Consiglio Nazionale delle Ricerche

Tipo di prodottoRassegna della letteratura scientifica in rivista (Literature review)
TitoloArtificial Neural Networks in Cardiovascular Diseases and its Potential for Clinical Application in Molecular Imaging
Anno di pubblicazione2020
Formato-
Autore/iRiccardo Laudicella 1, Albert Comelli 2, Alessandro Stefano 3, Monika Szostek 4, Ludovica Crocè 1, Antonio Vento 1, Alessandro Spataro 1, Alessio Danilo Comis 1, Flavia La Torre 1, Michele Gaeta 5, Sergio Baldari 1, Pierpaolo Alongi 6
Affiliazioni autori1. Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging, University of Messina, 98125 Messina. Italy. 2. Ri.MED Foundation, Palermo. Italy. 3. Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Cefalù. Italy. 4.Maria Sklodowska- Curie National Research Institute of Oncology (MSCNRIO), Department of Endocrine Oncology and Nuclear Medicine, Warsaw. Poland. 5.Section of Radiological Sciences, Department of Biomedical Sciences and Morphological and Functional Imaging, University of Messina, Messina. Italy. 6.Nuclear Medicine Unit, Fondazione Istituto G.Giglio , Ct.da Pietra Pollastra-Pisciotto, Cefalù. Italy.
Autori CNR e affiliazioni
  • ALESSANDRO STEFANO
Lingua/e
  • inglese
AbstractBackground: In medical imaging, Artificial Intelligence is described as the ability of a system to properly interpret and learn from external data, acquiring knowledge to achieve specific goals and tasks through flexible adaptation. The number of possible applications of Artificial Intelligence is huge also in clinical medicine and in cardiovascular diseases. Objective: To describe for the first time in literature, the main results of articles about Artificial Intelligence potential for clinical applications in molecular imaging techniques, and to describe its advancements in cardiovascular diseases assessed with nuclear medicine imaging modalities. Methods: A comprehensive search strategy was used based on SCOPUS and PubMed databases. From all studies published in English, we selected the most relevant articles that evaluated the technological insights of AI in nuclear cardiology applications. Results: Artificial Intelligence may improve the patient care on many different fields, from the semi-automatization of the medical work, through the technical aspect of image preparation, interpretation, the calculation of additional factors based on data obtained during scanning, to the prognostic prediction and risk-group selection. Conclusion: Myocardial implementation of Artificial Intelligence algorithms in nuclear cardiology can improve and facilitate the diagnostic and predictive process, and global patient care. Building large databases containing clinical and image data is a first but essential step to create and train automated diagnostic/prognostic models able to help the clinicians to make unbiased and faster decisions for precision healthcare.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da-
Pagine a-
Pagine totali-
RivistaCurrent radiopharmaceuticals (Online)
Attiva dal 2008
Editore: Bentham Science Publishers - Hilversum
Paese di pubblicazione: Paesi Bassi
Lingua: inglese
ISSN: 1874-4729
Titolo chiave: Current radiopharmaceuticals (Online)
Titolo proprio: Current radiopharmaceuticals. (Online)
Numero volume della rivista-
Fascicolo della rivista-
DOI10.2174/1874471013666200621191259
Verificato da referee-
Stato della pubblicazionePostprint
Indicizzazione (in banche dati controllate)
  • PubMed (Codice:32564769)
Parole chiaveCT; Medical imaging; SPECT; artificial intelligence; deep learning; nuclear cardiology; radiomics.
Link (URL, URI)-
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IBFM — Istituto di bioimmagini e fisiologia molecolare
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati