</b> 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. <b>Results:</b> In 108 fully rated cases, intact bone segmentation scored very high (means 4.67/4.72; median 5) and fracture detection was good (means 4.15/4.20; median 4). In contrast, individual fracture-fragment differentiation was limited (means 2.64/2.75; median 2). Inter-reader association was very high (Spearman r 0.943-0.967; Kendall's tau-b 0.875-0.971; all <i>p</i> < 0.001). Greater fracture complexity was associated with poorer fracture-fragment differentiation. <b>Discussion:</b> A PACS-integrated AI workflow can reliably segment intact bones and detect TPF within routine imaging, supporting rapid 3D visualization for surgical planning. However, robust separation of individual fracture fragments remains the primary limitation, motivating larger and more diverse training datasets and continued model refinement.