Evidence map›Paper›PMID 42003287›Full record

ArticleJournal of applied clinical medical physics2026

Mitral regurgitation detection and central/eccentric classification using transformer-based deep learning in multi-view echocardiography.

Xiaofang Zhong, Jiancheng Zhang, Yuanyuan Sheng, Ying Guo, Yumei Yang, Yuxiang Huang, Hongjuan Zhao, Wei Zeng, Lixin Chen, Dong Ni and 3 more

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Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Xiaofang ZhongDepartment of Ultrasound, Shenzhen People's Hospital, (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, China.
Jiancheng ZhangThe National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Yuanyuan ShengDepartment of Ultrasound, Shenzhen People's Hospital, (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, China.
Ying GuoDepartment of Ultrasound, Shenzhen People's Hospital, (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, China.
Yumei YangDepartment of Ultrasound, Shenzhen People's Hospital, (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, China.
Yuxiang HuangDepartment of Ultrasound, Shenzhen People's Hospital, (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, China.
Hongjuan ZhaoDepartment of Ultrasound, Shenzhen People's Hospital, (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, China.
Wei ZengDepartment of Ultrasound, Shenzhen People's Hospital, (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, China.
Lixin ChenDepartment of Ultrasound, Shenzhen People's Hospital, (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, China.
Dong NiThe National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Jinfeng XuDepartment of Ultrasound, Shenzhen People's Hospital, (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, China.
Wufeng XueThe National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Yingying LiuDepartment of Ultrasound, Shenzhen People's Hospital, (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, China.ORCID https://orcid.org/0000-0002-6792-8933

Funding

a Project of Innovation of the Science and Technology Commission of Shenzhen City JCYJ20250604142033004a Project of International cooperative research of the Science and Technology Commission of Shenzhen City GJHZ20210705142205017a Project of International cooperative research of the Science and Technology Commission of Shenzhen City GJHZ20210705142206019National Natural Science Foundation of China 82102041Shenzhen People's Hospital Clinician Scientist Training Plan SYWGSCGZH202302Shenzhen Science and Technology Program 20220810145705001
6 · The paper itself

Abstract

backgroundFully automated diagnostic echocardiography allows for broader screening, earlier diagnosis for patients with mitral regurgitation (MR). This feasibility study developed a deep learning (DL) framework to automatically detect MR in echocardiography videos and classify regurgitation samples into central or eccentric regurgitation.

methodsWe designed a Transformer-based deep learning model for fully automatic Doppler video detection of MR. This framework can automatically detect MR and classify central or eccentric regurgitation. The algorithm was trained, validated, and tested using retrospectively selected studies. A prospective dataset of 217 patients was used as an independent test database.

results(a) The model demonstrated exceptional diagnostic accuracy for MR, achieving accuracy rates of 0.94(95%CI: 0.90-0.97) and 0.92(95%CI: 0.89-0.96) in retrospective and prospective test sets, respectively, with area under the curve (AUC) values of 0.98 (95%CI: 0.96-1.0) and 0.97 (95%CI: 0.95-0.99). Its performance aligned with that of middle seniority physicians. (b) The model achieved an accuracy of 0.93 in identifying central and eccentric MR across both retrospective and prospective test sets, AUC values of 0.96 (95%CI: 0.89-0.99) and 0.95 (95%CI: 0.89-0.98), respectively. (c) On the tasks of MR, the diagnostic performance of the model in multiple views was better than that of the parasternal long-axis view (PLAX) alone or the apical four-chamber view (A4C) in both retrospective and prospective datasets.

conclusionThe Transformer-based algorithm proposed in this study can automate and improve the efficiency of clinical workflows, screen multi-view for the presence of MR, and assist in classifying regurgitation characteristics.

Indexed as

AlgorithmsDeep LearningEchocardiographyImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedMitral Valve InsufficiencyFeasibility StudiesHumansProspective StudiesRetrospective Studiescentraleccentricmitral regurgitationmulti‐viewtransformer

Identifiers

PMID42003287
PMCPMC13092907

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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.