Gallant Cassandra, Bernard Lori, Kwok Cherise, Wichuk Stephanie, Noga Michelle, Punithakumar Kumaradevan, Hareendranathan Abhilash, Becher Harald, Buchanan Brian, Jaremko Jacob L
Department of Radiology and Diagnostic Imaging, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB T6G 2R3, Canada.
Department of Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB T6G 2R3, Canada.
J Clin Med. 2025 Apr 23;14(9):2899. doi: 10.3390/jcm14092899.
Echocardiography is crucial to understanding cardiac function in the Intensive Care Unit (ICU), often by measuring the left ventricular ejection fraction (LVEF). Traditionally, measures of LVEF are completed as part of a comprehensive examination by an expert sonographer or cardiologist, but front-line practitioners increasingly perform focused point-of-care estimates of LVEF while managing life-threatening illness. The two main echocardiographic windows used to grossly estimate LVEF are parasternal and apical windows. Artificial intelligence (AI) algorithms have recently been developed to assist non-experts in obtaining and interpreting point-of-care ultrasound (POCUS) echo images. We tested the feasibility, accuracy and reliability of novice users estimating LVEF using POCUS-AI echo. A total of 30 novice users (most never holding an ultrasound probe before) received 2 h of instruction, then scanned ICU patients (10 patients, 80 scans) using the Exo Iris POCUS probe with AI guidance tool. They were permitted up to 5 min to attempt parasternal long axis (PLAX) and apical 4 chamber (A4C) views. AI-reported LVEF results from these scans were compared to gold-standard LVEF obtained by an expert echo sonographer. To further assess accuracy, this sonographer also scanned another 65 patients using Exo Iris POCUS-AI vs. conventional protocol. Novices obtained images sufficient to estimate LVEF in 96% of patients in <5 min. Novices obtained PLAX views significantly faster than A4C (1.5 min vs. 2.3 min). Inter-rater reliability of LVEF estimation was very high (ICC 0.88-0.94) whether images were obtained by novices or experts. In n = 65 patients, POCUS-AI LVEF was highly specific for a decreased LVEF ≤ 40% (SP = 90% for PLAX) but only moderately sensitive (SN = 56-70%). : Estimating cardiac LVEF from AI-enhanced POCUS is highly feasible even for novices in ICU settings, particularly using the PLAX view. POCUS-AI LVEF results were highly consistent whether performed by novice or expert. When AI detected a decreased LVEF, it was highly accurate, although a normal LVEF reported by POCUS-AI was not necessarily reassuring. This POCUS-AI tool could be clinically useful to rapidly confirm a suspected low LVEF in an ICU patient. Further improvements to sensitivity for low LVEF are needed.
超声心动图对于了解重症监护病房（ICU）中的心脏功能至关重要，通常通过测量左心室射血分数（LVEF）来实现。传统上，LVEF的测量是由专业超声检查医师或心脏病专家在全面检查中完成的一部分，但一线从业者在处理危及生命的疾病时越来越多地进行LVEF的即时床旁评估。用于大致估计LVEF的两个主要超声心动图窗口是胸骨旁窗口和心尖窗口。最近开发了人工智能（AI）算法来协助非专家获取和解读即时床旁超声（POCUS）回声图像。我们测试了新手使用POCUS-AI回声估计LVEF的可行性、准确性和可靠性。共有30名新手用户（大多数以前从未手持过超声探头）接受了2小时的培训，然后使用带有AI引导工具的Exo Iris POCUS探头对ICU患者（10名患者，80次扫描）进行扫描。他们被允许最多5分钟尝试获取胸骨旁长轴（PLAX）和心尖四腔（A4C）视图。将这些扫描中AI报告的LVEF结果与专业超声检查医师获得的金标准LVEF进行比较。为了进一步评估准确性，该超声检查医师还使用Exo Iris POCUS-AI与传统方案对另外65名患者进行了扫描。新手在不到5分钟的时间内为96%的患者获取了足以估计LVEF的图像。新手获取PLAX视图的速度明显快于A4C视图（1.5分钟对2.3分钟）。无论图像是由新手还是专家获取的，LVEF估计的评分者间可靠性都非常高（ICC为0.88 - 0.94）。在n = 65名患者中，POCUS-AI LVEF对于LVEF降低≤40%具有高度特异性（PLAX视图的SP = 90%），但敏感性仅为中等（SN = 56 - 70%）。即使在ICU环境中，对于新手来说，通过AI增强的POCUS估计心脏LVEF也是高度可行的，特别是使用PLAX视图。无论由新手还是专家进行，POCUS-AI LVEF结果都高度一致。当AI检测到LVEF降低时，其准确性很高，尽管POCUS-AI报告的正常LVEF不一定令人放心。这种POCUS-AI工具在临床上可能有助于快速确认ICU患者疑似的低LVEF。需要进一步提高对低LVEF的敏感性。
