[Tibial plateau fractures].

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Abstract

[Indexed for MEDLINE] 10. Knee. 2025 Jun;54:81-89. doi: 10.1016/j.knee.2025.02.001. Epub 2025 Mar 1. Deep learning for tibial plateau fracture detection and classification. van der Gaast N(1), Bagave P(2), Assink N(3), Broos S(4), Jaarsma RL(4), Edwards MJR(5), Hermans E(5), IJpma FFA(3), Ding AY(2), Doornberg JN(6), Oosterhoff JHF(7); Machine Learning Consortium. Author information: (1)Department of Orthopaedics & Trauma Surgery, Flinders Medical Centre and Flinders University, Adelaide, SA, Australia; Department of Trauma Surgery, Radboud University Medical Center, Radboud University Nijmegen, the Netherlands. Electronic address: Nynke.vandergaast@radboudumc.nl. (2)Department of Engineering Systems and Services, Faculty of Technology Policy and Management, Delft University of Technology, Delft, the Netherlands. (3)Department of Orthopaedic and Trauma Surgery, University Medical Center Groningen, University of Groningen, the Netherlands. (4)Department of Orthopaedics & Trauma Surgery, Flinders Medical Centre and Flinders University, Adelaide, SA, Australia. (5)Department of Trauma Surgery, Radboud University Medical Center, Radboud University Nijmegen, the Netherlands. (6)Department of Orthopaedics & Trauma Surgery, Flinders Medical Centre and Flinders University, Adelaide, SA, Australia; Department of Orthopaedic and Trauma Surgery, University Medical Center Groningen, University of Groningen, the Netherlands. (7)Department of Engineering Systems and Services, Faculty of Technology Policy and Management, Delft University of Technology, Delft, the Netherlands; Department of Orthopaedic and Trauma Surgery, University Medical Center Groningen, University of Groningen, the Netherlands. BACKGROUND: Deep learning (DL) has been shown to be successful in interpreting radiographs and aiding in fracture detection and classification. However, no study has aimed to develop a computer vision model for tibia plateau fractures using the Schatzker classification. Therefore, this study aims to develop a deep learning model for (1) detection of tibial plateau fractures and (2) classification according to the Schatzker classification. METHODS: A multicenter approach was performed for the collection of radiographs of patients with tibia plateau fractures. Both anteroposterior and lateral images were uploaded into an annotation software and manually labelled and annotated. The dataset was balanced for optimizing model development and split into a training set and a test set. We trained two convolutional neural networks (GoogleNet and ResNet) for the detection and classification of tibia plateau fractures following the Schatzker classification. RESULTS: A total of 1506 knee radiographs from 753 patients, including 368 tibial plateau fractures and 385 healthy knees, were used to create the algorithm. The GoogleNet algorithm demonstrated high sensitivity (92.7%) but intermediate accuracy (70.4%) and positive predictive value (64.4%) in detecting tibial plateau fractures, indicating reliable detection of fractured cases. It exhibited limited success in accurately classifying fractures according to the Schatzker system, achieving an accuracy of only 34.6% and a sensitivity of 32.1%. CONCLUSION: This study shows that detection of tibial plateau fractures is a task that a DL algorithm can grasp; further refinement is necessary to enhance their accuracy in fracture classification. Computer vision models might improve using different classification systems, as the current Schatzker classification suffers from a low interobserver agreement on conventional radiographs. Copyright © 2025 The Author(s). Published by Elsevier B.V. All rights reserved. DOI: 10.1016/j.knee.2025.02.001