Artificial Intelligence in Cardiovascular Imaging: Integration with Clinical and Nonimaging Data

Journal of Cardiovascular Echography

Abstract

Abstract Artificial intelligence (AI) is increasingly transforming cardiovascular imaging, extending beyond automated image analysis to the integration of imaging with diverse clinical and nonimaging data. This multimodal approach has the potential to enhance diagnostic accuracy, improve risk stratification, and support personalized management across a wide range of cardiovascular diseases. In this review, we discuss the role of AI in integrating cardiovascular imaging data with complementary information sources, including clinical and demographic variables, laboratory biomarkers, electrocardiographic data, genetic and omics profiles, electronic health records, and data derived from wearable devices. Machine learning and deep learning techniques enable the analysis of these heterogeneous datasets, facilitating the identification of complex patterns that are not readily captured by traditional analytical methods. We highlight the potential applications of multimodal AI models in major cardiovascular conditions, including coronary artery disease, heart failure, valvular heart disease, and cardiomyopathies. By combining information from imaging modalities such as echocardiography, computed tomography, and cardiac magnetic resonance, with nonimaging data, AI may improve disease detection, prognostic assessment, and therapeutic decision-making. Finally, we outline the key challenges related to data integration, model interpretability, generalizability, and ethical considerations. The continued development of AI-driven multimodal frameworks is expected to play an important role in advancing precision cardiovascular medicine and maximizing the clinical utility of cardiovascular imaging.

Authors 9

1. Antonella Maurizia Moreo

   Azienda Socio Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda

   Affiliation as printed

   Department of Cardiology, De Gasperis Cardio Center, ASST Grande Ospedale Metropolitano Niguarda, Piazza dell’Ospedale Maggiore 3, 20162 Milan, Italy

2. Sara Bombace

   University Medical Center of the Johannes Gutenberg University Mainz

   Affiliation as printed

   Department of Cardiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany

3. Stefano Sforna
   Affiliation as printed

   Advanced Cardiovascular Diagnostic Service, Cardiac Rehabilitation and Sports Cardiology, Media Valle del Tevere Hospital, USL Umbria 1, Pantalla (Perugia), Italy

4. Emanuele Frontoni

   University of Macerata

   Affiliation as printed

   Department of Political Sciences, Communication and International Relations, University of Macerata, Macerata, Italy

5. Marcello Panunzi

   Ospedale SS. Annunziata

   Affiliation as printed

   Heart Department, University Cardiology Division, SS. Annunziata Hospital, Chieti, Italy

6. Andrea Bonelli

   Azienda Socio Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda

   Affiliation as printed

   Department of Cardiology, De Gasperis Cardio Center, ASST Grande Ospedale Metropolitano Niguarda, Piazza dell’Ospedale Maggiore 3, 20162 Milan, Italy

7. Fabrizio Ricci

   Ospedale SS. Annunziata

   Affiliation as printed

   Heart Department, University Cardiology Division, SS. Annunziata Hospital, Chieti, Italy

8. Nina Ajmone Marsan Aachen Department of Cardiology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Cardiology, Leiden University Medical Center, Albinusdreef 2, Leiden 2333 ZA, Netherlands

9. Federico Fortuni

   University of Perugia

   Affiliation as printed

   Cardiology and Cardiovascular Pathophysiology, S. Maria Della Misericordia Hospital, University of Perugia, Piazzale Giorgio Menghini, 3, Perugia 06129, Italy