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Heart failure & cardiomyopathiesResearch article

Detection of Left Ventricular Outflow Obstruction From Standard B-Mode Echocardiogram Videos Using Deep Learning

JACC AdvPublication date not resolved

  • development of a deep learning model
  • matched by age, sex, and septal thickness
  • held-out test sets from three centres
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Authors' conclusion

In this study, we developed an artificial intelligence model to detect LVOT obstruction from standard apical 4-chamber videos, highlighting patients who may benefit from more detailed cardiac workup for obstructive HCM.

Study context

Population
Patients with left ventricular outflow tract (LVOT) obstruction and control patients matched by age, sex, and septal thickness, with non-Doppler apical 4-chamber B-mode echocardiographic videos2,396 patients with LVOT obstruction and 6,177 control patients · patients with LVOT obstruction 2,396 · control patients 6,177 · Cedars-Sinai Medical Center test set (videos) 3,848
Participants
2,396 vs 6,177
Comparison
Deep learning model on non-Doppler apical 4-chamber B-mode echocardiographic videos against outflow obstruction identified later in the same study by spectral Doppler
  • LVOT obstructionn = 2,396
  • control patientsn = 6,177

Main finding

As reported in the abstract

Detection of LVOT obstruction, Cedars-Sinai Medical Center test set

AUC

0.858 (95% CI 0.847-0.870)

Further findings

Detection of LVOT obstruction, Kaiser Permanente Northern California cohort

AUC
0.817 (95% CI 0.740-0.922)

Detection of LVOT obstruction, Stanford Healthcare

AUC
0.836 (95% CI 0.807-0.827)
  • CI as printed in the abstract

Performance across patient subgroups

Performance was consistent across patient subgroups, including those with hyperdynamic left ventricular function, pre-existing valvular disease, and small left ventricular cavity size

Original publication

Detection of Left Ventricular Outflow Obstruction From Standard B-Mode Echocardiogram Videos Using Deep Learning. JACC Adv. doi:10.1016/j.jacadv.2026.102801
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Summarised from the published abstract; every number is checked against it