Evidence map›Paper›PMID 40040863›Full record

ArticleEClinicalMedicine2025

Development of an MRI based artificial intelligence model for the identification of underlying atrial fibrillation after ischemic stroke: a multicenter proof-of-concept analysis.

Zijie Zhang, Yang Ding, Kaibin Lin, Wenli Ban, Luyue Ding, Yudong Sun, Chuanliang Fu, Yihang Ren, Can Han, Xue Zhang and 14 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EClinicalMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

24 authors.

Zijie ZhangHeart Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yang DingSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Kaibin LinHeart Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Wenli BanSchool of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Luyue DingSchool of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Yudong SunSchool of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Chuanliang FuSchool of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Yihang RenSchool of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Can HanSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Xue ZhangHeart Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiaoer WeiDepartment of Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shundong HuDepartment of Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yuwu ZhaoDepartment of Neurology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Li CaoDepartment of Neurology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jun WangSchool of Computer and Computational Science, Hangzhou City University, Hangzhou, China.
Saman NazarianSection for Cardiac Electrophysiology, Department of Medicine/Cardiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Ying CaoDepartment of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
Lan ZhengDepartment of Neurology, Minhang Hospital, Fudan University, Shanghai, China.
Min ZhangDepartment of Neurology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
Jianliang FuDepartment of Neurology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
Jingbo LiHeart Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiang HanDepartment of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
Dahong QianSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Dong HuangHeart Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Atrial fibrillation (AF) represents a major risk factor of ischemic stroke recurrence with serious management implications. However, it often remains undiagnosed due to lack of standard or prolonged cardiac rhythm monitoring. We aim to create a novel end-to-end artificial intelligence (AI) model that uses MRI data to rapidly identify high AF risk in patients who suffer from an acute ischemic stroke. Methods: This study comprises an internal retrospective cohort and a prospective cohort from Shanghai sixth people's hospital to train and validate an MRI-based AI model. Between January 1, 2018 and December 31, 2021, 510 patients were retrospectively enrolled for algorithm development and performance was measured using fivefold cross-validation. Patients from this trial were registered with http://www.chictr.org.cn, ChiCTR2200056385. Between September 1, 2022 and July 31, 2023, 73 patients were prospectively enrolled for algorithm test. An external cohort of 175 patients from Huashan Hospital, Minhang Hospital, and Shanghai Tenth People's Hospital was also enrolled retrospectively for further model validation. A combined classifier leveraging pre-defined radiomics features and Findings: The top-performing combined classifier achieved an AUC of 0.94 (95% CI, 0.90-0.98) in the internal retrospective validation group, 0.85 (95% CI, 0.79-0.91) in the external validation group, and 0.87 (95% CI, 0.90-0.98) in the prospective test group. Based on subgroup analysis, the AI model performed well in female patients, patients with NIHSS > 4 or CHA Interpretation: Our work suggested a potential association between brain ischemic lesion pattern on MR images and underlying AF. Furthermore, with additional validation, the AI model we developed may serve as a rapid screening tool for AF in clinical practice of stroke units. Funding: This work was supported by grants from the National Natural Science Foundation of China (NSFC, Grant Number: 81871102 and 82172068); Shanghai Jiao Tong University School of Medicine, Two-Hundred Talent Program as Research Doctor (Grant Number: SBR202204); Municipal Science and Technology Commission Medical Innovation Project of Shanghai, (Grant/Award Number: 20Y11910200); Research Physician Program of Shanghai Shen Kang Hospital Development Center (Grant Number: SHD2022CRD039) to Dr. Dong Huang and the SJTU Trans-med Awards Research (No. 20220101) to Dahong Qian.

Indexed as

Atrial fibrillationConvolutional neural networkIschemic strokeRadiomics

Identifiers

PMID40040863
PMCPMC11876936

What OpenQuestion holds

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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.