Machine Learning-Based Diagnostic Prediction Model Using T1-Weighted Striatal Magnetic Resonance Imaging for Early-Stage Parkinson's Disease Detection.

Journal: Academic radiology
Published Date: Apr 18, 2025

Abstract

RATIONALE AND OBJECTIVES: Diagnosing Parkinson's disease (PD) typically relies on clinical evaluations, often detecting it in advanced stages. Recently, artificial intelligence has increasingly been applied to imaging for neurodegenerative disorders. This study aims to develop a diagnostic prediction model using T1-weighted magnetic resonance imaging (T1-MRI) data from the caudate and putamen in individuals with early-stage PD.

Authors

  • Alicia R M Accioly
    Medical Science Center, Federal University of Pernambuco, Recife, Brazil (A.R.M.A., L.H.C., D.P.C.F.B.). Electronic address: aliciarma@gmail.com.
  • Vinícius O Menezes
    Nuclear Medicine, Clinical Hospital of Federal University of Pernambuco, Recife, Brazil (V.O.M., F.A.M.).
  • Lucas H Calixto
    Medical Science Center, Federal University of Pernambuco, Recife, Brazil (A.R.M.A., L.H.C., D.P.C.F.B.).
  • Dharah P C F Bispo
    Medical Science Center, Federal University of Pernambuco, Recife, Brazil (A.R.M.A., L.H.C., D.P.C.F.B.).
  • Maximilian Lachmann
    Center for Exact and Natural Sciences, Federal University of Pernambuco, Recife, Brazil (M.L.).
  • Felipe A Mourato
    Nuclear Medicine, Clinical Hospital of Federal University of Pernambuco, Recife, Brazil (V.O.M., F.A.M.).
  • Marcos A D Machado
    Radiology Department, Hospital Universitário Prof. Edgard Santos, Bahia, Brazil (M.A.D.M.).
  • Paula R B Diniz
    Telehealth Unit, Medical Science Center, Federal University of Pernambuco, Recife, Brazil (P.R.B.D.).

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