Login / Signup

Diagnostic accuracy of research criteria for prodromal frontotemporal dementia.

Alberto BenussiEnrico PremiMario GrassiAntonella AlbericiValentina CantoniStefano GazzinaSilvana ArchettiRoberto GasparottiGiorgio G FumagalliArabella BouziguesLucy L RussellKiran SamraDavid M CashMartina BocchettaEmily G ToddRhian S ConveryImogen SwiftAitana Sogorb-EsteveCarolin HellerJohn C van SwietenLize C JiskootHarro SeelaarRaquel Sanchez-ValleFermin MorenoRobert Jr LaforceCaroline GraffMatthis SynofzikDaniela GalimbertiJames B RoweMario MasellisMaria Carmela TartagliaElizabeth FingerRik VandenbergheAlexandre MendonçaPietro TiraboschiChris R ButlerIsabel SantanaAlexander GerhardIsabelle Le BerFlorence PasquierSimon DucharmeJohannes LevinSandro SorbiMarkus OttoAlessandro PadovaniJonathan D RohrerBarbara Borroninull null
Published in: Alzheimer's research & therapy (2024)
The proposed MCBMI criteria showed very good classification accuracy for identifying the prodromal stage of FTD.
Keyphrases
  • parkinson disease
  • machine learning
  • deep learning
  • deep brain stimulation