Machine learning-based brain magnetic resonance imaging radiomics for identifying rapid eye movement sleep behavior disorder in Parkinson’s disease patients

Background Traditional clinical diagnostic methods of rapid eye movement sleep behavior disorder (RBD) have certain limitations, especially in the early stages. This study aims to develop and validate an magnetic resonance imaging (MRI) radiomics-based machine learning classifier to accurately detec...

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Veröffentlicht in:	BMC medical imaging Jg. 25; H. 1; S. 227 - 11
Hauptverfasser:	lian, Yandong, Xu, Yibin, Hu, Linlin, Wei, Yuguo, Wang, Zhaoge
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 01.07.2025 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Aged Algorithms Alzheimer's disease Artificial intelligence Automation Behavior disorders Biomarkers Brain Brain - diagnostic imaging Brain research Cerebrospinal fluid Data mining Disease Eye movements Female Group theory Humans Imaging Instability Learning algorithms Machine Learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Medical imaging Medical imaging equipment Medicine Medicine & Public Health Medicine, Preventive Mental illness Middle Aged Movement disorders Neurodegenerative diseases Neuroimaging Parkinson Disease - complications Parkinson Disease - diagnostic imaging Parkinson's disease Patients Performance evaluation Posture Prediction models Preventive health services Quantitative imaging and biomarkers Radiology Radiomics Rapid eye movement sleep behavior disorder Regression analysis REM sleep REM Sleep Behavior Disorder - diagnostic imaging REM Sleep Behavior Disorder - etiology Risk Risk groups ROC Curve Sleep Sleep disorders Software Stability Substantia alba Substantia grisea Wavelet transforms Taiwan Biomarkers Magnetic resonance imaging Rapid eye movement sleep behavior disorder Machine learning Radiomics
ISSN:	1471-2342, 1471-2342
Online-Zugang:	Volltext
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