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CORRECTION article

Front. Med., 14 October 2025

Sec. Pathology

Volume 12 - 2025 | https://doi.org/10.3389/fmed.2025.1718394

This article is part of the Research TopicFrom Black-box to Clarity in Lesion Diagnostics: Clinical Causal Cognition Led Interpretable Diagnostic AI SystemsView all 4 articles

Correction: A new integrated machine learning model: application to improve the accuracy of predicting left atrial appendage thrombus in patients with non-valvular atrial fibrillation

This article is a correction to:

  1. A new integrated machine learning model: application to improve the accuracy of predicting left atrial appendage thrombus in patients with non-valvular atrial fibrillation

    1. Read original article
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A Correction on
A new integrated machine learning model: application to improve the accuracy of predicting left atrial appendage thrombus in patients with non-valvular atrial fibrillation

by Mai, P., Huo, H., Li, X., Zhou, D., He, F., Li, Y., and Wang, H. (2025). Front. Med. 12:1661696. doi: 10.3389/fmed.2025.1661696

The conflict of interest statement was erroneously given as “YL was employed by Yizhun Medical AI Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.”

The correct conflict of interest statement is “YL was employed by Yizhun Medical AI Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor DZ declared a shared parent affiliation of Xi'an Jiaotong University with the author HH at the time of review.”

The original version of this article has been updated.

Keywords: non-valvular atrial fibrillation, transthoracic echocardiography, left atrial appendage thrombosis, machine learning models, transesophageal echocardiography

Citation: Frontiers Production Office (2025) Correction: A new integrated machine learning model: application to improve the accuracy of predicting left atrial appendage thrombus in patients with non-valvular atrial fibrillation. Front. Med. 12:1718394. doi: 10.3389/fmed.2025.1718394

Received: 03 October 2025; Accepted: 03 October 2025;
Published: 14 October 2025.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

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