Evidence map›Paper›PMID 36708505›Full record

ReviewCurrent cardiology reports2023

Current and Future Applications of Artificial Intelligence in Cardiac CT.

Mugdha Joshi, Diana Patricia Melo, David Ouyang, Piotr J Slomka, Michelle C Williams, Damini Dey

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current cardiology reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Mugdha JoshiDepartment of Medicine, Stanford Healthcare, Palo Alto, CA, USA.
Diana Patricia MeloDivision of Cardiovascular Medicine, Stanford Healthcare, Palo Alto, CA, USA.
David OuyangCedars-Sinai Medical Center, Smidt Heart Institute, Los Angeles, CA, USA.
Piotr J SlomkaDepartment of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Michelle C WilliamsBritish Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
Damini DeyCedars-Sinai Medical Center, Biomedical Imaging Research Institute, 116 N Robertson Boulevard, Los Angeles, CA, 90048, USA. damini.dey@cshs.org.ORCID 0000-0003-2236-6970

Funding

Integrated prediction of cardiovascular events by automated coronary plaque and pericoronary adipose tissue quantification from CT AngiographyR01HL148787 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI DEY, DAMINI · 2020 to 2023
$3.0M
Effect of Intensive Medical Treatment on Quantified Coronary Artery Plaque Components with Serial Coronary CTA in Women with Non-Obstructive CADR01HL151266 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI DEY, DAMINI, TAMARAPPOO, BALAJI K. · 2020 to 2023
$1.6M
British Heart Foundation FS/ICRF/20/26002Medical Research Council G0701127NHLBI Division of Intramural Research 1R01HL148787NHLBI Division of Intramural Research 1R01HL151266
6 · The paper itself

Abstract

purpose of reviewIn this review, we aim to summarize state-of-the-art artificial intelligence (AI) approaches applied to cardiovascular CT and their future implications. RECENT

findingsRecent studies have shown that deep learning networks can be applied for rapid automated segmentation of coronary plaque from coronary CT angiography, with AI-enabled measurement of total plaque volume predicting future heart attack. AI has also been applied to automate assessment of coronary artery calcium on cardiac and ungated chest CT and to automate the measurement of epicardial fat. Additionally, AI-based prediction models integrating clinical and imaging parameters have been shown to improve prediction of cardiac events compared to traditional risk scores. Artificial intelligence applications have been applied in all aspects of cardiovascular CT - in image acquisition, reconstruction and denoising, segmentation and quantitative analysis, diagnosis and decision assistance and to integrate prognostic risk from clinical data and images. Further incorporation of artificial intelligence in cardiovascular imaging holds important promise to enhance cardiovascular CT as a precision medicine tool.

Indexed as

Artificial IntelligenceMyocardial InfarctionComputed Tomography AngiographyCoronary AngiographyHeartHumansArtificial intelligenceCardiovascular CTCoronary calcium scoringCoronary CT angiographyMachine learning

Identifiers

PMID36708505

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.