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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".