Leveraging Deep Learning for Accurate Detection and Precise Localization of Vertebral Fractures in Medical Imaging

Cervical spine fractures present a serious risk to the patient's health and necessitate an early and precise diagnosis for the right course of action. Deep learning techniques have recently demonstrated promising results in the analysis of medical imaging, offering a potential remedy for automated fracture detection. An innovative method for identifying cervical spine fractures using deep learning algorithms is presented in this abstract. Our suggested approach makes use of a Convolutional Neural Network (CNN) architecture that was trained on a sizable dataset of CT scans of the cervical spine (neck) at both the level of a single vertebrae and the entire patient. To ensure that the model can distinguish between fracture patterns and healthy anatomy, the dataset includes both normal and fractured cases. To increase the model's robustness and generalizability, preprocessing methods like image normalization and augmentation are used. To maximize its performance in fracture detection, the CNN is trained using a combination of supervised learning and transfer learning strategies. The experiments are performed on a separate test set made up of a wide variety of cervical spine fracture cases in order to assess the model's performance. The outcomes show how effective our deep learning methodology is at detecting fractures, with a high accuracy rate and a low false-negative rate. Additionally, the model performs better than conventional manual interpretation by radiologists, demonstrating its potential to support clinical judgement. Rapid and automated analysis, reduced human error, and potentially improved patient outcomes are just a few benefits of the proposed deep learning-based cervical spine fracture detection system. Its incorporation into current radiology workflows may increase productivity and permit prompt interventions, which would improve patient care.