Development of a Machine Learning-Based Model for Accurate Detection and Classification of Cervical Spine Fractures Using CT Imaging

Parsa Riazi Esfahani; Monica Guirgus; Maya Maalouf; Pasha Mazboudi; Akshay J Reddy; Reem O Sarsour; Sherif S Hassan

doi:10.7759/cureus.47328

Development of a Machine Learning-Based Model for Accurate Detection and Classification of Cervical Spine Fractures Using CT Imaging

Cureus. 2023 Oct 19;15(10):e47328. doi: 10.7759/cureus.47328. eCollection 2023 Oct.

Authors

Parsa Riazi Esfahani¹, Monica Guirgus¹, Maya Maalouf¹, Pasha Mazboudi¹, Akshay J Reddy¹, Reem O Sarsour¹, Sherif S Hassan^{2

3}

Affiliations

¹ Medicine, California University of Science and Medicine, Colton, USA.
² Anatomy, Faculty of Medicine, Cairo University, Cairo, EGY.
³ Medical Education, Anatomy, & Neuroanatomy, California University of Science and Medicine, Colton, USA.

Abstract

Cervical spine fractures represent a significant healthcare challenge, necessitating accurate detection for appropriate management and improved patient outcomes. This study aims to develop a machine learning-based model utilizing a computed tomography (CT) image dataset to detect and classify cervical spine fractures. Leveraging a large dataset of 4,050 CT images obtained from the Radiological Society of North America (RSNA) Cervical Spine Fracture dataset, we evaluate the potential of machine learning and deep learning algorithms in achieving accurate and reliable cervical spine fracture detection. The model demonstrates outstanding performance, achieving an average precision of 1 and 100% precision, recall, sensitivity, specificity, and accuracy values. These exceptional results highlight the potential of machine learning algorithms to enhance clinical decision-making and facilitate prompt treatment initiation for cervical spine fractures. However, further research and validation efforts are warranted to assess the model's generalizability across diverse populations and real-world clinical settings, ultimately contributing to improved patient outcomes in cervical spine fracture cases.

Keywords: ai & robotics in healthcare; cervical spine fractures; deep neural network; diagnosis; public health.