Advancements in Cardiac CT Imaging: The Era of Artificial Intelligence.
In the last decade, artificial intelligence (AI) has influenced the field of cardiac computed tomography (CT), with its scope further enhanced by advanced methodologies such as machine learning (ML) and deep learning (DL). The AI‐driven techniques leverage large datasets to develop and train algorit...
| Publicado en: | Echocardiography Vol. 41; no. 12; pp. 1 - 13 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images pictorial review tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Dec2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181948249&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181948249 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07422822 GSE jtl: Echocardiography issn: 07422822 maglogo: Y pubinfo: dt: Dec2024 vid: 41 iid: 12 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 181948249 181948249 181948249 10.1111/echo.70042 181948249 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Advancements in Cardiac CT Imaging: The Era of Artificial Intelligence. aug: au: Costantini, Pietro Groenhoff, Léon Ostillio, Eleonora Coraducci, Francesca Secchi, Francesco Carriero, Alessandro Colarieti, Anna Stecco, Alessandro affil: Department of Translational Medicine, University of Eastern Piedmont, Novara, Italy sug: subj: Artificial Intelligence Heart Radiography Tomography, X-Ray Computed Methods Image Processing, Computer Assisted Coronary Arteriosclerosis Radiography Computed Tomography Angiography Coronary Circulation Evaluation Perfusion Evaluation Epicardial Adipose Tissue Analysis Deep Learning Coronary Artery Calcification Calcium Analysis Radiation Dosage Image Enhancement Time Factors Risk Assessment ab: In the last decade, artificial intelligence (AI) has influenced the field of cardiac computed tomography (CT), with its scope further enhanced by advanced methodologies such as machine learning (ML) and deep learning (DL). The AI‐driven techniques leverage large datasets to develop and train algorithms capable of making precise evaluations and predictions. The realm of cardiac CT is expanding day by day and multiple tools are offered to answer different questions. Coronary artery calcium score (CACS) and CT angiography (CTA) provide high‐resolution images that facilitate the detailed anatomical evaluation of coronary plaque burden. New tools such as myocardial CT perfusion (CTP) and fractional flow reserve (FFRCT) have been developed to add a functional evaluation of the stenosis. Moreover, epicardial adipose tissue (EAT) is gaining interest as its role in coronary artery plaque development has been deepened. Seen the great added value of these tools, the demand for new exams has increased such as the burden on imagers. Due to its ability to fast compute multiple data, AI can be helpful in both the acquisition and post‐processing phases. AI can possibly reduce radiation dose, increase image quality, and shorten image analysis time. Moreover, different types of data can be used for risk assessment and patient risk stratification. Recently, the focus of the scientific community on AI has led to numerous studies, especially on CACS and CTA. This narrative review concentrates on AI's role in the post‐processing of CACS, CTA, FFRCT, CTP, and EAT, discussing both current capabilities and future directions in the field of cardiac imaging. pubtype: Academic Journal doctype: diagnostic images pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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