Front. Microbiol. Frontiers in Microbiology Front. Microbiol. 1664-302X Frontiers Media S.A. 10.3389/fmicb.2025.1607769 Microbiology Correction Corrigendum: Bioinformatic analysis reveals the association between bacterial morphology and antibiotic resistance using light microscopy with deep learning Ikebe Miki 1 2 ‡ Aoki Kota 1 * † ‡ Hayashi-Nishino Mitsuko 1 2 3 * Furusawa Chikara 4 5 Nishino Kunihiko 1 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

Approved by: Frontiers Editorial Office, Frontiers Media SA, Switzerland

 *Correspondence: Kota Aoki, aoki.k@tottori-u.ac.jp Mitsuko Hayashi-Nishino, mnishino@sanken.osaka-u.ac.jp Kunihiko Nishino, nishino@sanken.osaka-u.ac.jp

†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

 17 04 2025 2025 16 1607769 08 04 2025 09 04 2025 Copyright © 2025 Ikebe, Aoki, Hayashi-Nishino, Furusawa and Nishino. 2025 Ikebe, Aoki, Hayashi-Nishino, Furusawa and Nishino

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 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 antibiotic resistance light microscopy bacterial morphology deep learning bioinformatic analysis section-at-acceptance Infectious Agents and Disease

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.

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