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Updated: Jan 31, 2026

Investigating the Pathogenesis of MYH7 Mutation Gly823Glu in Familial Hypertrophic Cardiomyopathy using a Mouse Model
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Assessment of HeartModel® automated left ventricular ejection fraction for patients with hypertrophic cardiomyopathy

John Morrissey1, Libin Wang2, Monica Dehn2

  • 1Department of Medicine, Tufts Medical Center, Boston, MA, USA.

European Heart Journal. Imaging Methods and Practice
|January 30, 2026
PubMed

Insights

Machine learning-based left ventricular ejection fraction (LVEF) assessments in hypertrophic cardiomyopathy (HCM) patients differ significantly from standard methods. This raises concerns for using automated LVEF software in guiding cardiac myosin inhibitor treatments.

Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Cardiac myosin inhibitors (CMIs) offer new treatment options for obstructive hypertrophic cardiomyopathy (HCM).
  • CMIs carry a risk of systolic dysfunction, necessitating accurate monitoring of left ventricular ejection fraction (LVEF).
  • Machine learning (ML) algorithms show potential for frequent and accurate LVEF assessments.

Purpose of the Study:

  • To evaluate the performance of a commercial ML-based LVEF model in patients with obstructive HCM.
  • To compare automated LVEF measurements with standard echocardiography and cardiac magnetic resonance imaging (cMRI).

Main Methods:

  • A single-center prospective study included 50 patients with HCM.
  • Left ventricular function was assessed using Philips HeartModel® (automated), echocardiographer assessment (standard), and cMRI.
  • Measurements included LVEF, end-diastolic volume (EDV), and end-systolic volume (ESV).

Main Results:

  • Automated LVEF was significantly lower than standard assessment (median difference -8%) and cMRI (median difference -12%).
  • Automated assessment yielded larger EDV and ESV compared to standard 2D tracings (P<0.001 for both).
  • The automated method identified more patients (22%) with LVEF < 50% compared to expert assessment (12%).

Conclusions:

  • Automated LV size and function assessments in HCM patients significantly differ from standard methods.
  • Concerns exist regarding the use of ML-enabled LVEF software for this population.
  • Further validation is needed before applying this technology to guide CMI treatments.
Abstract

Keywords:
cardiac myosin inhibitorshypertrophic cardiomyopathymachine learningtransthoracic echocardiography

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