Comparative accuracy of artificial intelligence versus manual interpretation in detecting pulmonary hypertension across chest imaging modalities: a diagnostic test accuracy meta-analysis

TL;DR

This study shows that AI improves the accuracy of detecting pulmonary hypertension in chest imaging compared to manual methods.

Contribution

The paper provides a meta-analysis comparing AI and manual interpretation for PH detection across multiple imaging modalities.

Findings

AI models showed a pooled sensitivity of 0.83 and specificity of 0.91 for detecting PH.

AI integration improved diagnostic accuracy with a logit mean difference in AUC of 0.43 compared to manual interpretation.

The meta-analysis included 12 studies with 7,459 patients and found low heterogeneity in AUC results.

Abstract

Pulmonary hypertension (PH) has an incidence of approximately 6 cases per million adults, with a global prevalence ranging from 49 to 55 cases per million adults. Recent advancements in artificial intelligence (AI) have demonstrated promising improvements in the diagnostic accuracy of imaging for PH, achieving an area under the curve (AUC) of 0.94, compared to seasoned professionals.

To systematically synthesize available evidence on the comparative accuracy of AI versus manual interpretation in detecting PH across various chest imaging modalities, i.e., chest X-ray, echocardiography, CT scan and cardiac MRI.

Following PRISMA guidelines, a comprehensive search was conducted across five databases—PubMed, Embase, ScienceDirect, Scopus, and the Cochrane Library—from inception through March 2025. Statistical analysis was performed using R (version 2024.12.1 + 563) with 2 × 2 contingency data. Sensitivity, specificity, and diagnostic odds ratio (DOR) were pooled using a bivariate random-effects model (reitsma() from the mada package), while the AUC were meta-analyzed using logit-transformed values via the metagen() function from the meta package.

This meta-analysis of 12 studies, encompassing 7,459 patients, demonstrated a statistically significant improvement in diagnostic accuracy of PH with AI integration, evidenced by a logit mean difference in AUC of 0.43 (95% CI: 0.23–0.64; p < 0.0001) and low heterogeneity (I2 = 21.0%, τ2 < 0.0001, p = 0.2090), which was consolidated by pooled AUC of 0.934 on bivariate model. Pooled sensitivity and specificity for AI models were 0.83 (95% CI: 0.73–0.90) and 0.91 (95% CI: 0.86–0.95), respectively, with substantial heterogeneity for sensitivity (I2 = 83.8%, τ2 = 0.4934, p < 0.0001) and moderate for specificity (I2 = 41.5%, τ2 = 0.1015, p = 0.1146); the diagnostic odds ratio was 54.26 (95% CI: 22.50–130.87) with substantial heterogeneity (I2 = 70.7%, τ2 = 0.8451, p = 0.0023). Sensitivity analysis showed stable estimates and did not reduce heterogeneity across outcomes.

AI-integrated imaging significantly enhances diagnostic accuracy for pulmonary hypertension, with higher sensitivity (0.83) and specificity (0.91) compared to manual interpretation across chest imaging modalities. However, further high-quality trials with externally validated cohorts may be needed to confirm these findings and reduce variability among AI models across diverse clinical settings.

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