Development and validation of multimodal deep learning algorithms for detecting pulmonary hypertension - Wei Zhao; Zhihua Huang; Xiaolin Diao; Zhan Yang; Zhihui Zhao; Yun Xia; Qing Zhao; Zhaohong Sun; Qunying Xi; Yanni Huo; Ou Xu; Jiahui Geng; et al. (npj Digital Medicine)
Abstract Transthoracic echocardiography (TTE), commonly used for initial screening of pulmonary hypertension (PH), often lacks sufficient accuracy. To address this gap, we developed and validated a multimodal fusion model for improved PH screening (MMF-PH). The study was registered in the ClinicalTrials.gov (NCT05566002, 09/30/2022). The MMF-PH underwent extensive training, validation, and testing, including comparisons with TTE and evaluations across various patient subgroups to assess robustness and reliability. We analyzed 2451 patients who underwent right heart catheterization, supplemented by a prospective dataset of 477 patients and an external dataset. The MMF-PH demonstrated robust performance across different datasets. The model outperformed TTE in terms of specificity and negative predictive value across all test datasets. An ablation study using the external test dataset confirmed the essential role of each module in the MMF-PH. The MMF-PH significantly advances PH detection, offering robust and reliable diagnostic accuracy across diverse patient populations and clinical settings.