Detection and prognostic stratification of left ventricular systolic dysfunction in left bundle branch block using an artificial intelligence–enabled electrocardiography

Q: What is the clinical evidence from this study?

Study design: Observational. Population: Adult patients (aged ≥18) with left bundle branch block (LBBB) and paired echocardiogram within 14 days (n=2813). Intervention: AiTiALVSD AI-enabled ECG model vs. No AI-ECG assessment (standard care). Primary outcome: Presence of left ventricular systolic dysfunction (LVSD) defined as echocardiographic LVEF ≤40% (AUROC 0.930, 95% CI 0.924–0.937).

Q: What does this research mean for the field?

The AiTiALVSD AI-enabled ECG model accurately detects left ventricular systolic dysfunction with an AUROC of 0.930 and stratifies long-term cardiovascular risk in patients with left bundle branch block. Novelty: ClaimNovelty.CONFIRMATORY. Consensus alignment: ConsensusAlignment.NEUTRAL.

Soo Youn Lee; Ah-Hyun Yoo; Sora Kang; Jong-Hwan Jang; Yong-Yeon Jo; Jeong Min Son; Min Sung Lee; Ga In Han; Joon-myoung Kwon; Hak Seung Lee; Kyung-Hee Kim

doi:10.1186/s44348-026-00066-9

February 16, 2026Journal of Cardiovascular ImagingOpen Access

Detection and prognostic stratification of left ventricular systolic dysfunction in left bundle branch block using an artificial intelligence–enabled electrocardiography

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Key result

AiTiALVSD AI-ECG model detects LV systolic dysfunction with ~98% sensitivity in LBBB patients.

AUROC 0.930
95% CI 0.924–0.937
n=2,813

Figures

Why the study?

Although AI-ECG models show promise in detecting LVSD, their performance in patients with LBBB remains underexplored.

Does the AiTiALVSD AI-ECG model accurately detect LVSD and predict future clinical outcomes in patients with left bundle branch block?

Key Points

To determine whether an artificial intelligence–enabled electrocardiography model can accurately detect left ventricular systolic dysfunction and predict clinical outcomes in patients with left bundle branch block.
Analyzed 5,689 expert-validated electrocardiograms from 2,813 patients with left bundle branch block collected across multiple centers between 2016 and 2024.
Defined left ventricular systolic dysfunction as an echocardiographic ejection fraction of ≤ 40% and evaluated diagnostic accuracy using AUROC, AUPRC, sensitivity, and specificity.
Stratified patients into high- and low-risk cohorts using a model score threshold that achieved 90% sensitivity, following them over a mean duration of 4.1 years.
The AiTiALVSD model detected left ventricular systolic dysfunction with an AUROC of 0.930 (95% CI, 0.924–0.937), AUPRC of 0.913 (95% CI, 0.902–0.923), sensitivity of 0.979 (95% CI, 0.974–0.985), and specificity of 0.473 (95% CI, 0.455–0.490).
High-risk patients demonstrated significantly increased risk of all-cause mortality (adjusted HR, 1.87; 95% CI, 1.53–2.28) and ICD/CRT implantation (adjusted HR, 15.2; 95% CI, 7.51–30.77).
High-risk classification was associated with incident ejection fraction reduction to ≤ 40% (adjusted HR, 12.05; 95% CI, 7.60–19.10) and cardiovascular hospitalization (adjusted HR, 1.11; 95% CI, 0.96–1.28).

Study Design

Type

Observational (n=2,813)

Multicenter

Yes

Structured PICO

Does the AiTiALVSD AI-ECG model accurately detect LVSD and predict future clinical outcomes in patients with left bundle branch block?

P

Population

2,813 adult patients (aged 18 years or older) with a 12-lead ECG showing LBBB and a paired echocardiogram performed within 14 days, mean age 70.7 years, 43.7% male, from two government-designated heart specialty hospitals in the Republic of Korea.

I

Intervention

AiTiALVSD ver. 2.00.00 AI-ECG model analyzing raw 12-lead ECG data to detect left ventricular systolic dysfunction

O

Outcome

Presence of LVSD, defined as an LVEF of 40% or less on echocardiography and measured using the biplane Simpson methodsurrogate

Main Result

Effect estimate: AUROC 0.930 (95% CI 0.924–0.937)

Limitations

Retrospective design
Relatively low specificity (47.3%) at sensitivity threshold
Study limited to Korean population
Possible bias due to inclusion of patients from development hospital despite exclusion from training data

Implication

The AiTiALVSD AI-ECG model accurately detects left ventricular systolic dysfunction and stratifies long-term cardiovascular risk in patients with left bundle branch block, offering a scalable screening tool.

Abstract

Background Left bundle branch block (LBBB) significantly increases the risk of left ventricular systolic dysfunction (LVSD) due to cardiac dyssynchrony. Although artificial intelligence–enabled electrocardiography (AI-ECG) models show promise in detecting LVSD, their performance in LBBB patients remains underexplored. We hypothesized that an AI-ECG model clinically validated for detecting LVSD would accurately detect LVSD and predict future clinical outcomes in LBBB patients. Methods In this retrospective multicenter study, 5,689 expert-validated LBBB ECGs collected from 2,813 patients between 2016 and 2024 were analyzed using a previously developed and validated AI-ECG model. LVSD was defined as an ejection fraction of ≤ 40%. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), sensitivity, and specificity. Patients were stratified into high- and low-risk groups based on a threshold that achieved 90% sensitivity. A Kaplan–Meier analysis was used to compare clinical outcomes. Results Among the 2,813 LBBB patients (mean age, 70.7 years; male sex, 43.7%), hypertension and a history of heart failure were common. The AiTiALVSD model showed strong diagnostic performance for LVSD (AUROC, 0.930 [95% CI, 0.924–0.937]; AUPRC, 0.913 [95% CI, 0.902–0.923]; sensitivity, 0.979; specificity, 0.473). During the mean follow-up of 4.1 years, high-risk patients had significantly higher hazards than low-risk patients for all-cause mortality (adjusted hazard ratio [HR], 1.87; 95% CI, 1.53–2.28), implantable cardioverter defibrillator/cardiac resynchronization therapy implantation (adjusted HR, 15.2; 95% CI, 7.51–30.77), and cardiovascular hospitalization (adjusted HR, 1.11; 95% CI, 0.96–1.28). Conclusions AiTiALVSD effectively detects LVSD and stratifies long-term cardiovascular risk in LBBB patients, supporting its clinical utility for early detection and patient management.

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Cite This Study

Lee et al. (2026) conducted an observational in Adult patients (aged ≥18) with left bundle branch block (LBBB) and paired echocardiogram within 14 days (n=2,813). AiTiALVSD AI-enabled ECG model vs. No AI-ECG assessment (standard care) was evaluated on Presence of left ventricular systolic dysfunction (LVSD) defined as echocardiographic LVEF ≤40% (AUROC 0.930, 95% CI 0.924–0.937). AiTiALVSD AI-enabled ECG model detected LV systolic dysfunction with an AUROC of 0.930 and 97.9% sensitivity in patients with LBBB.

synapsesocial.com/papers/699405494e9c9e835dfd628a https://doi.org/10.1186/s44348-026-00066-9

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Detection and prognostic stratification of left ventricular systolic dysfunction in left bundle branch block using an artificial intelligence–enabled electrocardiography

Figures

Key Points

Study Design

Structured PICO

Main Result

Limitations

Implication

Abstract

Discussion

Cite This Study

Detection and prognostic stratification of left ventricular systolic dysfunction in left bundle branch block using an artificial intelligence–enabled electrocardiography

Figures

Key Points

Study Design

Structured PICO

Main Result

Limitations

Implication

Abstract

Discussion

Cite This Study

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