Evidence map›Paper›PMID 34564743›Full record

ArticleEuropean radiology2022

Do plaque-related factors affect the diagnostic performance of an artificial intelligence coronary-assisted diagnosis system? Comparison with invasive coronary angiography.

Jie Xu, Linli Chen, Xiaojia Wu, Chuanming Li, Guangyong Ai, Yuexi Liu, Bitong Tian, Dajing Guo, Zheng Fang

Abstract read
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In one paragraph

Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 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

5 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Observational
  4. Review
  5. 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

9 authors.

Jie Xu *Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Linli Chen *Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Xiaojia WuDepartment of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Chuanming LiDepartment of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Guangyong AiDepartment of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Yuexi LiuDepartment of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Bitong TianDepartment of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Dajing GuoDepartment of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China. guodaj@163.com.ORCID http://orcid.org/0000-0001-8655-6621
Zheng FangDepartment of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China. wwwfans2000@hospital.cqmu.edu.cn.

Funding

the Kuanren Talents Program of the Second Affiliated Hospital of Chongqing Medical University 2020-7the Kuanren Talents Program of the Second Affiliated Hospital of Chongqing Medical University 2021-24
6 · The paper itself

Abstract

objectiveThe aim of this study was to investigate the effects of plaque-related factors on the diagnostic performance of an artificial intelligence coronary-assisted diagnosis system (AI-CADS).

methodsPatients who underwent coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) were retrospectively included in this study. The degree of stenosis in each vessel was collected from CCTA and ICA, and the information on plaque-related factors (plaque length, plaque type, and coronary artery calcium score (CAC)) of the vessels with plaques was collected from CCTA.

resultsIn total, 1224 vessels in 306 patients (166 men; 65.7 ± 10.1 years) were analyzed. Of these, 391 vessels in 249 patients showed significant stenosis using ICA as the gold standard. Using per-vessel as the unit, the area under the curves of coronary stenosis ≥ 50% for AI-CADS, doctor, and AI-CADS + doctor was 0.764, 0.837, and 0.853, respectively. The accuracies in interpreting the degree of coronary stenosis were 56.0%, 68.1%, and 71.2%, respectively. Seven hundred fifty vessels showed plaques on CCTA; plaque type did not affect the interpretation results by AI-CADS (chi-square test: p = 0.0093; multiple logistic regression: p = 0.4937). However, the interpretation results for plaque length (chi-square test: p < 0.0001; multiple logistic regression: p = 0.0061) and CACs (chi-square test: p < 0.0001; multiple logistic regression: p = 0.0001) were significantly different.

conclusionAI-CADS has an ability to distinguish ≥ 50% coronary stenosis, but additional manual interpretation based on AI-CADS is necessary. The plaque length and CACs will affect the diagnostic performance of AI-CADS. KEY POINTS: • AI-CADS can help radiologists quickly assess CCTA and improve diagnostic confidence. • Additional manual interpretation on the basis of AI-CADS is necessary. • The plaque length and CACs will affect the diagnostic performance of AI-CADS.

Indexed as

Coronary Artery DiseaseCoronary StenosisArtificial IntelligenceComputed Tomography AngiographyCoronary AngiographyCoronary VesselsHumansMalePredictive Value of TestsRetrospective StudiesArtificial intelligenceComputed tomography angiographyCoronary stenosis

Identifiers

PMID34564743

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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.