将人工智能整合到超声心动图部门：在大容量临床环境中自动与人工测量的可行性和比较研究。

IF 2.2 3区医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS

Archives of Cardiovascular Diseases Pub Date : 2025-09-01 DOI:10.1016/j.acvd.2025.04.051

Stéphane Lafitte , Louis Lafitte , Melchior Jonveaux , Zoe Pascual , Julien Ternacle , Marina Dijos , Guillaume Bonnet , Patricia Reant , Anne Bernard

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引用次数: 0

摘要

背景：超声心动图是一种重要的心脏病诊断工具，对心脏病的治疗至关重要。然而，其耗时的性质和对用户专业知识的依赖构成了其在大批量诊所使用的挑战。人工智能（AI）提供了将超声心动图师手动执行的任务自动化的潜力，并有望提高效率和诊断的一致性。目的：评估基于人工智能的工具在大容量超声心动图科室的整合，并评估人工智能生成的测量与人工测量的一致性。方法：本研究在波尔多大学医院超声心动图科进行。在2个月的时间里，由三个经验级别的操作员（护士、住院医师和专家）完成894张超声心动图，由人工智能和人类进行测量。统计分析评估了两者的测量一致性。结果：人工智能系统在6周内成功融入医院基础设施。一致性分析显示，人工智能与人类测量的大多数参数之间具有良好到非常好的一致性，特别是射血分数（类内相关系数[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）。Bland-Altman分析显示，全球平均差异为-4%，标准差为15%。亚组分析显示，与护士相比，专家和住院医生的一致性更高（平均icc分别为0.78和0.79对0.72）。结论：人工智能可以有效地融入临床超声心动图实践，人工智能与人体测量结果一致性高。需要进一步的研究来调查对临床结果和效率的长期影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Integrating artificial intelligence into an echocardiography department: Feasibility and comparative study of automated versus human measurements in a high-volume clinical setting

查看原文本刊更多论文

Integrating artificial intelligence into an echocardiography department: Feasibility and comparative study of automated versus human measurements in a high-volume clinical setting

Background

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.

Aims

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.

Methods

The study was conducted in the echocardiography department of Bordeaux University Hospital. Over 2 months, 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.

Results

The AI system was successfully integrated into the hospital's infrastructure within 6 weeks. 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).

Conclusion

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.

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来源期刊

Archives of Cardiovascular Diseases 医学-心血管系统

CiteScore

4.40

自引率

6.70%

发文量

审稿时长

34 days

期刊介绍： The Journal publishes original peer-reviewed clinical and research articles, epidemiological studies, new methodological clinical approaches, review articles and editorials. Topics covered include coronary artery and valve diseases, interventional and pediatric cardiology, cardiovascular surgery, cardiomyopathy and heart failure, arrhythmias and stimulation, cardiovascular imaging, vascular medicine and hypertension, epidemiology and risk factors, and large multicenter studies. Archives of Cardiovascular Diseases also publishes abstracts of papers presented at the annual sessions of the Journées Européennes de la Société Française de Cardiologie and the guidelines edited by the French Society of Cardiology.