Evidence map›Paper›PMID 40985001›Full record

ArticleEuropean heart journal. Digital health2025

Artificial intelligence-based accurate myocardial infarction mapping using 12-lead electrocardiography.

Hui Wang, Zhifan Gao, Heye Zhang, Yuzhen Zhu, Shichang Lian, Kairui Bo, Shuang Li, Yifeng Gao, Baiyan Zhuang, Zhen Zhou and 4 more

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In one paragraph

Article in European heart journal. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Hui WangDepartment of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China.ORCID https://orcid.org/0000-0002-8549-3398
Zhifan GaoSchool of Biomedical Engineering, Sun Yat-sen University, Guangdong, China.
Heye ZhangSchool of Biomedical Engineering, Sun Yat-sen University, Guangdong, China.
Yuzhen ZhuSchool of Biomedical Engineering, Sun Yat-sen University, Guangdong, China.
Shichang LianSchool of Biomedical Engineering, Sun Yat-sen University, Guangdong, China.
Kairui BoDepartment of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China.
Shuang LiDepartment of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China.
Yifeng GaoDepartment of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China.
Baiyan ZhuangDepartment of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China.
Zhen ZhouDepartment of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China.
Xinwei ZhangDepartment of Radiology, Linyi People's Hospital, Linyi, China.
Cuiyan WangDepartment of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong.
Koen NiemanDepartment of Radiology, Stanford University School of Medicine and Cardiovascular Institute, Stanford, CA, USA.ORCID https://orcid.org/0000-0002-8312-0598
Lei XuDepartment of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China.ORCID https://orcid.org/0000-0002-8499-0448

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Assessing myocardial fibrosis (MF) in patients with prior myocardial infarction (MI) is crucial for prognosis. Artificial intelligence-assisted electrocardiography (AI-ECG) has a great potential to detect MF. However, training a precise AI-ECG model requires voluminous ECGs. A biosimulation model may be an efficient substitution. This study aimed to develop and validate a novel artificial intelligence-assisted method using 12-lead electrocardiography (AI-MI-12ECG). Methods and results: The AI-MI-12ECG was trained by a biosimulation model to visualize the presence, location, and size of MF in post-MI patients. A total of 182 post-MI patients were included in this prospective study. The MF detected by AI-MI-12ECG and the cardiologist were compared with the late gadolinium-enhanced (LGE) area of cardiac magnetic resonance (CMR). The results show that AI-MI-12ECG exhibited strong correlation with LGE in identifying the MI location ( Conclusion: The AI-MI-12ECG trained using the biosimulation model in post-MI patients was adequately aligned with CMR-LGE. This highlights its potential for accurate detection of fibrosis and identification of individuals with significant infarct burdens.

Indexed as

12-Lead electrocardiographyArtificial intelligenceBiosimulationLate gadolinium-enhancedMyocardial fibrosis

Identifiers

PMID40985001
PMCPMC12450511

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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.