Evidence map›Paper›PMID 31728692›Full record

Observational studyEuropean radiology2020

Evaluation of an AI-based, automatic coronary artery calcium scoring software.

Mårten Sandstedt, Lilian Henriksson, Magnus Janzon, Gusten Nyberg, Jan Engvall, Jakob De Geer, Joakim Alfredsson, Anders Persson

Abstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing 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

42 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

8 authors.

Mårten SandstedtCenter for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden. martensandstedt@gmail.com.ORCID http://orcid.org/0000-0002-0973-9499
Lilian HenrikssonCenter for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
Magnus JanzonDepartment of Cardiology and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.
Gusten NybergCenter for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
Jan EngvallCenter for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
Jakob De GeerCenter for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
Joakim AlfredssonDepartment of Cardiology and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.
Anders PerssonCenter for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo evaluate an artificial intelligence (AI)-based, automatic coronary artery calcium (CAC) scoring software, using a semi-automatic software as a reference.

methodsThis observational study included 315 consecutive, non-contrast-enhanced calcium scoring computed tomography (CSCT) scans. A semi-automatic and an automatic software obtained the Agatston score (AS), the volume score (VS), the mass score (MS), and the number of calcified coronary lesions. Semi-automatic and automatic analysis time were registered, including a manual double-check of the automatic results. Statistical analyses were Spearman's rank correlation coefficient (⍴), intra-class correlation (ICC), Bland Altman plots, weighted kappa analysis (κ), and Wilcoxon signed-rank test.

resultsThe correlation and agreement for the AS, VS, and MS were ⍴ = 0.935, 0.932, 0.934 (p < 0.001), and ICC = 0.996, 0.996, 0.991, respectively (p < 0.001). The correlation and agreement for the number of calcified lesions were ⍴ = 0.903 and ICC = 0.977 (p < 0.001), respectively. The Bland Altman mean difference and 1.96 SD upper and lower limits of agreements for the AS, VS, and MS were - 8.2 (- 115.1 to 98.2), - 7.4 (- 93.9 to 79.1), and - 3.8 (- 33.6 to 25.9), respectively. Agreement in risk category assignment was 89.5% and κ = 0.919 (p < 0.001). The median time for the semi-automatic and automatic method was 59 s (IQR 35-100) and 36 s (IQR 29-49), respectively (p < 0.001).

conclusionsThere was an excellent correlation and agreement between the automatic software and the semi-automatic software for three CAC scores and the number of calcified lesions. Risk category classification was accurate but showing an overestimation bias tendency. Also, the automatic method was less time-demanding. KEY POINTS: • Coronary artery calcium (CAC) scoring is an excellent candidate for artificial intelligence (AI) development in a clinical setting. • An AI-based, automatic software obtained CAC scores with excellent correlation and agreement compared with a conventional method but was less time-consuming.

Indexed as

Artificial IntelligenceSoftwareCalciumCoronary AngiographyCoronary Artery DiseaseCoronary VesselsCross-Sectional StudiesFemaleHumansMaleMiddle AgedStatistics, NonparametricTomography, X-Ray ComputedCalciumArtificial intelligenceCoronary artery diseaseMultidetector computed tomographySoftware

Identifiers

PMID31728692
PMCPMC7033052

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Registered trials

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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.