Identification of Depression Subtypes in Parkinson's Disease Patients via Structural MRI Whole-Brain Radiomics: An Unsupervised Machine Learning Study.

Journal: CNS neuroscience & therapeutics

PMID: 39915918

Abstract

OBJECTIVE: Current clinical evaluation may tend to lack precision in detecting depression in Parkinson's disease (DPD). Radiomics features have gradually shown potential as auxiliary diagnostic tools in identifying and distinguishing different subtypes of Parkinson's disease (PD), and a radiomic approach that combines unsupervised machine learning has the potential to identify DPD.

Authors

Zihan Zhang
Jiaxuan Peng

Jinzhou Medical University, Jinzhou, Liaoning Province, China.
Qiaowei Song

Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China.
Yuyun Xu
Yuguo Wei

Precision Health Institution, GE Healthcare, Xihu District, Hangzhou, China.
Zhenyu Shu

Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou.

Keywords

Aged Brain Depression Female Humans Magnetic Resonance Imaging Male Middle Aged Parkinson Disease Radiomics Unsupervised Machine Learning

External Resources

View on PubMed Access via DOI PubMed (39915918)

Identification of Depression Subtypes in Parkinson's Disease Patients via Structural MRI Whole-Brain Radiomics: An Unsupervised Machine Learning Study.

Abstract

Authors

Keywords

External Resources

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