Evidence map›Paper›PMID 28277804›Full record

ReviewExpert review of medical devices2017

Cardiac imaging: working towards fully-automated machine analysis & interpretation.

Piotr J Slomka, Damini Dey, Arkadiusz Sitek, Manish Motwani, Daniel S Berman, Guido Germano

Open access · greenAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Expert review of medical devices, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 1 pooled it
11.6field-weighted citation impact, top 1% of its field
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

47 citing papers in PubMed, 1 synthesis or guideline pooled it, 106 citations in OpenAlex.

  1. Pooled it
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  18. Machine Learning in Medicine: Review and Applicability.Arquivos brasileiros de cardiologia · 2022
    Review
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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 at 3 institutions in 2 countries.

Piotr J Slomkaa Department of Imaging (Division of Nuclear Medicine) and Medicine , Cedars-Sinai Medical Center , Los Angeles , CA , USA.
Damini Deyb Biomedical Imaging Research Institute , Cedars-Sinai Medical Center , Los Angeles , CA , USA.
Arkadiusz Sitekc Philips Research North America , Cambridge , MA , USA.
Manish Motwanid Cardiovascular Imaging , Manchester Heart Centre, Manchester Royal Infirmary , Manchester , UK.
Daniel S Bermana Department of Imaging (Division of Nuclear Medicine) and Medicine , Cedars-Sinai Medical Center , Los Angeles , CA , USA.
Guido Germanoa Department of Imaging (Division of Nuclear Medicine) and Medicine , Cedars-Sinai Medical Center , Los Angeles , CA , USA.
Cedars-Sinai Medical Center · USManchester Royal Infirmary · GBPhilips (United States) · US

Funding

Quantitative Prediction of Disease and Outcomes from Next Generation SPECT and CTR01HL089765 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI SLOMKA, PIOTR J · 2007 to 2021
$6.1M
Automated quantitative CT imaging of epicardial adipose tissue and risk of cardiac eventsR01HL133616 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI DEY, DAMINI · 2016 to 2019
$2.6M
NHLBI NIH HHS R01 HL089765NHLBI NIH HHS R01 HL133616
6 · The paper itself

Abstract

introductionNon-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

Indexed as

Diagnostic ImagingImage Processing, Computer-AssistedPrecision MedicineSoftwareHeartHumansArtificial intelligencecardiac imagingdeep learningimage segmentationmachine learning

Identifiers

PMID28277804
PMCPMC5450918
OpenAlexW2591746411

What OpenQuestion holds

Textmetadata
LicenceTDM
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.