Artificial intelligence-driven multivariate integration for pulmonary arterial pressure prediction in pulmonary hypertension.

ABSTRACT: Reliable machine learning techniques have vast potential in assisting clinical decision-making, including applications in bioinformatics and medical imaging analysis. However, AI-driven medical research is often limited by data scarcity, data quality, and the black-box nature of machine learning models. Thus, there is an urgent need for reliable surrogate models to overcome these challenges, enabling accurate learning from small datasets to guide clinical diagnosis. Here, we conducted a retrospective observational clinical study and proposed a data-driven predictive model that estimates mean pulmonary artery pressure (mPAP) based on individual patient clinical diagnostic features, enabling accurate assessment of pulmonary hypertension. Furthermore, we innovatively incorporate CMR-related f

SUBMITTER: Zeng Y 

PROVIDER: S-EPMC12816686 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature