YOLOv8-Seg: a deep learning approach for accurate classification of osteoporotic vertebral fractures

Abstract

Introduction: This study investigates the application of a deep learning model, YOLOv8-Seg, for the automated classification of osteoporotic vertebral fractures (OVFs) from computed tomography (CT) images. Methods: A dataset of 673 CT images from patients admitted between March 2013 and May 2023 was collected and classified according to the European Vertebral Osteoporosis Study Group (EVOSG) system. Of these, 643 images were used for training and validation, while a separate set of 30 images was reserved for testing. Results: The model achieved a mean Average Precision (mAP50-95) of 85.9% in classifying fractures into crush, anterior wedge, and biconcave types. Discussion: The results demonstrate the high proficiency of the YOLOv8-Seg model in identifying OVFs, indicating its potential as a decision-support tool to streamline the current manual diagnostic process. This work underscores the significant potential of deep learning to assist medical professionals in achieving early and precise diagnoses, thereby improving patient outcomes.