BMC Cardiovascular Disorders (Nov 2024)

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

  • Mahboobeh Sheikh,
  • Sahar Asl Fallah,
  • Muhammadhosein Moradi,
  • Arash Jalali,
  • Ahmad Vakili-Basir,
  • Mohammad Sahebjam,
  • Haleh Ashraf,
  • Arezou Zoroufian

DOI
https://doi.org/10.1186/s12872-024-04355-3
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 11

Abstract

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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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