224 Artificial Intelligence-based Decision Support Predicts Requirement for Neurosurgical Intervention in Acute Traumatic Brain Injury: Automated Surgical Intervention Support Tool (ASIST-TBI) Development, Validation and Simulated Prospective Deployment

INTRODUCTION: Artificial intelligence (AI) model integration into clinical workflow offers potential to optimize decision-support for transfer of acute traumatic brain injury (TBI) patients to appropriate trauma centers. METHODS: We retrospectively identified TBI patients from 2005-2021 treated at a quaternary Canadian trauma center. We employed various modeling techniques including principal component analysis, three-dimensional convolutional neural networks, and a transformer-based approach using Vision Transformer (ViT) architecture. Model training, validation, and testing was performed using head CT scans with binary ground truth labels corresponding to whether the patient received neurosurgical intervention witin 72 hours. The finalized model, termed Automated Surgical Intervention Support Tool for TBI (ASIST-TBI), was then deployed in a simulated prospective fashion on consecutive TBI patients at our center between March 2021 - September 2022. RESULTS: A dataset of 2,806 trauma patients with acute head CT scans were divided into training, validation, and testing groups; the ViT model exhibited optimal performance. There was accurate prediction of requirement for neurosurgical intervention with an area under the receiver operating curve (AUC) of 0·92, accuracy of 0·87, sensitivity of 0·87, and specificity of 0·88 on the testing cohort. In simulated 18-month prospective deployment, an additional 612 consecutive scans were used to assess the performance of ASIST-TBI. Classification accuracy remained robust with AUC of 0·89, 0·85 sensitivity, 0·84 specificity, and 0·84 accuracy. We manually reviewed false positive and false negative cases to identify reasons for misclassification. CONCLUSIONS: We developed a novel deep learning model that accurately predicts requirement for acute neurosurgical intervention using unlabeled TBI scans. ASIST-TBI has potential application to optimize state-wide triage efficiency and care pathways for brain-injured patients.