A Novel Informatics System For Craniosynostosis Surgery

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Abstract Alzheimer's disease (AD) is characterized by progressive memory loss and cognitive decline, cerebral accumulation of amyloid-β peptide (Aβ) in senile plaques and hyper-phosphorylated tau in neurofibrillary tangles (NFT). Since AD is a complex and multifactorial disease, large datasets with multiple data types have been critical to identify its risk factors. For several decades, only the allele 4 of Apolipoprotein E (APOE), which is present in about half of late-onset AD (LOAD) patients, has been convincingly demonstrated to affect risk for LOAD. However, unfortunately, current treatments are just palliative because they do not slow down or halt the disease progression. More research on biomarkers are urgently needed. Data used in this study were obtained fromthe Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Currently ADNI consortium opened MRI imaging data for over 2,000 AD patients from normal, mild, moderate and severe stages. We plan to apply the AI and machine learning methods developed for craniosynostosis study in the parent R01DE027027 to the ADNI data and try to segment and reconstruct the AD imaging data, characterize the biomechanical property of brain in AD patients, and then further stratify the AD patients for better therapy. This kind of idea was never applied to AD research, which could be a potential contribution to the AD study. Staging the AD disease is very important for design therapy strategy. There are numerous work studied imaging genetics from the ADNI data sets and biomarker based staging technologies, but none of those work studied the biomechanical property changes during the AD development. It has been observed by many researchers and physicians that AD tissues tend to be less stiff and less elastic. Hence, there is an urgent need to improve our understanding of the AD brain tissue property correlated to AD stages. Our immediate goal is to develop computational model to characterize the AD patient specific tissue elasticity and AD stages. To achieve these goals, our Specific Aims are: (1) to develop deep learning framework to obtain the brain volume and surface of AD patients; (2) to develop computational techniques for estimating sub-region tissue stiffness directly from AD imaging data; and to predict AD progression based on the biomechanical features of AD brain. The scope of this NIA suppl. is within the scope of the parent R01DE027027 “eSuture system: A novel informatics system for craniosynostosis (CSO) surgery.” The eSuture system focuses on developing novel imaging informatics and machine leaning technologies to segment CSO imagining data, to stratify and classify CSO patients, and to characterize the biomechanical property of calvarial bone tissue with nonlinear finite element models.