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

作者：Yuxuan Zeng, Gonghao Ling, Haojie Zhang, Wei Cao, Xuan Zheng, Xiaoxian Deng, Lan Lan, Rongqing Sun, Xintian Liu, Lin Tian, Haibo Xu, Ziyu Wang, Gangcheng Zhang · 发表于：npj Digital Medicine · 年份：2025 · DOI：10.1038/s41746-025-02233-6 · 被引用次数：2 · 研究领域：Pulmonary Hypertension Research and Treatments、Hemodynamic Monitoring and Therapy、Phonocardiography and Auscultation Techniques

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 features into the disease evaluation framework. Compared to traditional invasive measurement methods, this framework can not only accurately predict a patient's mPAP using easily accessible noninvasive physiological features but also incorporate uncertainty quantification to extract qualitative patterns, aiding clinical diagnosis.