Key result
AI accurately assesses right ventricular size and function with a pooled AUROC of ~0.89.
Why the study?
Accurate RV assessment is challenged by the limitations of traditional echocardiography, and the diagnostic accuracy of AI models for predicting RV size and function remains uncertain.
Do artificial intelligence models accurately predict right ventricular size and function?
Meta-Analysis
Do artificial intelligence models accurately predict right ventricular size and function?
Effect estimate: AUROC 0.89 (95% CI 0.86-0.92)
AI models show promising diagnostic accuracy for assessing right ventricular size and function, though high heterogeneity necessitates cautious interpretation.
AI models may support RV assessment with moderate accuracy; leaves open need for prospective validation amid high heterogeneity.
Background: Accurate assessment of right ventricular (RV) size and function is critical for managing cardiac diseases but is challenged by the limitations of traditional echocardiography. Artificial intelligence (AI) models offer potential for improving RV assessment, yet their diagnostic accuracy remains uncertain. This systematic review and meta-analysis evaluates the diagnostic accuracy of AI models for predicting RV size and function, synthesizing performance metrics and assessing evidence quality. Methods: Adhering to PRISMA guidelines, we searched 5 databases up to June 2025 using MeSH and Emtree terms for "Artificial Intelligence," "Right Ventricular Function," and "Right Ventricular Dysfunction." Two reviewers screened studies, extracted data and assessed quality using PROBAST+AI. Pooled estimates were calculated using STATA 18 with MIDAS and METADATA modules. Heterogeneity was explored via subgroup analyses, meta-regression, and sensitivity analyses. Publication bias was assessed using funnel plot. Results: From 25 studies, 18 provided data for meta-analysis, yielding a pooled sensitivity of 0.85 (95 % CI: 0.73-0.92), specificity of 0.81 (95 % CI: 0.72-0.88), and AUROC of 0.89 (95 % CI: 0.86-0.92). High heterogeneity (I² = 71.63 % for sensitivity, 73.51 % for specificity) was partially explained by algorithm type and study country. The GRADE assessment indicated moderate certainty of evidence due to heterogeneity and bias in 25 % of studies. Conclusion: AI models show promising diagnostic accuracy for RV assessment, but high heterogeneity and moderate evidence certainty necessitate cautious interpretation and further research.
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A 2025 study conducted a meta-analysis in Right ventricular size and function assessment. Artificial intelligence (AI) models was evaluated on Diagnostic accuracy (AUROC) (AUROC 0.89, 95% CI 0.86-0.92). Artificial intelligence models showed promising diagnostic accuracy for right ventricular assessment, yielding a pooled AUROC of 0.89 (95% CI 0.86-0.92), sensitivity of 0.85, and specificity of 0.81.
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