Deep Learning-Based Automated Detection of Cervical Spine Fractures in CT Scans for Enhanced Vehicle Structure Safety in the Automotive Industry

Abstract

Cervical spine fractures represent a severe form of spinal cord injury with potential life-altering consequences, such as tetraplegia or quadriplegia. These fractures can occur due to various causes, including motor vehicle accidents, falls, arthritis and cancer. In emergency and trauma situations, rapid and accurate detection of fractures is crucial for timely intervention. This article proposes an innovative approach that leverages deep convolutional neural networks (CNNs), specifically PyTorch based CNN and ResNet, to develop an automated solution for detecting cervical spine fractures from Computer Tomography (CT) scans. Through comparative analysis, a customized CNN emerged as the best-performing model, showcasing promising results for cervical spine fracture detection. To facilitate practical usage, the best-performing model is integrated into a user-friendly web application using the Python Django web framework. By implementing this automated detection system, medical professionals and practitioners in the automotive industry can enhance the accuracy and efficiency of cervical spine fracture diagnosis, ultimately leading to improved patient outcomes and reinforcing safety measures for vehicle structures. This technological advancement holds significant potential for revolutionizing fracture detection and management, contributing to a safer and more secure automotive industry landscape.