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Related Concept Videos

Parkinson's Disease: Overview01:15

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Neurodegenerative disorders are progressive diseases that cause irreversible damage and loss to neurons in specific brain areas. Examples of these disorders include Parkinson's disease, Alzheimer's disease, Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS). These disorders share characteristics such as proteinopathies, selective neuronal vulnerability, and a complex interplay between genetic and environmental factors. The primary therapeutic goal for these conditions is...
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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Radiomics for Parkinson's disease classification using advanced texture-based biomarkers.

Sonal Gore1, Aniket Dhole1, Shrishail Kumbhar1

  • 1Pimpri Chinchwad College of Engineering, Nigdi, Pune, Maharashtra, India.

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|October 4, 2023
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Summary

This study introduces an advanced texture analysis using Local Binary Patterns (LBP) on MRI scans for Parkinson's disease (PD) detection. The method achieved up to 83.33% accuracy, offering a faster, more precise diagnostic tool for neurodegenerative diseases.

Keywords:
Custom LBP method to extract advanced biomedical texture descriptorsLocal binary patternParkinson's diseaseRadiomicsRecursive feature eliminationSupport vector machine

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Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Radiomics
  • Machine Learning

Background:

  • Parkinson's disease (PD) diagnosis is typically manual and time-consuming.
  • Computer-aided diagnosis using MRI can enhance diagnostic precision and speed.
  • Texture-based radiomic analysis offers potential for identifying subtle disease markers.

Purpose of the Study:

  • To investigate the efficacy of advanced texture analysis using Local Binary Pattern (LBP) variants for Parkinson's disease classification from MRI scans.
  • To develop and evaluate a computer-aided diagnostic model for Parkinson's disease.
  • To explore the potential of radiomic features for early and accurate PD detection.

Main Methods:

  • Radiomic analysis was performed on 3D T1-weighted and resting-state MRI scans from 72 subjects (36 healthy controls, 47 Parkinson's patients).
  • Local Binary Pattern (LBP) method with custom variants was applied to extract textural biomarkers from 360 selected 2D MRI images.
  • Feature selection using recursive feature elimination reduced ~150-300 LBP histogram features to 13-21 significant features, analyzed with SVM and random forest algorithms.

Main Results:

  • Variant-I of the LBP method achieved the highest test accuracy of 83.33%, with precision of 84.62%, recall of 91.67%, and F1-score of 88%.
  • Classification accuracies ranged from 61.11% to 83.33%, with AUC-ROC values between 0.43 and 0.86 across four LBP variants.
  • The proposed method, utilizing an SVM classifier with 10-fold cross-validation, demonstrated significant potential in detecting Parkinson's patients.

Conclusions:

  • Advanced biomedical texture features, specifically extended LBP variants, can effectively detect subtle variations in local appearance indicative of Parkinson's disease.
  • The developed radiomic analysis model shows promise as a precise and rapid computer-aided diagnostic tool for Parkinson's disease.
  • Texture-based radiomic analysis of MRI scans represents a valuable approach for improving the diagnostic workflow for neurodegenerative conditions like PD.