Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea.
Sci Rep. 2022 Jul 21;12(1):12454. doi: 10.1038/s41598-022-16313-0.
The study aims to measure the effectiveness of an AI-based traumatic intracranial hemorrhage prediction model in the decisions of emergency physicians regarding ordering head computed tomography (CT) scans. We developed a deep-learning model for predicting traumatic intracranial hemorrhages (DEEPTICH) using a national trauma registry with 1.8 million cases. For simulation, 24 cases were selected from previous emergency department cases. For each case, physicians made decisions on ordering a head CT twice: initially without the DEEPTICH assistance, and subsequently with the DEEPTICH assistance. Of the 528 responses from 22 participants, 201 initial decisions were different from the DEEPTICH recommendations. Of these 201 initial decisions, 94 were changed after DEEPTICH assistance (46.8%). For the cases in which CT was initially not ordered, 71.4% of the decisions were changed (p < 0.001), and for the cases in which CT was initially ordered, 37.2% (p < 0.001) of the decisions were changed after DEEPTICH assistance. When using DEEPTICH, 46 (11.6%) unnecessary CTs were avoided (p < 0.001) and 10 (11.4%) traumatic intracranial hemorrhages (ICHs) that would have been otherwise missed were found (p = 0.039). We found that emergency physicians were likely to accept AI based on how they perceived its safety.
本研究旨在衡量一种基于人工智能的创伤性颅内出血预测模型在急诊医师决定是否开具头部计算机断层扫描（CT）检查方面的有效性。我们使用一个包含 180 万例病例的全国性创伤登记处开发了一种用于预测创伤性颅内出血的深度学习模型（DEEPTICH）。为了模拟，我们从之前的急诊科病例中选择了 24 个病例。对于每个病例，医师两次就开具头部 CT 做出决定：最初没有 DEEPTICH 辅助，随后使用 DEEPTICH 辅助。在 22 名参与者的 528 次回复中，201 次初始决策与 DEEPTICH 建议不同。在这 201 次初始决策中，有 94 次在 DEEPTICH 辅助后发生了改变（46.8%）。对于最初未开具 CT 的病例，71.4%的决策发生了改变（p<0.001），对于最初开具 CT 的病例，在 DEEPTICH 辅助后，有 37.2%（p<0.001）的决策发生了改变。使用 DEEPTICH 时，避免了 46 次（11.6%）不必要的 CT 检查（p<0.001），并发现了 10 次（11.4%）原本可能遗漏的创伤性颅内出血（ICH）（p=0.039）。我们发现，急诊医师更倾向于接受基于其对人工智能安全性的看法。
