# SCH: AI-Enhanced Multimodal Sensor-on-a-chip for Alzheimer's Disease Detection

> **NIH NIH R01** · PENNSYLVANIA STATE UNIVERSITY, THE · 2022 · $299,177

## Abstract

We propose a new research paradigm aimed at addressing scientific questions in both biosensing and
machine learning for the early prediction of Alzheimer's disease (AD), and at solving a grand challenge in
the identification of minimally-invasive AD biomarkers in tear, saliva, and blood. Our goal is to develop a
novel and minimally-invasive system that integrates a multimodal biosensing platform and a machine
learning framework, which synergistically work together to significantly enhance the detection accuracy.
The program will pioneer a novel Multimodal Optical, Mechanical, Electrochemical Nano-sensor with Twodimensional
material Amplification (MOMENTA) platform for sensitive and selective detection of AD
biomarkers. The sensor outputs are used for training the new Hierarchical Multimodal Machine Learning
(HMML) framework, which not only automatically integrates the heterogeneous data from different
modalities but also ranks the importance of different biosensors and biomarkers for AD prediction.
Moreover, the framework is able to identify potential new biomarkers based on a statistical analysis of the
learned weights on the input signals and provide feedback information to further improve the MOMENTA
platform design. This interdisciplinary research brings together materials scientists who create new twodimensional
(2D) material platforms for sensor enhancement, nanotechnology and device experts who
advance chip-scale sensor platforms, data scientists who analyze data with machine learning methods to
target early prediction of AD, and AD experts who help to identify potentially new AD biomarkers. The
machine-learning-enhanced multi-modal sensor system will not only offer major performance boost
compared to state-of-the-art, but also yield critical insights on new biomarker discovery for AD diagnosis at
an early stage.

## Key facts

- **NIH application ID:** 10437992
- **Project number:** 1R01AG077016-01
- **Recipient organization:** PENNSYLVANIA STATE UNIVERSITY, THE
- **Principal Investigator:** Juejun Hu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $299,177
- **Award type:** 1
- **Project period:** 2022-09-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10437992

## Citation

> US National Institutes of Health, RePORTER application 10437992, SCH: AI-Enhanced Multimodal Sensor-on-a-chip for Alzheimer's Disease Detection (1R01AG077016-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10437992. Licensed CC0.

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