# A Non-contrast Head CT Foundation Model for Comprehensive Neuro-Trauma Triage Source: [A Non-contrast Head CT Foundation Model for Comprehensive Neuro-Trauma Triage]() Collection index: [llms.txt]() Data updated: 2026-01-02T08:55:04.924Z Authors: Youngjin Yoo, Bogdan Georgescu, Yanbo Zhang, Sasa Grbic, Han Liu, Gabriela D. Aldea, Thomas J. Re, Jyotipriya Das, Poikavila Ullaskrishnan, Eva Eibenberger, Andrei Chekkoury, Uttam K. Bodanapally, Savvas Nicolaou, Pina C. Sanelli, Thomas J. Schroeppel, Yvonne W. Lui, Eli Gibson Published: 2025-02-28T14:44:55.000Z ## Abstract Recent advancements in AI and medical imaging offer transformative potential in emergency head CT interpretation for reducing assessment times and improving accuracy in the face of an increasing request of such scans and a global shortage in radiologists. This study introduces a 3D foundation model for detecting diverse neuro-trauma findings with high accuracy and efficiency. Using large language models (LLMs) for automatic labeling, we generated comprehensive multi-label annotations for critical conditions. Our approach involved pretraining neural networks for hemorrhage subtype segmentation and brain anatomy parcellation, which were integrated into a pretrained comprehensive neuro-trauma detection network through multimodal fine-tuning. Performance evaluation against expert annotations and comparison with CT-CLIP demonstrated strong triage accuracy across major neuro-trauma findings, such as hemorrhage and midline shift, as well as less frequent critical conditions such as cerebral edema and arterial hyperdensity. The integration of neuro-specific features significantly enhanced diagnostic capabilities, achieving an average AUC of 0.861 for 16 neuro-trauma conditions. This work advances foundation models in medical imaging, serving as a benchmark for future AI-assisted neuro-trauma diagnostics in emergency radiology. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Neuro-trauma detection on Neuro-Trauma Dataset 6 major findings](): Neuro-trauma detection; Neuro-Trauma Dataset 6 major findings; Average AUC: 0.875. - [Neuro-trauma detection on Neuro-Trauma Dataset all 16 findings](): Neuro-trauma detection; Neuro-Trauma Dataset all 16 findings; Average AUC: 0.861. - [Neuro-trauma Finding Detection on CQ500](): Neuro-trauma Finding Detection; CQ500; Hemorrhage AUC: 92. ## Original sources and implementation [Original paper]() [PDF]()