A Machine Learning-Based Approach to Discrimination of Tauopathies Using [<sup>18</sup> F]PM-PBB3 PET Images.
Hironobu EndoKenji TagaiMaiko OnoYoko IkomaAsaka OyamaKiwamu MatsuokaNaomi KokuboKosei HirataYasunori SanoMasaki OyaHideki MatsumotoShin KuroseChie SekiHiroshi ShimizuAkiyoshi KakitaKeisuke TakahataHitoshi ShinotohHitoshi ShimadaTakahiko TokudaKazunori KawamuraMing-Rong ZhangKenichi OishiSusumu MoriYuhei TakadoMakoto HiguchiPublished in: Movement disorders : official journal of the Movement Disorder Society (2022)
These findings highlight our technology's unbiased capability to identify topologies of 3R + 4R versus 4R tau deposits. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Keyphrases
- machine learning
- deep learning
- particulate matter
- air pollution
- computed tomography
- convolutional neural network
- pet ct
- positron emission tomography
- optical coherence tomography
- cerebrospinal fluid
- polycyclic aromatic hydrocarbons
- big data
- systematic review
- randomized controlled trial
- risk assessment
- water soluble