Deep learning for tibial plateau fracture detection and classification

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. 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. 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%. 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.