Mabit Léo, Lepoittevin Maryne, Valls Martin, Thomas Clément, Guillevin Rémy, Herpe Guillaume
Radiology Department, University Hospital of Poitiers, 2 rue de la Milétrie, 86000 Poitiers, France.
Institut National de la Santé et de la Recherche Médicale U1313-Ischémie-Reperfusion, Metabolisme et Inflammation Sterile en Transplantation, University Hospital of Poitiers, 2 rue de la Milétrie, 86021 Poitiers, Cedex 9, France.
J Clin Med. 2025 Jun 20;14(13):4403. doi: 10.3390/jcm14134403.
: Traumatic brain injury (TBI) is a major cause of morbimortality in the world, and it can cause potential intracranial hemorrhage (ICH), a life-threatening condition that requires rapid diagnosis with computed tomography (CT). Artificial intelligence tools for ICH detection are now commercially available. : Investigate the real-world performance of qER.ai, an artificial intelligence-based CT hemorrhage detection tool, in a post-traumatic population. : Retrospective monocentric observational study of a dataset of consecutively acquired head CT scans at the emergency radiology unit to explore brain trauma. AI performance was compared to ground truth determined by expert consensus. A subset of night shift cases with the radiological report of a junior resident was compared to the AI results and ground truth. : A total of 682 head CT scans were analyzed. AI demonstrated a sensitivity of 88.8% and a specificity of 92.1% overall, with a positive predictive value of 65.4% and a negative predictive value of 98%. AI's performance was comparable to that of junior residents in detecting ICH, with the latter showing a sensitivity of 85.7% and a high specificity of 99.3%. Interestingly, the AI detected two out of three ICH cases missed by the junior residents. When AI assistance was integrated, the combined sensitivity improved to 95.2%, and the overall accuracy reached 98.8%. : This study shows better performance from AI and radiologist residents working together than each one alone. These results are encouraging for rethinking the radiological workflow and the future of triage of this large population of brain traumatized patients in the emergency unit.
创伤性脑损伤（TBI）是全球致残致死的主要原因，它可导致潜在的颅内出血（ICH），这是一种危及生命的状况，需要通过计算机断层扫描（CT）进行快速诊断。目前已有用于ICH检测的人工智能工具上市。：研究基于人工智能的CT出血检测工具qER.ai在创伤后人群中的实际性能。：对急诊放射科连续采集的头部CT扫描数据集进行回顾性单中心观察性研究，以探究脑外伤情况。将人工智能的性能与专家共识确定的真实情况进行比较。将一组由初级住院医师出具放射学报告的夜班病例与人工智能结果和真实情况进行比较。：共分析了682例头部CT扫描。人工智能总体上表现出88.8%的灵敏度和92.1%的特异度，阳性预测值为65.4%，阴性预测值为98%。人工智能在检测ICH方面的性能与初级住院医师相当，后者的灵敏度为85.7%，特异度高达99.3%。有趣的是，人工智能检测出了初级住院医师漏诊的三例ICH病例中的两例。当整合人工智能辅助时，综合灵敏度提高到95.2%，总体准确率达到98.8%。：这项研究表明，人工智能和放射科住院医师共同工作的表现优于各自单独工作。这些结果对于重新思考放射学工作流程以及急诊科大量脑外伤患者的分诊未来而言是令人鼓舞的。
