Machine learning-based radiomics in neurodegenerative and cerebrovascular disease

Area of Science:

• Radiomics and Machine Learning in Neurology
• Medical Imaging Analysis for Cognitive Disorders

Background:

• Cognitive impairments from neurodegenerative and cerebrovascular diseases pose a global health challenge.
• Distinct patterns and severity exist between neurodegenerative and cerebrovascular cognitive impairments.
• Traditional diagnostic methods struggle with high-dimensional data in complex neurological conditions.

Purpose of the Study:

• To review the application of machine learning-based radiomics in cognitive impairments.
• To focus on neurodegenerative diseases (Alzheimer's, Parkinson's, Lewy body dementia, Huntington's) and cerebrovascular diseases (stroke, small vessel disease, moyamoya disease).
• To discuss challenges, limitations, and future clinical applications of this technology.

Main Methods:

• Review of machine learning techniques applied to radiomic analysis.
• Analysis of radiomic data for classification and prediction of cognitive impairments.
• Focus on neuroimaging data for neurodegenerative and cerebrovascular conditions.

Main Results:

• Machine learning radiomics offers a powerful tool for analyzing high-dimensional, multivariate data.
• These models can predict disease development and accurately classify overlapping symptoms.
• Radiomics facilitates improved clinical decision-making in cognitive impairment diagnosis.

Conclusions:

• Machine learning-based radiomics shows significant potential in diagnosing and managing cognitive impairments.
• Addressing current challenges is crucial for advancing the clinical utility of radiomics.
• Future research should focus on overcoming limitations for broader clinical integration.