Cervical spine fracture detection using deep learning algorithm

Artificial intelligence (AI) has revolutionized various scientific fields, including healthcare. In particular, deep learning (DL) algorithms have shown remarkable potential in medical imaging, enabling accurate disease detection and diagnosis. Among the prevalent injuries, cervical spine fractures require rapid identification for timely treatment. Recent advancements in AI and DL techniques have opened up new possibilities for automated recognition and classification of cervical spine fractures using medical imaging data. In this study, we investigate the capabilities of deep learning algorithms, specifically the convolutional neural network (CNN) architecture, for automated detection and classification of cervical spine fractures. The outcomes of our research have the potential to significantly enhance early detection and treatment of cervical spine fractures, leading to improved patient outcomes.