Evidence map›Paper›PMID 36512047›Full record

ArticleEuropean radiology2023

Deep learning based on carotid transverse B-mode scan videos for the diagnosis of carotid plaque: a prospective multicenter study.

Jia Liu, Xinrui Zhou, Hui Lin, Xue Lu, Jian Zheng, Erjiao Xu, Dianhu Jiang, Hui Zhang, Xin Yang, Junlin Zhong and 12 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 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

22 authors.

Jia LiuThe Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Tianhe District, Guangzhou, 510630, China.
Xinrui ZhouSchool of Biomedical Engineering, Health Science Center, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen, 518073, China.
Hui LinThe Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Tianhe District, Guangzhou, 510630, China.
Xue LuThe Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Tianhe District, Guangzhou, 510630, China.
Jian ZhengLonggang District People's Hospital, Shenzhen, China.
Erjiao XuThe Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, China.
Dianhu JiangThe Second People's Hospital of Foshan, Foshan, China.
Hui ZhangThe Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Tianhe District, Guangzhou, 510630, China.
Xin YangSchool of Biomedical Engineering, Health Science Center, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen, 518073, China.
Junlin ZhongThe Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Tianhe District, Guangzhou, 510630, China.
Xindi HuShenzhen RayShape Medical Technology Co. Ltd., Shenzhen, China.
Yuhao HuangSchool of Biomedical Engineering, Health Science Center, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen, 518073, China.
Yanling ZhangThe Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Tianhe District, Guangzhou, 510630, China.
Jiamin LiangSchool of Biomedical Engineering, Health Science Center, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen, 518073, China.
Qin LiuSchool of Biomedical Engineering, Health Science Center, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen, 518073, China.
Min ZhongLonggang District People's Hospital, Shenzhen, China.
Yuansen ChenThe Third People's Hospital of Longgang District, Shenzhen, China.
Huixiang YanThe Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, China.
Haowen DengThe Second People's Hospital of Foshan, Foshan, China.
Rongqin ZhengThe Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Tianhe District, Guangzhou, 510630, China.
Dong NiSchool of Biomedical Engineering, Health Science Center, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen, 518073, China. nidong@szu.edu.cn.
Jie RenThe Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Tianhe District, Guangzhou, 510630, China. renj@mail.sysu.edu.cn.

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2021A1515110553Guangzhou Science and Technology Project 202102010255National Natural Science Foundation of China 81971632Natural Science Foundation of Guangdong Province 2020A1515010425Shenzhen Longgang District Economic and Technological Development Special Fund Project for Medical and Health Technology LGWJ2021-（036）
6 · The paper itself

Abstract

objectivesAccurate detection of carotid plaque using ultrasound (US) is essential for preventing stroke. However, the diagnostic performance of junior radiologists (with approximately 1 year of experience in carotid US evaluation) is relatively poor. We thus aim to develop a deep learning (DL) model based on US videos to improve junior radiologists' performance in plaque detection.

methodsThis multicenter prospective study was conducted at five hospitals. CaroNet-Dynamic automatically detected carotid plaque from carotid transverse US videos allowing clinical detection. Model performance was evaluated using expert annotations (with more than 10 years of experience in carotid US evaluation) as the ground truth. Model robustness was investigated on different plaque characteristics and US scanning systems. Furthermore, its clinical applicability was evaluated by comparing the junior radiologists' diagnoses with and without DL-model assistance.

resultsA total of 1647 videos from 825 patients were evaluated. The DL model yielded high performance with sensitivities of 87.03% and 94.17%, specificities of 82.07% and 74.04%, and areas under the receiver operating characteristic curve of 0.845 and 0.841 on the internal and multicenter external test sets, respectively. Moreover, no significant difference in performance was noted among different plaque characteristics and scanning systems. Using the DL model, the performance of the junior radiologists improved significantly, especially in terms of sensitivity (largest increase from 46.3 to 94.44%).

conclusionsThe DL model based on US videos corresponding to real examinations showed robust performance for plaque detection and significantly improved the diagnostic performance of junior radiologists. KEY POINTS: • The deep learning model based on US videos conforming to real examinations showed robust performance for plaque detection. • Computer-aided diagnosis can significantly improve the diagnostic performance of junior radiologists in clinical practice.

Indexed as

Deep LearningCarotid ArteriesDiagnosis, Computer-AssistedHumansProspective StudiesUltrasonographyAtherosclerosisCarotid arteriesComputer-assisted image interpretationDeep learningUltrasonography

Identifiers

PMID36512047

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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.