Automated assessment of right heart function by artificial intelligence: A systematic review and meta-analysis

Abstract

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.