Artificial Intelligence in Pulmonary Hypertension: Current State and Future Prospects
Abstract
Pulmonary hypertension (PH) remains diagnostically challenging due to the associated non-specific symptomatology and frequent diagnostic delays, both of which contribute to increased morbidity and mortality. Meanwhile, right heart catheterization is the diagnostic gold standard; nonetheless, the invasive nature and limited accessibility of this technique limit its routine use, particularly in resource-constrained settings. This review evaluates computational approaches that may enhance PH diagnosis through advanced analysis of cardiovascular imaging data. We conducted a comprehensive literature review focusing on computer-assisted diagnostic methods in PH across multiple imaging modalities, including electrocardiogram, chest X-ray, echocardiography, cardiac magnetic resonance (CMR) imaging, and cardiac computed tomography (CCT). Eligible studies were analyzed for diagnostic performance, clinical applicability, and methodological rigor. Preliminary studies have demonstrated promising performance in detecting early or subclinical PH phenotypes across various imaging platforms. Advanced imaging modalities benefit from automated segmentation and quantitative analysis, and CMR- and CT-based approaches demonstrate high diagnostic accuracy. Current artificial intelligence (AI) models face significant challenges related to clinical interpretability, external validation across diverse populations, and seamless integration into existing diagnostic workflows. Most studies are based on…
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