Prediction of Amyloid β and Tau Pathology using deep learning based artificial intelligence system
K. H. Lee, J. Y. Park, E. H. Seo, S. H. Won
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AbstractThe pathological cascade of Alzheimer's disease (AD) begins decades before the development of clinical symptoms (Jack et al, 2010). Thus, predicting Alzheimer's before disease onset can minimize the socioeconomic burden and delay the cognitive impairments of patients. The Rey-complex figure task (RCFT) was revisited as the sensitive neurocognitive evaluator that is associated with cerebrospinal fluid amyloid β, tau levels and neurodegeneration observed by magnetic resonance imaging (MRI) (Seo et al, 2021). But the application of these findings is limited in real life. Assessing brain biomarker content with positron emission tomography (PET) is not readily accessible to ordinary patients, and the scoring system of RCFT is unstable due to the interrater gap in scores. To overcome this problem, we propose AI, deep-learning-based solutions for RCFT scoring, and MRI-based PET prognosis.Keywords: Alzheimer's disease, rey complex figure test, Artificial Intelligence, deep learning, convolutional neural network
K. H. Lee, J. Y. Park, E. H. Seo, S. H. Won (2022). Prediction of Amyloid β and Tau Pathology using deep learning based artificial intelligence system. Gerontechnology, 21(s),2-2
https://doi.org/10.4017/gt.2022.21.s.821.2.sp3