Evidence map›Paper›PMID 31489585›Full record

ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2019

Leveraging latest computer science tools to advance nuclear cardiology.

Piotr Slomka

Abstract readEditorial
PubMed Publisher
In one paragraph

Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Rise and fall, and provisional rebirth of exercise stress testing at the dawn of the third millennium.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2021
    Article
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

1 author.

Piotr SlomkaDepartment of Medicine, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. A047N, Los Angeles, CA, 90048, USA. slomkap@cshs.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nuclear cardiology has unique advantages compared to other modalities, since the image analysis is already much more automated compared to what is currently clinically performed for CT, MR, or echocardiography imaging. The diverse image and clinical data available to assess coronary disease function, perfusion, flow, and associated CT data provide new opportunities, but logistically these additional assessments increase the overall complexity of SPECT/PET reporting, necessitating additional expertise and time. The advances in artificial intelligence software can be leveraged to obtain comprehensive risk predictions and diagnoses from all available data. They will allow nuclear cardiology to retain competitive edge compared to other modalities and improve its overall clinical utility. These tools will enhance diagnosis and risk prediction beyond what is possible by subjective visual analysis and mental integration of data by physicians.

Indexed as

Artificial IntelligenceCardiologyCoronary Artery DiseaseDiagnostic ImagingEchocardiographyHumansNuclear MedicinePerfusionPositron-Emission TomographyReproducibility of ResultsRiskRisk AssessmentSoftwareTomography, Emission-Computed, Single-Photon

Identifiers

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.