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Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media?
In recent years, cardiovascular imaging examinations have experienced exponential growth due to technological innovation, and this trend is consistent with the most recent chest pain guidelines. Contrast media have a crucial role in cardiovascular magnetic resonance (CMR) imaging, allowing for more...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297403/ https://www.ncbi.nlm.nih.gov/pubmed/37370956 http://dx.doi.org/10.3390/diagnostics13122061 |
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author | Cau, Riccardo Pisu, Francesco Suri, Jasjit S. Mannelli, Lorenzo Scaglione, Mariano Masala, Salvatore Saba, Luca |
author_facet | Cau, Riccardo Pisu, Francesco Suri, Jasjit S. Mannelli, Lorenzo Scaglione, Mariano Masala, Salvatore Saba, Luca |
author_sort | Cau, Riccardo |
collection | PubMed |
description | In recent years, cardiovascular imaging examinations have experienced exponential growth due to technological innovation, and this trend is consistent with the most recent chest pain guidelines. Contrast media have a crucial role in cardiovascular magnetic resonance (CMR) imaging, allowing for more precise characterization of different cardiovascular diseases. However, contrast media have contraindications and side effects that limit their clinical application in determinant patients. The application of artificial intelligence (AI)-based techniques to CMR imaging has led to the development of non-contrast models. These AI models utilize non-contrast imaging data, either independently or in combination with clinical and demographic data, as input to generate diagnostic or prognostic algorithms. In this review, we provide an overview of the main concepts pertaining to AI, review the existing literature on non-contrast AI models in CMR, and finally, discuss the strengths and limitations of these AI models and their possible future development. |
format | Online Article Text |
id | pubmed-10297403 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102974032023-06-28 Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media? Cau, Riccardo Pisu, Francesco Suri, Jasjit S. Mannelli, Lorenzo Scaglione, Mariano Masala, Salvatore Saba, Luca Diagnostics (Basel) Review In recent years, cardiovascular imaging examinations have experienced exponential growth due to technological innovation, and this trend is consistent with the most recent chest pain guidelines. Contrast media have a crucial role in cardiovascular magnetic resonance (CMR) imaging, allowing for more precise characterization of different cardiovascular diseases. However, contrast media have contraindications and side effects that limit their clinical application in determinant patients. The application of artificial intelligence (AI)-based techniques to CMR imaging has led to the development of non-contrast models. These AI models utilize non-contrast imaging data, either independently or in combination with clinical and demographic data, as input to generate diagnostic or prognostic algorithms. In this review, we provide an overview of the main concepts pertaining to AI, review the existing literature on non-contrast AI models in CMR, and finally, discuss the strengths and limitations of these AI models and their possible future development. MDPI 2023-06-14 /pmc/articles/PMC10297403/ /pubmed/37370956 http://dx.doi.org/10.3390/diagnostics13122061 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Cau, Riccardo Pisu, Francesco Suri, Jasjit S. Mannelli, Lorenzo Scaglione, Mariano Masala, Salvatore Saba, Luca Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media? |
title | Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media? |
title_full | Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media? |
title_fullStr | Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media? |
title_full_unstemmed | Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media? |
title_short | Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media? |
title_sort | artificial intelligence applications in cardiovascular magnetic resonance imaging: are we on the path to avoiding the administration of contrast media? |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297403/ https://www.ncbi.nlm.nih.gov/pubmed/37370956 http://dx.doi.org/10.3390/diagnostics13122061 |
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