What question did this study set out to answer?

To evaluate the diagnostic performance of AI-assisted echocardiography for identifying hypertrophic cardiomyopathy (HCM).

January 18, 2026Cardiology in Review

Diagnostic Performance of Artificial Intelligence-Assisted Echocardiography in Identifying Hypertrophic Cardiomyopathy: A Systematic Review and Meta-Analysis

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Key result

AI-assisted echocardiography identifies HCM with a pooled AUC of 0.96.

Why the study?

Hypertrophic cardiomyopathy remains underdiagnosed due to overlapping echocardiographic characteristics and subjective interpretations.

Does artificial intelligence-assisted echocardiography accurately identify hypertrophic cardiomyopathy?

Comparison

AI-assisted echocardiography interpretations

Design

Systematic review and meta-analysis

Authors

SSShayan ShojaeiTehran University of Medical Sciences MNMohammad Ali NazariInstitut polytechnique de Grenoble NGNegar GhasemlooIran University of Medical Sciences

Discussion

Member takes

Implication

Supports AI-assisted echocardiography for HCM detection; confirms consensus and extends pooled diagnostic evidence.

Key Points

To evaluate the diagnostic performance of AI-assisted echocardiography for identifying hypertrophic cardiomyopathy (HCM).
Performed a systematic review and meta-analysis
Included studies reporting diagnostic metrics like sensitivity and specificity
Pooled data using a bivariate random-effects model
Evaluated heterogeneity using the I2 statistic
Analyzed 25 eligible studies
Pooled area under the curve (AUC) was 0.93 (95% CI, 0.90–0.95)
After correction, pooled AUC improved to 0.96 (95% CI, 0.93–0.97)
Sensitivity of AI-based echocardiography was 0.89 (95% CI, 0.83–0.93)
Specificity was 0.87 (95% CI, 0.76–0.94)
Convolutional neural networks showed higher sensitivity compared to other algorithms.

Structured PICO

Does artificial intelligence-assisted echocardiography accurately identify hypertrophic cardiomyopathy?

Population

25 studies evaluating patients for hypertrophic cardiomyopathy (HCM) using echocardiography

Intervention

Artificial intelligence (AI)-assisted echocardiography interpretations (including convolutional neural networks, support vector machines, and ensemble learning algorithms)

Outcome

Diagnostic performance measured by area under the curve (AUC), sensitivity, and specificitysurrogate

Artificial intelligence-assisted echocardiography demonstrates high diagnostic accuracy for identifying hypertrophic cardiomyopathy, highlighting its potential to improve clinical decision-making.

Cite This Study

Shojaei et al. (2026) studied this question. AI-assisted echocardiography demonstrated a pooled AUC of 0.96 for identifying hypertrophic cardiomyopathy, with sensitivity of 0.89 and specificity of 0.87.

synapsesocial.com/papers/696c77d4eb60fb80d13960b6 https://doi.org/10.1097/crd.0000000000001172

Topics

Artificial intelligence in cardiology Echocardiography Hypertrophic cardiomyopathy

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Diagnostic Performance of Artificial Intelligence-Assisted Echocardiography in Identifying Hypertrophic Cardiomyopathy: A Systematic Review and Meta-Analysis

Discussion

Member takes

Implication

Key Points

Structured PICO

Cite This Study

Topics

Diagnostic Performance of Artificial Intelligence-Assisted Echocardiography in Identifying Hypertrophic Cardiomyopathy: A Systematic Review and Meta-Analysis

Discussion

Member takes

Implication

Key Points

Structured PICO

Cite This Study

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