Evidence map›Paper›PMID 39694017›Full record

ArticleCell reports. Medicine2024

Foundation model of ECG diagnosis: Diagnostics and explanations of any form and rhythm on ECG.

Yuanyuan Tian, Zhiyuan Li, Yanrui Jin, Mengxiao Wang, Xiaoyang Wei, Liqun Zhao, Yunqing Liu, Jinlei Liu, Chengliang Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports. Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

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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

9 authors.

Yuanyuan TianState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address: tian102@sjtu.edu.cn.
Zhiyuan LiState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China.
Yanrui JinState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China.
Mengxiao WangState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China.
Xiaoyang WeiState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China.
Liqun ZhaoDepartment of Cardiology, Shanghai First People's Hospital Affiliated to Shanghai Jiao Tong University, Shanghai 200080, China.
Yunqing LiuState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China.
Jinlei LiuState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China.
Chengliang LiuState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address: profclliu@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We propose a knowledge-enhanced electrocardiogram (ECG) diagnosis foundation model (KED) that utilizes large language models to incorporate domain-specific knowledge of ECG signals. This model is trained on 800,000 ECGs from nearly 160,000 unique patients. Despite being trained on single-center data, KED demonstrates exceptional zero-shot diagnosis performance across various regions, including different locales in China, the United States, and other regions. This performance spans across all age groups for various conditions such as morphological abnormalities, rhythm abnormalities, conduction blocks, hypertrophy, myocardial ischemia, and infarction. Moreover, KED exhibits robust performance on diseases it has not encountered during its training. When compared to three experienced cardiologists on real clinical datasets, the model achieves comparable performance in zero-shot diagnosis of seven common clinical ECG types. We concentrate on the zero-shot diagnostic capability and the generalization performance of the proposed ECG foundation model, particularly in the context of external multi-center data and previously unseen disease.

Indexed as

ElectrocardiographyAdolescentAdultAgedAged, 80 and overChildChild, PreschoolFemaleHumansInfantInfant, NewbornMaleMiddle AgedYoung Adultcardiovascular diseasescontrast learningelectrocardiogramsfoundation modelknowledge enhancementmultimodalsignal-language modelzero-shot diagnosis

Identifiers

PMID39694017
PMCPMC11722092

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.