Deep learning system for automated detection of posterior ligamentous complex injury in patients with thoracolumbar fracture on MRI.

ABSTRACT: This study aimed to develop a deep learning (DL) algorithm for automated detection and localization of posterior ligamentous complex (PLC) injury in patients with acute thoracolumbar (TL) fracture on magnetic resonance imaging (MRI) and evaluate its diagnostic performance. In this retrospective multicenter study, using midline sagittal T2-weighted image with fracture (± PLC injury), a training dataset and internal and external validation sets of 300, 100, and 100 patients, were constructed with equal numbers of injured and normal PLCs. The DL algorithm was developed through two steps (Attention U-net and Inception-ResNet-V2). We evaluate the diagnostic performance for PLC injury between the DL algorithm and radiologists with different levels of experience. The area under the curves (AUCs)

SUBMITTER: Jo SW 

PROVIDER: S-EPMC10624679 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature