Lafitte Stéphane, Lafitte Louis, Jonveaux Melchior, Pascual Zoe, Ternacle Julien, Dijos Marina, Bonnet Guillaume, Reant Patricia, Bernard Anne
Bordeaux University, 33000 Bordeaux, France; Unité médicochirurgicale des valvulopathies, CHU de Bordeaux, 33000 Bordeaux, France.
Bordeaux University, 33000 Bordeaux, France.
Arch Cardiovasc Dis. 2025 Apr 24. doi: 10.1016/j.acvd.2025.04.051.
Echocardiography is an important diagnostic tool in cardiology as it is essential for heart disease treatment. However, its time-consuming nature and reliance on user expertise constitutes a challenge for its use in high-volume clinics. Artificial intelligence (AI) offers the potential to automate tasks performed manually by echocardiographers and promises to improve efficiency and diagnostic consistency.
To evaluate the integration of AI-based tools in a high-volume echocardiography department and assess the concordance of AI-generated measurements with manually-performed measurements.
The study was conducted in the echocardiography department of Bordeaux University Hospital. Over 2months, 894 echocardiograms were performed by operators with three experience levels (nurses, residents and experts), with measurements performed by AI and humans. The statistical analyses assessed measurement agreement between both.
The AI system was successfully integrated into the hospital's infrastructure within 6weeks. Concordance analysis revealed good to very good agreement between AI and human measurements for most parameters, especially for ejection fraction (intraclass correlation coefficient [ICC]: 0.81, 95% confidence interval [95% CI]: 0.78-0.85) and Doppler-based flow measurements (mitral E wave velocity: ICC 0.97, 95% CI 0.95-0.98). Bland-Altman analysis showed a global mean difference of -4% with a standard deviation of 15%. Subgroup analysis revealed higher concordance for experts and residents compared with nurses (mean ICCs: 0.78 and 0.79 vs. 0.72, respectively).
AI can be effectively integrated into clinical echocardiography practice, with high agreement between AI and human measurements. Further research is needed to investigate the long-term impact on clinical outcomes and efficiency.
超声心动图是心脏病学中一项重要的诊断工具,对心脏病治疗至关重要。然而,其耗时的特性以及对用户专业知识的依赖,对其在高流量诊所的应用构成了挑战。人工智能(AI)有望实现超声心动图检查人员手动执行任务的自动化,并有望提高效率和诊断一致性。
评估基于人工智能的工具在高流量超声心动图科室的整合情况,并评估人工智能生成的测量结果与手动测量结果的一致性。
该研究在波尔多大学医院的超声心动图科室进行。在两个月的时间里,由具有三个经验水平(护士、住院医师和专家)的操作人员进行了894次超声心动图检查,并由人工智能和人工进行测量。统计分析评估了两者之间的测量一致性。
人工智能系统在6周内成功集成到医院的基础设施中。一致性分析显示,对于大多数参数,人工智能和人工测量之间的一致性良好到非常好,尤其是射血分数(组内相关系数[ICC]:0.81,95%置信区间[95%CI]:0.78 - 0.85)和基于多普勒的血流测量(二尖瓣E波速度:ICC 0.97,95%CI 0.95 - 0.98)。布兰德 - 奥特曼分析显示总体平均差异为 - 4%,标准差为15%。亚组分析显示,与护士相比,专家和住院医师的一致性更高(平均ICC分别为0.78和0.79,而护士为0.72)。
人工智能可以有效地整合到临床超声心动图实践中,人工智能和人工测量之间具有高度一致性。需要进一步研究以调查其对临床结果和效率的长期影响。