Increasing the confidence of F-Florbetaben PET interpretations: Machine learning quantitative approximation.

Journal: Revista espanola de medicina nuclear e imagen molecular

Published Date: Apr 21, 2021

AIM: To assess the added value of semiquantitative parameters on the visual assessment and to study the patterns of F-Florbetaben brain deposition.

Ana María García Vicente

Nuclear Medicine Department, University General Hospital, Ciudad Real, Spain. Electronic address: angarvice@yahoo.es.
María Jesús Tello Galán

Nuclear Medicine Department, University General Hospital, Ciudad Real, Spain.
Francisco José Pena Pardo

Nuclear Medicine Department, University General Hospital, Ciudad Real, Spain.
Mariano Amo-Salas

Mathematics Department, Castilla-La Mancha University, Ciudad Real, Spain.
Beatriz Mondejar Marín

Neurology Department, Complejo Hospitalario de Toledo, Spain.
Santiago Navarro Muñoz

Neurology Department, La Mancha Centro Hospital, Alcázar de San Juan, Ciudad Real, Spain.
Ignacio Rueda Medina

Neurology Department, La Mancha Centro Hospital, Alcázar de San Juan, Ciudad Real, Spain.
Víctor Manuel Poblete García

Nuclear Medicine Department, University General Hospital, Ciudad Real, Spain.
Carlos Marsal Alonso

Neurology Department, Complejo Hospitalario de Toledo, Spain.
Ángel Soriano Castrejón

Nuclear Medicine Department, University General Hospital, Ciudad Real, Spain.

Alzheimer Disease Amyloid beta-Peptides Aniline Compounds Humans Machine Learning Positron-Emission Tomography Retrospective Studies Stilbenes

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