A Machine Learning–Based Approach to Discrimination of Tauopathies Using [18F]PM‐PBB3 PET Images

• Published in: Movement disorders Vol. 37; no. 11; pp. 2236 - 2246
• Main Authors: Endo, Hironobu, Tagai, Kenji, Ono, Maiko, Ikoma, Yoko, Oyama, Asaka, Matsuoka, Kiwamu, Kokubo, Naomi, Hirata, Kosei, Sano, Yasunori, Oya, Masaki, Matsumoto, Hideki, Kurose, Shin, Seki, Chie, Shimizu, Hiroshi, Kakita, Akiyoshi, Takahata, Keisuke, Shinotoh, Hitoshi, Shimada, Hitoshi, Tokuda, Takahiko, Kawamura, Kazunori, Zhang, Ming‐Rong, Oishi, Kenichi, Mori, Susumu, Takado, Yuhei, Higuchi, Makoto
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.11.2022; Wiley Subscription Services, Inc
• Subjects: Alzheimer Disease - diagnostic imaging; Alzheimer Disease - pathology; Alzheimer's disease; Amygdala; Brain - pathology; Fibrils; Globus pallidus; Humans; Learning algorithms; Machine Learning; Movement Disorders; Neurodegenerative diseases; Paralysis; Patients; Positron emission tomography; Progressive supranuclear palsy; Supranuclear Palsy, Progressive - pathology; tau PET; Tau protein; tau Proteins - metabolism; Tauopathies - diagnostic imaging; Tauopathies - pathology; tauopathy; tauopathy; machine learning; Alzheimer's disease; tau PET; progressive supranuclear palsy

ABSTRACT Background We recently developed a positron emission tomography (PET) probe, [18F]PM‐PBB3, to detect tau lesions in diverse tauopathies, including mixed three‐repeat and four‐repeat (3R + 4R) tau fibrils in Alzheimer's disease (AD) and 4R tau aggregates in progressive supranuclear pals...