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Related Experiment Video

Updated: May 22, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Multinomial logistic regression algorithm for the classification of patients with parkinsonisms.

Eva Štokelj1, Tomaž Rus2,3, Jan Jamšek3

  • 1Faculty of Mathematics and Physics, University of Ljubljana, Jadranska ulica 19, 1000, Ljubljana, Slovenia.

EJNMMI Research
|March 17, 2025
PubMed
Summary

This study introduces a new algorithm for diagnosing neurodegenerative parkinsonisms like Parkinson's disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP) using FDG-PET scans. The integrated approach achieves high accuracy, aiding in earlier and more precise differential diagnoses.

Keywords:
FDG-PETMetabolic brain patternMultinomial logistic regressionParkinsonismsSSM/PCA

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Area of Science:

  • Neuroimaging
  • Nuclear Medicine
  • Neurology

Background:

  • Accurate diagnosis of neurodegenerative parkinsonisms is challenging due to overlapping symptoms and misdiagnosis.
  • 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) offers potential for improved diagnostic accuracy.

Purpose of the Study:

  • To develop and validate an integrated classification algorithm for differential diagnosis of parkinsonisms.
  • To improve diagnostic accuracy for Parkinson's disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP).

Main Methods:

  • Developed an algorithm combining multinomial logistic regression and Scaled Subprofile Model/Principal Component Analysis (SSM/PCA).
  • Applied SSM/PCA to FDG-PET brain images for dimensionality reduction and feature extraction.
  • Used principal components in a logistic regression model to generate disease-specific topographic classifications.

Main Results:

  • The algorithm achieved high Area Under the Curve (AUC) values: 0.95 for PSP, 0.93 for PD, and 0.90 for MSA.
  • At a 99% probability threshold, correct classification rates were 82% for PD, 29% for MSA, and 77% for PSP.
  • Low misclassification rates (5% PD, 6% MSA, 6% PSP) were observed, with a portion of cases remaining undetermined.

Conclusions:

  • The developed algorithm provides comparable accuracy and reliability to existing methods for diagnosing PD, MSA, and PSP.
  • This approach does not require healthy control images and can simultaneously distinguish between different parkinsonisms.
  • The algorithm is flexible and can be adapted to include new disease groups.