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arXiv · 2505.07347

AI-Enabled Accurate Non-Invasive Assessment of Pulmonary Hypertension Progression via Multi-Modal Echocardiography

Jiewen Yang·Taoran Huang·Shangwei Ding·Xiaowei Xu·Qinhua Zhao·Yong Jiang·Jiarong Guo·Bin Pu·Jiexuan Zheng·Caojin Zhang·Hongwen Fei·Xiaomeng Li
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Abstract

Echocardiographers can detect pulmonary hypertension using Doppler echocardiography; however, accurately assessing its progression often proves challenging. Right heart catheterization (RHC), the gold standard for precise evaluation, is invasive and unsuitable for routine use, limiting its practicality for timely diagnosis and monitoring of pulmonary hypertension progression. Here, we propose MePH, a multi-view, multi-modal vision-language model to accurately assess pulmonary hypertension progression using non-invasive echocardiography. We constructed a large dataset comprising paired standardized echocardiogram videos, spectral images and RHC data, covering 1,237 patient cases from 12 medical centers. For the first time, MePH precisely models the correlation between non-invasive multi-view, multi-modal echocardiography and the pressure and resistance obtained via RHC. We show that MePH significantly outperforms echocardiographers' assessments using echocardiography, reducing the mean absolute error in estimating mean pulmonary arterial pressure (mPAP) and pulmonary vascular resistance (PVR) by 49.73% and 43.81%, respectively. In eight independent external hospitals, MePH achieved a mean absolute error of 3.147 for PVR assessment. Furthermore, MePH achieved an area under the curve of 0.921, surpassing echocardiographers (area under the curve of 0.842) in accurately predicting the severity of pulmonary hypertension, whether mild or severe. A prospective study demonstrated that MePH can predict treatment efficacy for patients. Our work provides pulmonary hypertension patients with a non-invasive and timely method for monitoring disease progression, improving the accuracy and efficiency of pulmonary hypertension management while enabling earlier interventions and more personalized treatment decisions.

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Jiewen Yang, Taoran Huang, Shangwei Ding, Xiaowei Xu, Qinhua Zhao, Yong Jiang, Jiarong Guo, Bin Pu, Jiexuan Zheng, Caojin Zhang, Hongwen Fei, Xiaomeng Li. 2025-05-12. AI-Enabled Accurate Non-Invasive Assessment of Pulmonary Hypertension Progression via Multi-Modal Echocardiography. https://arxiv.org/abs/2505.07347

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