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.
Statements
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.
Summary
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
Volume
16 - 2024
Edited and reviewed by
Robert Petersen, Central Michigan University, United States
Updates
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 sunboycmu@163.comRenwang Pu purenwang777@163.com
†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.