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CORRECTION article

Front. Aging Neurosci., 03 October 2024
Sec. Parkinson’s Disease and Aging-related Movement Disorders
Volume 16 - 2024 | https://doi.org/10.3389/fnagi.2024.1490807

Corrigendum: A more objective PD diagnostic model: integrating texture feature markers of cerebellar gray matter and white matter through machine learning

This article is a correction to:

  1. A more objective PD diagnostic model: integrating texture feature markers of cerebellar gray matter and white matter through machine learning

    1. Read original article
\r\nYini Chen&#x;Yini Chen1†Yiwei Qi&#x;Yiwei Qi1†Tianbai Li&#x;Tianbai Li2†Andong LinAndong Lin3Yang NiYang Ni2Renwang Pu Renwang Pu1*Bo Sun Bo Sun1*
  • 1Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
  • 2Liaoning Provincial Key Laboratory for Research on the Pathogenic Mechanisms of Neurological Diseases, The First Affiliated Hospital, Dalian Medical University, Dalian, China
  • 3Department of Neurology, Zhejiang Taizhou Municipal Hospital, Taizhou, Zhejiang, China

A corrigendum on
A more objective PD diagnostic model: integrating texture feature markers of cerebellar gray matter and white matter through machine learning

by Chen, Y., Qi, Y., Li, T., Lin, A., Ni, Y., Pu, R., and Sun, B. (2024). Front. Aging Neurosci. 16:1393841. doi: 10.3389/fnagi.2024.1393841

In the published article, there was an error. The model was incorrectly described as being composed of a single feature. A correction has been made to Results, paragraph four. This sentence previously stated:

“Concerning the REF, the pipeline deploying an LDA classifier secured the highest AUC while utilizing merely a single feature, in accordance with the “one-standard error” rule.”

The corrected sentence appears below:

“Concerning the REF, the pipeline deploying an LDA classifier secured the highest AUC while utilizing three features, in accordance with the “one-standard error” rule.”

In the published article, there was an error. The model was incorrectly described as being composed of a single feature. A correction has been made to Results, paragraph five. A sentence has been removed as it was redundant. This sentence previously stated:

“This was accomplished by utilizing a mere single feature.”

In the published article, there was an error. The model was incorrectly described as being composed of seventeen features. A correction has been made to Results, paragraph five. This sentence previously stated:

“The seventeen features curated by the FAE, presented in Figure 5C, encompass a diverse set including three glcm features, one gldm feature, one glrlm feature, and one ngtdm feature.”

The corrected sentence appears below:

“The six features curated by the FAE, presented in Figure 5C, encompass a diverse set including three glcm features, one gldm feature, one glrlm feature, and one ngtdm feature.”

In the published article, there was an error. A correction has been made to Discussion, paragraph one. A sentence has been removed as it was redundant. This sentence previously stated:

“This study established a robust model, and more importantly, through the visualization of the model by SHAP, it was found that the texture features extracted from the white matter of the cerebellum have greater diagnostic value.”

The authors apologize for these errors 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: Parkinson's disease, radiomic, machine learning, SHAP, FeAture Explorer

Citation: Chen Y, Qi Y, Li T, Lin A, Ni Y, Pu R and Sun B (2024) Corrigendum: A more objective PD diagnostic model: integrating texture feature markers of cerebellar gray matter and white matter through machine learning. Front. Aging Neurosci. 16:1490807. doi: 10.3389/fnagi.2024.1490807

Received: 03 September 2024; Accepted: 16 September 2024;
Published: 03 October 2024.

Edited and reviewed by: Robert Petersen, Central Michigan University, United States

Copyright © 2024 Chen, Qi, Li, Lin, Ni, Pu and Sun. 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: Bo Sun, c3VuYm95Y211JiN4MDAwNDA7MTYzLmNvbQ==; Renwang Pu, cHVyZW53YW5nNzc3JiN4MDAwNDA7MTYzLmNvbQ==

†These authors share first authorship

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.