对非对比头部 CT 检查中颅内出血的人工智能分诊进行前瞻性评估。

IF 4.7 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING American Journal of Roentgenology Pub Date : 2024-09-04 DOI:10.2214/AJR.24.31639
Cody H Savage, Manoj Tanwar, Asser Abou Elkassem, Adam Sturdivant, Omar Hamki, Houman Sotoudeh, Gopi Sirineni, Aparna Singhal, Desmin Milner, Jesse Jones, Dirk Rehder, Mei Li, Yufeng Li, Kevin Junck, Srini Tridandapani, Steven A Rothenberg, Andrew D Smith
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引用次数: 0

摘要

背景:评估人工智能(AI)算法在非对比 CT(NCCT)上检测颅内出血(ICH)的回顾性研究显示了良好的效果,但缺乏前瞻性验证。目的评估放射科在头部 NCCT 检查中采用人工智能分流和通知系统进行 ICH 检测对放射科医生实际 ICH 检测总成绩和 ICH 阳性检查报告周转时间的影响。方法:这项前瞻性单中心研究纳入了 2021 年 5 月 12 日至 2021 年 6 月 30 日(第一阶段)或 2021 年 9 月 30 日至 2021 年 12 月 4 日(第二阶段)接受头部 NCCT 检查的成人患者。在第一阶段之前,放射科采用了商用人工智能分流系统进行 ICH 检测,该系统可处理头部 NCCT 检查,并通过浮动弹出式显示的 widget 将阳性结果通知放射科医生。检查结果由神经放射科医生或急诊放射科医生判读,他们分别在第一阶段和第二阶段对没有人工智能辅助和有人工智能辅助的检查结果进行评估。放射科专家小组对放射报告与人工智能不一致的所有检查和其余检查的子集进行了审查,以确定参考标准。诊断性能和报告周转时间的比较分别采用了皮尔逊卡方检验(Pearson chi-square test)和威尔科克森秩和检验(Wilcoxon rank-sum test)。对五个诊断性能指标进行了 Bonferroni 校正(调整后的显著性阈值为 0.01 [α=.05/5])。结果:共纳入了 7371 名患者的 9954 次检查(平均年龄为 54.8±19.8 岁;女性 3773 人,男性 3598 人)。在第一和第二阶段,分别有 19.8%(735/3716)和 21.9%(1368/6238)的检查结果呈 ICH 阳性(P=0.01)。无 AI 的放射科医生与有 AI 的放射科医生在准确性(99.5% vs 99.2%)、敏感性(98.6% vs 98.9%)、PPV(99.0% vs 99.7%)或 NPV(99.7% vs 99.7%)方面无显著差异(均 P>.01);无 AI 的放射科医生的特异性高于有 AI 的放射科医生(分别为 99.8% vs 99.3%,P=.004)。ICH 阳性检查的平均报告周转时间为:未使用 AI 的 147.1 分钟对使用 AI 的 149.9 分钟(P=.11)。结论:用于 ICH 检测的人工智能分流系统并未提高放射科医生的诊断性能或报告周转时间。临床影响:这项大型前瞻性真实世界研究不支持使用人工智能辅助进行 ICH 检测。
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Prospective Evaluation of Artificial Intelligence Triage of Intracranial Hemorrhage on Noncontrast Head CT Examinations.

Background: Retrospective studies evaluating artificial intelligence (AI) algorithms for intracranial hemorrhage (ICH) detection on noncontrast CT (NCCT) have shown promising results but lack prospective validation. Objective: To evaluate the impact on radiologists' real-world aggregate performance for ICH detection and report turnaround times for ICH-positive examinations of a radiology department's implementation of an AI triage and notification system for ICH detection on head NCCT examinations. Methods: This prospective single-center study included adult patients who underwent head NCCT examinations from May 12, 2021 to June 30, 2021 (phase 1) or September 30, 2021 to December 4, 2021 (phase 2). Before phase 1, the radiology department implemented a commercial AI triage system for ICH detection that processed head NCCT examinations and notified radiologists of positive results through a widget with a floating pop-up display. Examinations were interpreted by neuroradiologists or emergency radiologists, who evaluated examinations without and with AI assistance in phase 1 and phase 2, respectively. A panel of radiologists conducted a review process for all examinations with discordance between the radiology report and AI and a subset of remaining examinations, to establish the reference standard. Diagnostic performance and report turnaround times were compared using Pearson chi-square test and Wilcoxon rank-sum test, respectively. Bonferroni correction was used to account for five diagnostic performance metrics (adjusted significance threshold, .01 [α=.05/5]). Results: A total of 9954 examinations from 7371 patients (mean age, 54.8±19.8 years; 3773 female, 3598 male) were included. In phases 1 and 2, 19.8% (735/3716) and 21.9% (1368/6238) of examinations, respectively, were positive for ICH (P=.01). Radiologists without versus with AI showed no significant difference in accuracy (99.5% vs 99.2%), sensitivity (98.6% vs 98.9%), PPV (99.0% vs 99.7%), or NPV (99.7% vs 99.7%) (all P>.01); specificity was higher for radiologists without than with AI (99.8% vs 99.3%, respectively, P=.004). Mean report turnaround time for ICH-positive examinations was 147.1 minutes without AI versus 149.9 minutes with AI (P=.11). Conclusion: An AI triage system for ICH detection did not improve radiologists' diagnostic performance or report turnaround times. Clinical Impact: This large prospective real-world study does not support use of AI assistance for ICH detection.

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来源期刊
CiteScore
12.80
自引率
4.00%
发文量
920
审稿时长
3 months
期刊介绍: Founded in 1907, the monthly American Journal of Roentgenology (AJR) is the world’s longest continuously published general radiology journal. AJR is recognized as among the specialty’s leading peer-reviewed journals and has a worldwide circulation of close to 25,000. The journal publishes clinically-oriented articles across all radiology subspecialties, seeking relevance to radiologists’ daily practice. The journal publishes hundreds of articles annually with a diverse range of formats, including original research, reviews, clinical perspectives, editorials, and other short reports. The journal engages its audience through a spectrum of social media and digital communication activities.
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