ArticleRadiology2024
Improving Prognostication in Pulmonary Hypertension Using AI-quantified Fibrosis and Radiologic Severity Scoring at Baseline CT.
Article in Radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- Artificial intelligence in pulmonary hypertension: a systematic review.European journal of medical research · 2025Pooled it
- A Comprehensive Review of Artificial Intelligence (AI) Applications in Pulmonary Hypertension (PH).Medicina (Kaunas, Lithuania) · 2025Pooled it
- Meta-Analysis of Interobserver Agreement in Assessment of Interstitial Lung Disease Using High-Resolution CT.Radiology · 2024Pooled it
- Artificial Intelligence and Multi-Omics Approaches in the Precision Management of Pulmonary Hypertension: From Early Diagnosis to Therapeutic Stratification.International journal of molecular sciences · 2026Review
- Artificial Intelligence in Pulmonary Hypertension: Current State and Future Prospects.Reviews in cardiovascular medicine · 2026Review
- A Stepwise Approach to Computed Tomography Imaging of Pulmonary Hypertension.The Indian journal of radiology & imaging · 2026Review
- Highlights of the 2026 Pulmonary Vascular Research Institute Conference: Celebrating 20 Years of Progress and Innovation.Pulmonary circulation · 2026Article
- Review
- Article
- The role of artificial intelligence and mobile health in diagnosis and management of pulmonary arterial hypertension.International journal of cardiology. Congenital heart disease · 2025Review
- Utility of Clustering in Mortality Risk Stratification in Pulmonary Hypertension.Bioengineering (Basel, Switzerland) · 2025Article
- Chest Computed Tomography to Improve Phenotyping in Pulmonary Hypertension Associated with Chronic Obstructive Pulmonary Disease.Annals of the American Thoracic Society · 2025Article
- Interaction of ferroptosis and cuproptosis in the perspective of pulmonary hypertension.Frontiers in cardiovascular medicine · 2025Review
- Use of artificial intelligence algorithms to analyse systemic sclerosis-interstitial lung disease imaging features.Rheumatology international · 2024Article
- Definition, classification and diagnosis of pulmonary hypertension.The European respiratory journal · 2024Article
- Emerging multimodality imaging techniques for the pulmonary circulation.The European respiratory journal · 2024Review
- Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years.Diagnostics (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
14 authors at 1 institution in 1 country.
Funding
Abstract
Background There is clinical need to better quantify lung disease severity in pulmonary hypertension (PH), particularly in idiopathic pulmonary arterial hypertension (IPAH) and PH associated with lung disease (PH-LD). Purpose To quantify fibrosis on CT pulmonary angiograms using an artificial intelligence (AI) model and to assess whether this approach can be used in combination with radiologic scoring to predict survival. Materials and Methods This retrospective multicenter study included adult patients with IPAH or PH-LD who underwent incidental CT imaging between February 2007 and January 2019. Patients were divided into training and test cohorts based on the institution of imaging. The test cohort included imaging examinations performed in 37 external hospitals. Fibrosis was quantified using an established AI model and radiologically scored by radiologists. Multivariable Cox regression adjusted for age, sex, World Health Organization functional class, pulmonary vascular resistance, and diffusing capacity of the lungs for carbon monoxide was performed. The performance of predictive models with or without AI-quantified fibrosis was assessed using the concordance index (C index). Results The training and test cohorts included 275 (median age, 68 years [IQR, 60-75 years]; 128 women) and 246 (median age, 65 years [IQR, 51-72 years]; 142 women) patients, respectively. Multivariable analysis showed that AI-quantified percentage of fibrosis was associated with an increased risk of patient mortality in the training cohort (hazard ratio, 1.01 [95% CI: 1.00, 1.02];
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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 OpenQuestion graph.