Artificial Intelligence and Multi-Omics Approaches in the Precision Management of Pulmonary Hypertension: From Early Diagnosis to Therapeutic Stratification
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Le résumé fourni par la source
Pulmonary hypertension (PH) is a heterogeneous clinical syndrome in which similar haemodynamic abnormalities may arise from distinct vascular, cardiac, pulmonary, thromboembolic, and molecular mechanisms. This complexity limits the ability of conventional classifications and risk scores to fully capture individual disease trajectories and treatment responses. Artificial intelligence (AI) offers a framework for integrating clinical data, electrocardiography, multimodal imaging, invasive haemodynamics, biomarkers, and multi-omics information across the PH care pathway. This review summarises current applications of machine learning and deep learning in early detection, diagnostic referral, right-ventricular and pulmonary vascular phenotyping, molecular endotyping, risk stratification, and therapeutic decision support. Available studies show promising results for AI-assisted electrocardiographic screening, automated echocardiographic and cardiac magnetic resonance analysis, computed tomography (CT)-based phenotyping, and multimodal prognostic modelling. True multi-omics integration in PH remains limited to discovery studies and has not yet yielded externally validated endotype or treatment-response classifiers. Evidence maturity is task-dependent: screening and phenotyping span several PH groups, whereas validated risk tools, molecular endotyping, and pathway-directed therapy remain predominantly PAH-based, particularly in idiopathic/heritable PAH. However, most evidence remains retrospective, derives from selected referral populations, and lacks robust external or prospective validation. No AI-based model currently supports routine drug selection or autonomous clinical decision-making. Future progress will require harmonised multicentre datasets and standardised acquisition protocols, transparent and interpretable models, and prospective studies demonstrating meaningful clinical benefit. AI should therefore be viewed as an emerging decision-support tool that may strengthen precision medicine in PH while complementing clinical expertise across diagnosis, phenotyping, risk assessment, and therapeutic stratification pathways.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Artificial Intelligence and Multi-Omics Approaches in the Precision Management of Pulmonary Hypertension: From Early Diagnosis to Therapeutic Stratification
- Date Crossref
- 30/08/2026
- Éditeur
- MDPI AG
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Ospedale di Santo Spirito pays non établi dans la noticeÉtablissement de santé
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University of Palermo Molecular and Clinical Medicine PhD Program pays non établi dans la noticeUniversité ou école supérieure
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Ospedale Vincenzo Cervello pays non établi dans la noticeÉtablissement de santé
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Fondazione Istituto G. Giglio di Cefalù pays non établi dans la noticeInstitution
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Ospedale Buccheri la Ferla Fatebenefratelli pays non établi dans la noticeÉtablissement de santé
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Università degli Studi di Enna Kore pays non établi dans la noticeUniversité ou école supérieure
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San Vito e Santo Spirito Hospital Cardiology Unit pays non établi dans la noticeÉtablissement de santé
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Cervello Hospital Cardiology Unit pays non établi dans la noticeÉtablissement de santé
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Medicine Unit pays non établi dans la noticeInstitution
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S. Elia Hospital Department of Internal Medicine pays non établi dans la noticeÉtablissement de santé
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Buccheri La Ferla Hospital Department of Internal Medicine pays non établi dans la noticeÉtablissement de santé
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“Kore” University of Enna Department of Medicine and Surgery pays non établi dans la noticeUniversité ou école supérieure
Ospedale di Santo Spirito, Molecular and Clinical Medicine PhD Program — University of Palermo et Ospedale Vincenzo Cervello, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.