Development and validation of multimodal deep learning algorithms for detecting pulmonary hypertension

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.

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Publication Details

Journal
npj Digital Medicine
Published
2025-04-09
DOI
https://doi.org/10.1038/s41746-025-01593-3
Citations
8
Primary Topic
Pulmonary Hypertension Research and Treatments
Type
article
Field-Weighted Citation Impact
12.15

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article

Development and validation of multimodal deep learning algorithms for detecting pulmonary hypertension

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8 citations
npj Digital Medicine
Pulmonary Hypertension Research and Treatments
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Development and validation of multimodal deep learning algorithms for detecting pulmonary hypertension

Zhihua Huang, Yanni Huo, Ou Xu, Anqi Duan, Xiaolin Diao, Yang Zhan, Sicong Li, Yijia Wang, Luyang Gao, Jiahui Geng, Qunying Xi, Yun Xia, Zhihong Liu, Xin Li, Qing Zhao, Zhihui Zhao, Qin Luo, Wei Zhao, Sicheng Zhang, Zhaohong Sun
article en
8 citations

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.

npj Digital MedicineVol. 8(1)
Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Peking Union Medical College Hospital (CN), Fuwai Yunnan Cardiovascular Hospital (CN), Fu Wai Hospital (CN)
Chinese Academy of Medical Sciences, Peking Union Medical College, Fuwai Hospital, Chinese Academy of Medical Sciences
Openalex Percentile: Top 1%
Pulmonary Hypertension Research and Treatments
12.15
DOIOpen Access PDFOpenAlex
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