基于无对比剂头颅CT的AI模型，可高效精准识别16类神经创伤病变。
A Non-contrast Head CT Foundation Model for Comprehensive Neuro-Trauma Triage
- 用大语言模型自动生成多标签标注，训练3D基础模型检测多种脑外伤。
- 在16种病变上平均AUC达0.861，对出血、中线移位等关键病变更准确。
- 适合急诊影像科医生用于快速筛查，提升危重患者分诊效率。
人工智能与医学影像的进展为缓解头颅CT检查量激增和放射科医生短缺提供了新可能。本研究提出一种用于检测多种神经创伤病变的3D基础模型。通过大语言模型（LLMs）实现自动标注，构建了涵盖危急状况的多标签数据集。模型先预训练用于血肿亚型分割与脑解剖分区，再通过多模态微调整合至综合神经创伤检测网络。评估显示，其在主要神经创伤发现（如出血、中线移位）及较少见但危急的病变（如脑水肿、动脉高密度）上均表现出色，性能优于CT-CLIP。融合神经特异性特征后，16类神经创伤条件的平均AUC达到0.861，显著提升诊断能力。该工作为急诊放射学中的AI辅助神经创伤诊断树立了新基准。
原文摘要 · Abstract (English)
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.
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