Automated Cervical Spine Fracture Detection via Deep Learning

Abstract

Detection of cervical spine fractures, a critical medical concern often stemming from traumatic incidents, necessitates timely and precise identification for effective patient care. This overview discusses conventional and emerging diagnostic methods, including X-rays, CT scans, and MRI, alongside their respective advantages and limitations. Advanced technologies like AI and ML algorithms show promise in improving the speed and performance of cervical spine fracture diagnosis. Challenges in detection include minimizing radiation exposure, enhancing diagnostic accuracy, and expediting image processing, with potential solutions including telemedicine and remote consultation, particularly beneficial in underserved areas.