Evidence map›Paper›PMID 41574037›Full record

ArticleEuropean heart journal. Digital health2026

Automated estimation of computed tomography-derived left ventricular mass using sex-specific 12-lead ECG-based temporal convolutional network.

Heng-Yu Pan, Benny Wei-Yun Hsu, Chun-Ti Chou, Yuan-Yuan Hsu, Chih-Kuo Lee, Wen-Jeng Lee, Tai-Ming Ko, Vincent S Tseng, Tzung-Dau Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European heart journal. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Review
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.

Heng-Yu PanDivision of Cardiology, Department of Internal Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu City, Taiwan.
Benny Wei-Yun HsuDepartment of Computer Science, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd, E District, Hsin-Chu City, 300093, Taiwan.
Chun-Ti ChouDepartment of Computer Science, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd, E District, Hsin-Chu City, 300093, Taiwan.
Yuan-Yuan HsuDepartment of Computer Science, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd, E District, Hsin-Chu City, 300093, Taiwan.
Chih-Kuo LeeDivision of Cardiology, Department of Internal Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu City, Taiwan.ORCID https://orcid.org/0000-0001-5796-6971
Wen-Jeng LeeDepartment of Medical Imaging, National Taiwan University Hospital, Taipei City, Taiwan.
Tai-Ming KoDepartment of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsin-Chu City, Taiwan.
Vincent S TsengDepartment of Computer Science, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd, E District, Hsin-Chu City, 300093, Taiwan.
Tzung-Dau WangCardiovascular Center and Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, No. 7, Zhong-Shan South Road, Taipei City 100225, Taiwan.ORCID https://orcid.org/0000-0002-7180-3607

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: To propose a novel deep learning-based method, the eLVMass-Net, for the estimation of left ventricular mass (LVM) based on 12-lead electrocardiograms (ECGs). Methods and results: We developed a deep learning model for LVM estimation using raw ECG signals, demographic data, and ECG parameters as input by using TW-CVAI dataset ( Conclusion: The proposed eLVMass-Net outperformed previously published approaches by ECG pre-processing with synchronized single heartbeat extraction and TCN as ECG encoder. Additionally, the development of sex-specific models proved to be a rational approach.

Indexed as

Cardiac imagingDeep learningElectrocardiogramLeft ventricular massSex-specific modelsTemporal convolutional network

Identifiers

PMID41574037
PMCPMC12821057

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.