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CORRECTION article

Front. Artif. Intell., 08 January 2026

Sec. Medicine and Public Health

Volume 8 - 2025 | https://doi.org/10.3389/frai.2025.1764066

Correction: Machine learning-based detection of cognitive decline using SSWTRT: classification performance and decision analysis

This article is a correction to:

  1. Machine learning-based detection of cognitive decline using SSWTRT: classification performance and decision analysis

    1. Read original article
Yuji Nozaki Yuji Nozaki1*Chihiro Kamohara,Chihiro Kamohara2,3Ryota AbeRyota Abe1Taiki IedaTaiki Ieda1Madoka NakajimaMadoka Nakajima3Maki SakamotoMaki Sakamoto1
  • 1Department of Informatics,Graduate School of Informatics and Engineering, The University of Electro-Communications, Chofu, Japan
  • 2Research Institute for Diseases of Old Age, Juntendo University School of Medicine, Tokyo, Japan
  • 3Department of Neurosurgery, Juntendo University School of Medicine, Tokyo, Japan

A Correction on
Machine learning-based detection of cognitive decline using SSWTRT: classification performance and decision analysis

by Nozaki, Y., Kamohara, C., Abe, R., Ieda, T., Nakajima, M., and Sakamoto, M. (2025). Front. Artif. Intell. 8:1689182. doi: 10.3389/frai.2025.1689182

In the original article, there was a mistake in Figure 9 as published.

The subpanels (a) and (d) were erroneously interchanged causing the displayed order of panels (a, b, c, d) to differ from the intended structure described in the article text.

The corrected figure image is provided below.

Figure 9
Four radar plots display model outcomes. Plot (a) shows correctly classified Class 1 samples, while plot (b) shows correctly classified Class 0 samples. Plot (c) depicts misclassified Class 1 samples, and plot (d) depicts misclassified Class 0 samples. Each plot compares model output values against features like score images, age, and years of education.

Figure 9. SHAP decision plots generated from the Random Forest classifier. The plots show feature contributions for all test samples stratified by classification outcome: (a) correctly classified samples of Class 1, (b) correctly classified samples of Class 0, (c) misclassified samples of Class 1, and (d) misclassified samples of Class 0.

The original article has been updated.

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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: sound symbolic words, texture recognition, dementia, neuropsychological tests, machine learning, SHAP

Citation: Nozaki Y, Kamohara C, Abe R, Ieda T, Nakajima M and Sakamoto M (2026) Correction: Machine learning-based detection of cognitive decline using SSWTRT: classification performance and decision analysis. Front. Artif. Intell. 8:1764066. doi: 10.3389/frai.2025.1764066

Received: 09 December 2025; Accepted: 10 December 2025;
Published: 08 January 2026.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2026 Nozaki, Kamohara, Abe, Ieda, Nakajima and Sakamoto. 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: Yuji Nozaki, bmEwMDMxNjlAdWVjLmFjLmpw

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.