Machine learning models for the differential diagnosis of vascular parkinsonism and Parkinson's disease using [(123)I]FP-CIT SPECT.

Journal: European journal of nuclear medicine and molecular imaging

Published Date: Aug 14, 2014

Abstract

PURPOSE: The study's objective was to develop diagnostic predictive models using data from two commonly used [(123)I]FP-CIT SPECT assessment methods: region-of-interest (ROI) analysis and whole-brain voxel-based analysis.

Authors

I Huertas-Fernández

Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Avda. Manuel Siurot s/n., 41013, Seville, Spain.
F J García-Gómez
D García-Solís
S Benítez-Rivero
V A Marín-Oyaga
S Jesús
M T Cáceres-Redondo
J A Lojo
J F Martín-Rodríguez
F Carrillo
P Mir

Keywords

Aged Aged, 80 and over Artificial Intelligence Diagnosis, Differential Female Humans Image Processing, Computer-Assisted Male Parkinson Disease Parkinson Disease, Secondary Radiopharmaceuticals Tomography, Emission-Computed, Single-Photon Tropanes

External Resources

View on PubMed Access via DOI PubMed (25120041)

Machine learning models for the differential diagnosis of vascular parkinsonism and Parkinson's disease using [(123)I]FP-CIT SPECT.

Abstract

Authors

Keywords

External Resources

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