Trost Biana, Rodrigues Laetitia, Ong Caroline, Dezellus Alexandre, Goldberg Ythan H, Bouchat Marine, Roger Emilie, Moal Olivier, Singh Varinder, Moal Bertrand, Lafitte Stephane
Northwell, New Hyde Park, New York, USA; Department of Cardiology, Lenox Hill Hospital, New York, USA.
DESKi, Bordeaux, France.
JACC Adv. 2025 Jul 22;4(8):102005. doi: 10.1016/j.jacadv.2025.102005.
Cardiac ultrasound exams provide real-time data to guide clinical decisions but require highly trained sonographers. Artificial intelligence (AI) that uses deep learning algorithms to guide novices in the acquisition of diagnostic echocardiographic studies may broaden access and improve care.
The objective of this trial was to evaluate whether nurses without previous ultrasound experience (novices) could obtain diagnostic-quality acquisitions of 10 echocardiographic views using AI-based software.
This noninferiority study was prospective, international, nonrandomized, and conducted at 2 medical centers, in the United States and France, from November 2023 to August 2024. Two limited cardiac exams were performed on adult patients scheduled for a clinically indicated echocardiogram; one was conducted by a novice using AI guidance and one by an expert (experienced sonographer or cardiologist) without it. Primary endpoints were evaluated by 5 experienced cardiologists to assess whether the novice exam was of sufficient quality to visually analyze the left ventricular size and function, the right ventricle size, and the presence of nontrivial pericardial effusion. Secondary endpoints included 8 additional cardiac parameters.
A total of 240 patients (mean age 62.6 years; 117 women (48.8%); mean body mass index 26.6 kg/m) completed the study. One hundred percent of the exams performed by novices with the studied software were of sufficient quality to assess the primary endpoints. Cardiac parameters assessed in exams conducted by novices and experts were strongly correlated.
AI-based software provides a safe means for novices to perform diagnostic-quality cardiac ultrasounds after a short training period.
心脏超声检查可提供实时数据以指导临床决策，但需要训练有素的超声检查人员。利用深度学习算法指导新手进行诊断性超声心动图研究的人工智能（AI）可能会扩大检查的可及性并改善医疗服务。
本试验的目的是评估此前没有超声检查经验的护士（新手）能否使用基于AI的软件获得10个超声心动图视图的诊断质量图像。
这项非劣效性研究是前瞻性、国际性、非随机的，于2023年11月至2024年8月在美国和法国的2个医学中心进行。对计划进行临床指征超声心动图检查的成年患者进行了两项有限的心脏检查；一项由新手在AI指导下进行，另一项由没有AI指导的专家（经验丰富的超声检查人员或心脏病专家）进行。由5名经验丰富的心脏病专家评估主要终点，以评估新手检查的质量是否足以直观分析左心室大小和功能、右心室大小以及是否存在大量心包积液。次要终点包括另外8个心脏参数。
共有240名患者（平均年龄62.6岁；117名女性（48.8%）；平均体重指数26.6kg/m²）完成了研究。新手使用所研究软件进行的检查中有100%的质量足以评估主要终点。新手和专家进行的检查中评估的心脏参数高度相关。
基于AI的软件为新手在经过短时间培训后进行诊断质量的心脏超声检查提供了一种安全的方法。
