# A Novel Informatics System For Craniosynostosis Surgery

> **NIH NIH R01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2021 · $390,000

## Abstract

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.

## Key facts

- **NIH application ID:** 10286746
- **Project number:** 3R01DE027027-05S1
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** Xiaobo Zhou
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $390,000
- **Award type:** 3
- **Project period:** 2017-09-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10286746

## Citation

> US National Institutes of Health, RePORTER application 10286746, A Novel Informatics System For Craniosynostosis Surgery (3R01DE027027-05S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10286746. Licensed CC0.

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