Evidence map›Paper›PMID 30820803›Full record

ArticleThe international journal of cardiovascular imaging2019

VASIM: an automated tool for the quantification of carotid atherosclerosis by computed tomography angiography.

Florentino Luciano Caetano Dos Santos, Marcin Kolasa, Mitsugu Terada, Juha Salenius, Hannu Eskola, Michelangelo Paci

Abstract read
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In one paragraph

Article in The international journal of cardiovascular imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.0field-weighted citation impact, top 22% 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

5 citing papers in PubMed, 11 citations in OpenAlex.

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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 4 institutions in 2 countries.

Florentino Luciano Caetano Dos SantosBioMediTech, Faculty of Medicine and Health Technology, Tampere University, SM312, Korkeakoulunkatu 3, 33720, Tampere, Finland. florentino.l.c.santos@ieee.org.ORCID http://orcid.org/0000-0001-8151-3585
Marcin KolasaDepartment of Radiology, Medical Imaging Center, Tampere University Hospital, Tampere, Finland.ORCID http://orcid.org/0000-0003-3782-4938
Mitsugu TeradaDepartment of Applied Physics, Faculty of Science, Fukuoka University, Fukuoka, Japan.
Juha SaleniusDivision of Vascular Surgery, Department of Surgery, Tampere University Hospital and Medical School, Tampere, Finland.ORCID http://orcid.org/0000-0002-4197-9820
Hannu EskolaBioMediTech, Faculty of Medicine and Health Technology, Tampere University, SM312, Korkeakoulunkatu 3, 33720, Tampere, Finland.ORCID http://orcid.org/0000-0003-1673-2305
Michelangelo PaciBioMediTech, Faculty of Medicine and Health Technology, Tampere University, SM312, Korkeakoulunkatu 3, 33720, Tampere, Finland.ORCID http://orcid.org/0000-0003-0510-1444
Tampere University · FITampere University Hospital · FIFukuoka University · JPTampere University of Applied Sciences · FI

Funding

Biotieteiden ja Ympäristön Tutkimuksen Toimikunta 307967CIMO KM-12-8107Competitive State Research Financing of the Expert Responsibility Area of Tampere University Hospital R07210/ 9K115Etelä-Pohjanmaan Rahasto 160735
6 · The paper itself

Abstract

The diagnostic imaging techniques currently used to evaluate the arterial atherosclerosis hinge on the manual marking and calculation of the stenosis degree. However, the manual assessment is highly dependent on the operator and characterized by low replicability. The study aimed to develop a fully-automated tool for the segmentation and analysis of atherosclerosis in the extracranial carotid arteries. The dataset consisted of 59 randomly-chosen individuals who had undergone head-and-neck computed tomography angiography (CTA), at the Tampere University Hospital, Tampere, Finland. The analysis algorithm was mainly based on the detection of carotid arteries, delineation of the vascular wall, and extraction of the atherosclerotic plaque. To improve the vascular detection rate, the model-based and volume-wide analytical approaches were deployed. A new fully-automated vascular imaging (VASIM) software tool was developed. For stenosis over 50%, the success rate was 83% for the detection and segmentation. Specificity and sensitivity of the algorithm were 25% and 83%, respectively. The overall accuracy was 71%. The VASIM tool is the first published approach for the fully-automated analysis of atherosclerosis in extracranial carotid arteries. The tool provides new outputs, which may help with the quantitative and qualitative, clinical evaluation of the atherosclerosis burden and evolution. The findings from this study provide a basis for the further development of automated atherosclerosis diagnosis and plaque analysis with CTA.

Indexed as

Plaque, AtheroscleroticAdultAgedAged, 80 and overAlgorithmsAutomationCarotid ArteriesCarotid StenosisChildComputed Tomography AngiographyFemaleFinlandHumansMaleMiddle AgedMultidetector Computed TomographyAngiographyCarotid atherosclerosisComputer-assisted diagnosisComputer-assisted image analysis

Identifiers

PMID30820803
OpenAlexW2918745879

What OpenQuestion holds

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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.