ArticleNPJ digital medicine2025
Development and validation of multimodal deep learning algorithms for detecting pulmonary hypertension.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Artificial Intelligence-Assisted Evaluation of Pulmonary Hypertension
Research on Dynamic Risk Prediction for Patients With Pulmonary Hypertension Based on Multimodal Data Fusion: A Prospective Observational Study
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in pulmonary hypertension: a systematic review.European journal of medical research · 2025Pooled it
- Artificial Intelligence in Pulmonary Hypertension: Current State and Future Prospects.Reviews in cardiovascular medicine · 2026Review
- Machine-Reported Electrocardiographic Right Axis Deviation or Right Ventricular Hypertrophy and Echocardiographic Pulmonary Hypertension Phenotypes and Right-Heart Abnormalities.Echocardiography (Mount Kisco, N.Y.) · 2026Observational
- Clinical research on pulmonary hypertension from a 2025 perspective: a narrative review.Journal of thoracic disease · 2026Review
- Advancing the diagnosis of pulmonary arterial hypertension-a Brazilian perspective aligned with global standards.Jornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia · 2026Article
- Article
- Artificial intelligence-empowered echocardiography: an updated review of clinical management in hypertrophic cardiomyopathy.Frontiers in cardiovascular medicine · 2026Review
- Artificial intelligence-driven multivariate integration for pulmonary arterial pressure prediction in pulmonary hypertension.NPJ digital medicine · 2025Article
- Leveraging Artificial Intelligence for the Diagnosis of Systemic Sclerosis Associated Pulmonary Arterial Hypertension: Opportunities, Challenges, and Future Perspectives.Advances in respiratory medicine · 2025Review
- Physics-Informed Neural Network for Modeling the Pulmonary Artery Blood Pressure from Magnetic Resonance Images: A Reduced-Order Navier-Stokes Model.Biomedicines · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors.
Funding
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.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.