Comparing HeartModelAI and cardiac magnetic resonance imaging for left ventricular volume and function evaluation in patients with dilated cardiomyopathy.

IF 2 3区 医学 Q3 CARDIAC & CARDIOVASCULAR SYSTEMS BMC Cardiovascular Disorders Pub Date : 2024-11-23 DOI:10.1186/s12872-024-04355-3
Mahboobeh Sheikh, Sahar Asl Fallah, Muhammadhosein Moradi, Arash Jalali, Ahmad Vakili-Basir, Mohammad Sahebjam, Haleh Ashraf, Arezou Zoroufian
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Abstract

Background: Integration of artificial intelligence enhances precision, yielding dependable evaluations of left ventricular volumes and ejection fraction despite image quality variations. Commercial software like HeartModelAI provides fully automated 3DE quantification, simplifying the measurement of left chamber volumes and ejection fraction. In this manuscript, we present a cross-sectional study to assess and compare the diagnostic accuracy of automated 3D echocardiography (HeartModelAI) to the standard Cardiac Magnetic Resonance Imaging in patients with dilated cardiomyopathy.

Methods: In this cross-sectional study, 30 patients with dilated cardiomyopathy referring to the Tehran Heart Center with cardiac magnetic resonance imaging and comprehensive 3D transthoracic echocardiography within 24 h were included. All 3D volume analysis was performed with fully automated quantification software (HeartModelAI) using 3D images of 2,3, and 4-chamber views at the end of systole and diastole.

Results: Excellent Inter- and Intra-observer correlation coefficient was reported for HeartModelAI software for all indexes. HeartModelAI displayed a remarkable correlation with cardiac magnetic resonance for left ventricular end-systolic volume index (r = 0.918 and r = 0.911); nevertheless, it underestimated left ventricular end-systolic volume index and left ventricular end-diastolic volume index. Conversely, ejection fraction, stroke volume, and left ventricular mass were overestimated. It was found that manual contour correction can enhance the accuracy of automated model estimations, particularly concerning EF in participants needing correction.

Conclusion: HeartModelAI software emerges as a rapid and viable imaging approach for evaluating the left ventricle's structure and function. In our study, LV volumes assessed by HeartModelAI demonstrated strong correlations with cardiac magnetic resonance imaging.

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比较 HeartModelAI 和心脏磁共振成像对扩张型心肌病患者左心室容量和功能的评估。
背景:整合人工智能可提高精确度,在图像质量存在差异的情况下仍能可靠地评估左心室容积和射血分数。HeartModelAI等商业软件提供全自动3DE量化,简化了左室容积和射血分数的测量。在本手稿中,我们介绍了一项横断面研究,以评估和比较自动三维超声心动图(HeartModelAI)与标准心脏磁共振成像对扩张型心肌病患者的诊断准确性:在这项横断面研究中,纳入了 30 名德黑兰心脏中心转诊的扩张型心肌病患者,他们在 24 小时内接受了心脏磁共振成像和全面的三维经胸超声心动图检查。所有三维容积分析均采用全自动量化软件(HeartModelAI),使用收缩末期和舒张末期二、三、四腔切面的三维图像:结果:HeartModelAI 软件在所有指标上的观察者间和观察者内相关系数都很高。在左心室收缩末期容积指数方面,HeartModelAI 与心脏磁共振显示出显著的相关性(r = 0.918 和 r = 0.911);然而,它低估了左心室收缩末期容积指数和左心室舒张末期容积指数。相反,射血分数、每搏容量和左心室质量被高估了。研究发现,手动轮廓校正可以提高自动模型估算的准确性,尤其是需要校正的参与者的射血分数:HeartModelAI软件是评估左心室结构和功能的一种快速可行的成像方法。在我们的研究中,HeartModelAI 评估的左心室容积与心脏磁共振成像显示出很强的相关性。
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来源期刊
BMC Cardiovascular Disorders
BMC Cardiovascular Disorders CARDIAC & CARDIOVASCULAR SYSTEMS-
CiteScore
3.50
自引率
0.00%
发文量
480
审稿时长
1 months
期刊介绍: BMC Cardiovascular Disorders is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of disorders of the heart and circulatory system, as well as related molecular and cell biology, genetics, pathophysiology, epidemiology, and controlled trials.
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