重要性  人工智能（AI）近年来已应用于医学成像分析，但是AI指导超声图像的采集是一个新颖的研究领域。一种新颖的深度学习（DL）算法，已针对超过500万个超声探头运动的图像质量结果示例进行了训练，可以为新手操作者提供实时的处方指导，以获取有限的经胸超声心动图诊断图像。

目的  为了测试新手用户是否可以使用此基于DL的软件获得10幅经胸超声心动图诊断质量的信息。

设计，场所和参加者  这项前瞻性，多中心诊断研究是在2所学术医院中进行的。招募了8名先前未进行过超声心动图检查的护士，并对其进行了AI培训。每位护士对30名至少18岁的患者进行了扫描，这些患者计划于2019年3月至5月之间在西北纪念医院或明尼阿波利斯心脏研究所接受临床指示的超声心动图检查。这些扫描结果与使用相同超声心动图硬件但未使用AI指导的超声检查者进行了比较。 

干预措施  每位患者均接受成对的有限超声心动图检查：一名来自没有使用DL算法的超声心动图检查经验的护士，另一位来自没有DL算法的超声医师。五名受过3级训练的超声心动图医师独立且盲地评估了每次采集。

主要结果和措施  依次评估了四个主要终点：关于左心室大小和功能，右心室大小以及是否存在心包积液的定性判断。次要终点包括6个其他临床参数以及护士与超声医师的扫描结果比较。

结果  共有240名患者（平均[SD]年龄，61 [16]岁；139名男性[57.9％]；79 [32.9％]，体重指数> 30）完成了研究。八名护士各自使用DL算法扫描30例患者，对240例中的237例（98.8％）的左心室大小，功能和心包积液具有诊断质量，而在240例中的222例（222.5％）的患者具有右心室大小的诊断质量。对于次要终点，护士超声检查的大多数参数无显著差异。

结论和相关性  这种DL算法允许没有超声检查经验的新手获得经胸超声心动图诊断学研究，以评估左心室大小和功能，右心室大小以及是否存在不重要的心包积液，从而将超声心动图的应用范围扩大到可立即进行对解剖结构和心脏功能进行了解，并且使用有限资源的场所。

 

 

Importance  Artificial intelligence (AI) has been applied to analysis of medical imaging in recent years, but AI to guide the acquisition of ultrasonography images is a novel area of investigation. A novel deep-learning (DL) algorithm, trained on more than 5 million examples of the outcome of ultrasonographic probe movement on image quality, can provide real-time prescriptive guidance for novice operators to obtain limited diagnostic transthoracic echocardiographic images.

Objective  To test whether novice users could obtain 10-view transthoracic echocardiographic studies of diagnostic quality using this DL-based software.

Design, Setting, and Participants  This prospective, multicenter diagnostic study was conducted in 2 academic hospitals. A cohort of 8 nurses who had not previously conducted echocardiograms was recruited and trained with AI. Each nurse scanned 30 patients aged at least 18 years who were scheduled to undergo a clinically indicated echocardiogram at Northwestern Memorial Hospital or Minneapolis Heart Institute between March and May 2019. These scans were compared with those of sonographers using the same echocardiographic hardware but without AI guidance.

Interventions  Each patient underwent paired limited echocardiograms: one from a nurse without prior echocardiography experience using the DL algorithm and the other from a sonographer without the DL algorithm. Five level 3–trained echocardiographers independently and blindly evaluated each acquisition.

Main Outcomes and Measures  Four primary end points were sequentially assessed: qualitative judgement about left ventricular size and function, right ventricular size, and the presence of a pericardial effusion. Secondary end points included 6 other clinical parameters and comparison of scans by nurses vs sonographers.

Results  A total of 240 patients (mean [SD] age, 61 [16] years old; 139 men [57.9%]; 79 [32.9%] with body mass indexes >30) completed the study. Eight nurses each scanned 30 patients using the DL algorithm, producing studies judged to be of diagnostic quality for left ventricular size, function, and pericardial effusion in 237 of 240 cases (98.8%) and right ventricular size in 222 of 240 cases (92.5%). For the secondary end points, nurse and sonographer scans were not significantly different for most parameters.

Conclusions and Relevance  This DL algorithm allows novices without experience in ultrasonography to obtain diagnostic transthoracic echocardiographic studies for evaluation of left ventricular size and function, right ventricular size, and presence of a nontrivial pericardial effusion, expanding the reach of echocardiography to clinical settings in which immediate interrogation of anatomy and cardiac function is needed and settings with limited resources.

原文链接：

https://jamanetwork.com/journals/jamacardiology/fullarticle/2776714

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