CORRECTION article

Front. Microbiol., 17 April 2025

Sec. Infectious Agents and Disease

Volume 16 - 2025 | https://doi.org/10.3389/fmicb.2025.1607769

Corrigendum: Bioinformatic analysis reveals the association between bacterial morphology and antibiotic resistance using light microscopy with deep learning

This article is a correction to:

  1. Bioinformatic analysis reveals the association between bacterial morphology and antibiotic resistance using light microscopy with deep learning

    1. Read original article
Miki Ikebe,&#x;Miki Ikebe1,2‡Kota Aoki &#x;&#x;Kota Aoki1*†‡Mitsuko Hayashi-Nishino,, Mitsuko Hayashi-Nishino1,2,3*Chikara Furusawa,Chikara Furusawa4,5Kunihiko Nishino,, Kunihiko Nishino1,2,6*
  • 1SANKEN (Institute of Scientific and Industrial Research), Osaka University, Osaka, Japan
  • 2Graduate School of Pharmaceutical Sciences, Osaka University, Suita, Japan
  • 3Artificial Intelligence Research Center (AIRC-SANKEN), Osaka University, Osaka, Japan
  • 4Center for Biosystems Dynamics Research, RIKEN, Suita, Japan
  • 5Universal Biology Institute, The University of Tokyo, Tokyo, Japan
  • 6Center for Infectious Disease Education and Research, Osaka University, Osaka, Japan

A Corrigendum on
Bioinformatic analysis reveals the association between bacterial morphology and antibiotic resistance using light microscopy with deep learning

by Ikebe, M., Aoki, K., Hayashi-Nishino, M., Furusawa, C., and Nishino, K. (2024). Front. Microbiol. 15:1450804. doi: 10.3389/fmicb.2024.1450804

In the published article, there was an error in the Data availability statement. It was incorrectly stated that the names of the repository (and accession number) can be found in the article or Supplementary material. The correct Data Availability statement appears below.

Data availability statement

The datasets presented in this study can be found in the online repository; https://doi.org/10.6084/m9.figshare.c.7757147.v1.

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: antibiotic resistance, light microscopy, bacterial morphology, deep learning, bioinformatic analysis

Citation: Ikebe M, Aoki K, Hayashi-Nishino M, Furusawa C and Nishino K (2025) Corrigendum: Bioinformatic analysis reveals the association between bacterial morphology and antibiotic resistance using light microscopy with deep learning. Front. Microbiol. 16:1607769. doi: 10.3389/fmicb.2025.1607769

Received: 08 April 2025; Accepted: 09 April 2025;
Published: 17 April 2025.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2025 Ikebe, Aoki, Hayashi-Nishino, Furusawa and Nishino. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Kota Aoki, YW9raS5rQHRvdHRvcmktdS5hYy5qcA==; Mitsuko Hayashi-Nishino, bW5pc2hpbm9Ac2Fua2VuLm9zYWthLXUuYWMuanA=; Kunihiko Nishino, bmlzaGlub0BzYW5rZW4ub3Nha2EtdS5hYy5qcA==

†Present address: Kota Aoki Department of Electrical Engineering and Computer Science, Faculty of Engineering, Tottori University, Tottori, Japan

‡These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.