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Automated Deep Learning Based Cardiac Quantification in Hypertrophic Cardiomyopathy: A Comparative Study with Manual

Shivam Angiras1, Deb Kumar Boruah1, Pranjal Phukan1

  • 1Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences Guwahati, Assam, India.

Acta Medica Lituanica
|February 9, 2026
PubMed

Insights

Deep learning software accurately quantifies cardiac function and mitral regurgitation in Hypertrophic Cardiomyopathy (HCM) patients, matching manual assessments. This automated approach streamlines workflow but requires validation in complex cases.

Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Hypertrophic Cardiomyopathy (HCM) is a common inherited heart condition.
  • Accurate assessment of Left Ventricular (LV) function and Mitral Regurgitation (MR) is vital in HCM.
  • Cardiac Magnetic Resonance (CMR) is the gold standard, but manual analysis is time-consuming.

Purpose of the Study:

  • To compare a deep learning (DL) software (SuiteHEART) against manual segmentation (syngo.Via) for cardiac parameter quantification in HCM patients.
  • To evaluate the accuracy and efficiency of automated DL-based cardiac segmentation.

Main Methods:

  • Prospective study of 25 adult HCM patients undergoing CMR.
  • Quantification of LVEF, LVEDV, LVSV, AoF, MR, and PG using both manual and automated DL segmentation.
  • Statistical analysis included correlation and Bland-Altman analysis.

Main Results:

  • Strong correlations found between DL and manual measurements for all assessed parameters (r=0.81-0.91, p<0.001).
  • Bland-Altman analysis showed acceptable agreement with no significant bias.
  • Automated segmentation significantly reduced post-processing time (p<0.001).

Conclusions:

  • Fully automated DL-based quantification accurately assesses LV function, MR, and flow in HCM patients.
  • DL algorithms can streamline clinical workflows for cardiac imaging analysis.
  • Further validation is necessary for complex HCM cases.
Abstract

Keywords:
automated segmentationcardiac MRIdeep learninghypertrophic cardiomyopathyleft ventricular functionmitral regurgitation

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