Evidence map›Paper›PMID 41574032›Full record

ArticleEuropean heart journal. Digital health2026

Pathological classification of non-ischaemic dilated cardiomyopathy based on deep learning.

Hao Jia, Yifan Wang, Zhimin Lv, Yiqi Zhao, Ningning Zhang, Xiulin Zhang, Wentao Wang, Yihang Feng, Weiteng Wang, Hao Cui and 9 more

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. Article
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

19 authors.

Hao JiaDepartment of Cardiac Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yifan WangDepartment of Cardiac Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Zhimin LvBeijing Key Laboratory of Preclinical Research and Evaluation for Cardiovascular Implant Materials, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yiqi ZhaoDepartment of Cardiac Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Ningning ZhangState Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xiulin ZhangBeijing Key Laboratory of Preclinical Research and Evaluation for Cardiovascular Implant Materials, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Wentao WangDepartment of Cardiac Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yihang FengBeijing Key Laboratory of Preclinical Research and Evaluation for Cardiovascular Implant Materials, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Weiteng WangDepartment of Cardiac Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0000-0001-6480-9325
Hao CuiBeijing Key Laboratory of Preclinical Research and Evaluation for Cardiovascular Implant Materials, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yuyang LiuSchool of Basic Medicial Sciences, Capital Medical University, Beijing, China.
Zheng GaoSchool of Basic Medicial Sciences, Capital Medical University, Beijing, China.
Han MoShenzhen Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences, Shenzhen, China.
Han HanDepartment of Cardiac Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yuhong HuDepartment of Cardiac Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xijia ShaoDepartment of Cardiac Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xiao ChenState Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Daniel ReichartDepartment of Cardiology, University Hospital, LMU Munich, Munich, Germany.
Jiangping SongDepartment of Cardiac Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0000-0002-9488-7823

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Non-ischaemic dilated cardiomyopathy (NIDCM) is a major cause of heart failure (HF) and heart transplantation (HTx), characterized by heterogeneity in aetiology, clinical phenotype, and disease progression. Nevertheless, precision medicine-based diagnostics and treatment strategies for NIDCM remain lacking. This proof-of-concept study aimed to stratify NIDCM patients by pathological features and identify those at high-risk for malignant arrhythmia (MA) and rapid progression to end-stage HF. Methods and results: 293 NIDCM-HTx patients were included in this study. A total of 3516 heart tissue slides from six representative sites of each patient were analyzed using deep learning-based computational pathology (DL-CPath) and unsupervised clustering to identify pathological subgroups (PGs): PGA, PGB, and PGC. PGA was characterized by interstitial fibrosis, cardiomyocyte vacuolization, microvascular intimal hyperplasia, and myocyte disarray, and had the highest rates of MA ( Conclusion: DL-based pathological classification effectively extracted clinically-meaningful imaging features and enabled the identification of high-risk NIDCM subgroup. Each PG exhibited unique histopathological and clinical characteristics, highlighting distinct phenotypes and risk profiles.

Indexed as

ArrhythmiaComputational pathologyDeep learningDisease progressionNon-ischaemic dilated cardiomyopathy

Identifiers

PMID41574032
PMCPMC12821066

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Registered trials

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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.