SMART: Development and Application of a Multimodal Multi-organ Trauma Screening Model for Abdominal Injuries in Emergency Settings

作者：Yaning Wang, Jingfeng Zhang, Mingyang Li, Zheng Miao, Jing Wang, Kan He, Qi Yang, Lei Zhang, Lin Mu, Huimao Zhang · 发表于：Academic Radiology · 年份：2024 · DOI：10.1016/j.acra.2024.11.056 · 被引用次数：8 · 研究领域：Trauma and Emergency Care Studies、Abdominal Trauma and Injuries、Trauma Management and Diagnosis

RATIONALE AND OBJECTIVES: Effective trauma care in emergency departments necessitates rapid diagnosis by interdisciplinary teams using various medical data. This study constructed a multimodal diagnostic model for abdominal trauma using deep learning on non-contrast computed tomography (CT) and unstructured text data, enhancing the speed and accuracy of solid organ assessments. MATERIALS AND METHODS: Data were collected from patients undergoing abdominal CT scans. The SMART model (Screening for Multi-organ Assessment in Rapid Trauma) classifies trauma using text data (SMART_GPT), non-contrast CT scans (SMART_Image), or both. SMART_GPT uses the GPT-4 embedding API for text feature extraction, whereas SMART_Image incorporates nnU-Net and DenseNet121 for segmentation and classification. A composite model was developed by integrating multimodal data via logistic regression of SMART_GPT, SMART_Image, and patient demographics (age and gender). RESULTS: This study included 2638 patients (459 positive, 2179 negative abdominal trauma cases). A trauma-based dataset included 1006 patients with 1632 real continuous data points for testing. SMART_GPT achieved a sensitivity of 81.3% and an area under the receiver operating characteristic curve (AUC) of 0.88 based on unstructured text data. SMART_Image exhibited a sensitivity of 87.5% and an AUC of 0.81 on non-contrast CT data, with the average sensitivity exceeding 90% at the organ level. The integrated SMART model achieved a sensitivity o...