Evidence map›Paper›PMID 38053612›Full record

ReviewOman medical journal2023

Use of Artificial Intelligence in Coronary Artery Calcium Scoring.

Mia Koponen, Waqas Anwaar, Habib-ur-Rahman3, Qasim Sheikh, Fouzia Sadiq

Open access · diamondAbstract readReview
In one paragraph

Review in Oman medical journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 42% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. 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

5 authors at 3 institutions in 2 countries.

Mia KoponenSchool of Medical Sciences, University of Aberdeen, Aberdeen, UK.
Waqas AnwaarDepartment of Computing, Shifa School of Computing, Shifa Tameer-e-Millat University, Islamabad, Pakistan.
Habib-ur-Rahman3
Qasim SheikhDepartment of Computing, Shifa School of Computing, Shifa Tameer-e-Millat University, Islamabad, Pakistan.
Fouzia SadiqDirectorate of Research, Shifa Tameer-e-Millat University, Islamabad, Pakistan.
Shifa Tameer-e-Millat University · PKShifa Tameer-e-Millat University · PKUniversity of Aberdeen · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronary artery calcium (CAC) scoring improves traditional risk factor-based coronary heart disease (CHD) risk stratification. Here, the contribution of CAC scoring to a traditional 10-year CHD risk prediction scores and new artificial intelligence methods used to automate CAC scoring were reviewed. Research shows that traditional risk factors tend to overestimate or underestimate the actual risk of CHD, meaning that including CAC score in the risk stratification has potential to reduce over- and undertreatment. The automated CAC scoring methods are shown to be accurate and significantly more time-effective than the commonly used semi-automated method.

Indexed as

Artificial IntelligenceCoronary ArteriesCoronary DiseaseVascular Calcification

Identifiers

PMID38053612
PMCPMC10694408
OpenAlexW4312690173

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.