Design of Screening Cardiometabolic Opportunities Using Transformative Echocardiography Artificial Intelligence (SCOUT-Echo-AI).

IF 3.4 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Sahir Eusuff, Jay Kim, Zara Fatima, Dilek Yalcinkaya, Yuki Sahashi, Alexander Sandhu, Long H Nguyen, Andrew P Ambrosy, Paul P Cheng, Stephanie T Chang, Alan S Go, Faraz S Ahmad, Amanda Cheung, Susan Cheng, Alan C Kwan, David Ouyang
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引用次数: 0

Abstract

Background: Steatotic liver disease (SLD) is highly prevalent among individuals with cardiovascular disease, but often remains undiagnosed until advanced sequelae such as fibrotic liver disease and hepatocellular carcinoma develop. ​​Transthoracic echocardiography routinely captures incidental subcostal views of the liver that are not used clinically. The EchoNet-Liver algorithm uses artificial intelligence (AI) to predict the presence of liver disease based on routine clinical echocardiograms. Prospective evaluation in real-world practice is needed to understand the clinical utility and impact of AI-enabled opportunistic liver disease screening.

Methods: The SCOUT-Echo-AI study is a single-arm, prospective, multicenter, open-label pragmatic interventional trial that will be conducted across six diverse U.S. healthcare systems (Kaiser Permanente Northern California, Stanford Healthcare, Cedars-Sinai Medical Center, University of California Los Angeles, Northwestern Medicine, and Massachusetts General Brigham). Routine echocardiograms will be analyzed by the EchoNet-Liver AI algorithm with an estimated enrollment of ∼3,000 patients. Treating clinicians or participants themselves will be notified of potential undiagnosed liver disease with site-specific workflows in place to ensure routine clinical care for definitive case confirmation. Primary outcomes will be SLD and fibrotic liver disease detection rates and positive predictive values within 12 months of AI identification. Pre-specified secondary outcomes will assess diagnostic accuracy against other gold-standard modalities, treatment initiation, time to diagnosis, and impact on other clinical events. Outcomes will be compared against historical usual-care and contemporary below-threshold cohorts.

Conclusion: SCOUT-Echo-AI is a prospective multicenter study evaluating the real-world impact of AI-enabled opportunistic liver disease screening using routine echocardiography.

利用变革性超声心动图人工智能(SCOUT-Echo-AI)筛选心脏代谢机会的设计。
背景:脂肪变性肝病(SLD)在心血管疾病患者中非常普遍,但通常直到晚期后遗症(如纤维化肝病和肝细胞癌)发生时才被诊断出来。经胸超声心动图常规捕获未在临床上使用的偶然肋下肝脏图像。EchoNet-Liver算法利用人工智能(AI)根据常规临床超声心动图预测肝脏疾病的存在。需要在现实世界的实践中进行前瞻性评估,以了解人工智能支持的机会性肝病筛查的临床效用和影响。方法:SCOUT-Echo-AI研究是一项单臂、前瞻性、多中心、开放标签的实用介入试验,将在六个不同的美国医疗保健系统(北加州Kaiser Permanente、斯坦福医疗保健、雪松-西奈医学中心、加州大学洛杉矶分校、西北医学院和马萨诸塞州布里格姆总医院)进行。常规超声心动图将通过EchoNet-Liver AI算法进行分析,预计将纳入约3000名患者。治疗临床医生或参与者本人将被告知潜在的未确诊的肝脏疾病,并制定特定地点的工作流程,以确保常规临床护理以确定病例确认。主要结果将是人工智能识别后12个月内SLD和纤维化性肝病的检出率和阳性预测值。预先指定的次要结局将评估与其他金标准模式相比的诊断准确性、治疗开始、诊断时间和其他临床事件的影响。结果将与历史常规护理和当代低于阈值的队列进行比较。结论:SCOUT-Echo-AI是一项前瞻性多中心研究,评估使用常规超声心动图进行ai支持的机会性肝病筛查的现实影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
American heart journal
American heart journal 医学-心血管系统
CiteScore
8.20
自引率
2.10%
发文量
214
审稿时长
38 days
期刊介绍: The American Heart Journal will consider for publication suitable articles on topics pertaining to the broad discipline of cardiovascular disease. Our goal is to provide the reader primary investigation, scholarly review, and opinion concerning the practice of cardiovascular medicine. We especially encourage submission of 3 types of reports that are not frequently seen in cardiovascular journals: negative clinical studies, reports on study designs, and studies involving the organization of medical care. The Journal does not accept individual case reports or original articles involving bench laboratory or animal research.
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