# SCH: Machine LEarning & MicrofluiDics for Multimodal Sensing of TiCk-bOrne Diseases(MEDICO)

> **NIH NIH R01** · WEST VIRGINIA UNIVERSITY · 2024 · $298,345

## Abstract

PROJECT SUMMARY (See instructions): 
The incidence of tick-borne diseases (TbD) is increasing, with ~500k reported cases during 2004-2016 
from the National Notifiable Diseases Surveillance System of the Centers for Disease Control (CDC), and 
is expected to increase due to global climate change. Some of the most common TbD in the U.S. include 
anaplasmosis, babesiosis, and Lyme disease transmitted by the same tick vector, I. scapularis. Lyme 
disease is especially pernicious because it is difficult to diagnose early, often misdiagnosed, and is difficult 
to treat. Because current diagnostic methods are insufficiently sensitive or non-existent, we propose a 
novel approach for diagnosis via a microfluidic platform with an integrated multimodal sensing system and 
machine learning (ML) algorithm. Based on our preliminary and published data, we hypothesize that we 
can detect TbD and their coinfections from whole blood. Central to our vision is a system designed for 
minimal user intervention to detect and measure complex cell data using ML. The diagnosis of TbD will be 
achieved through three objectives: 1) design a dielectrophoresis (DEP) based platform for detecting TbDs 
from whole blood; 2) design 3D sensors and a readout integrated circuit (ROIG) for sensitive in-vitro 
detection of cells; and 3) develop an ML algorithm to diagnose early-stage Lyme disease, babesiosis, and 
anaplasmosis. An extremely sensitive (<0.1aF), low-voltage (1.8V for core & 3.3V for 10), low-power 
(<10mW), multi-channel (~16-ch), high-speed (~5MSample/sec per channel) readout integrated circuit 
(ROIG) will be integrated to detect single-cell behavior at high resolution (~10-bit resolution). The novel ML 
algorithm(s) will use cell data to determine the type of infection intelligently, reducing >96Gbit data during 
each diagnostic cycle (~2min) into as simple as 1-byte diagnostic information (i.e., 1: positive, 0: negative) 
per tested population of the cells or disease. Further, an open-source program to diagnose TbD will be 
created and made available freely from a public software repository. This developed diagnostic technology 
will simultaneously meet high-sensitivity, low-power/voltage, high-purity, and high-viability performance 
metrics by cutting the diagnosis time from 4-6 weeks (late stages) to <30 min (early stages). The proposed 
research will lead to faster diagnosis, reducing the hospitalization, morbidity, and mortality associated with 
TbD and their coinfections.

## Key facts

- **NIH application ID:** 10910919
- **Project number:** 5R01AI174300-03
- **Recipient organization:** WEST VIRGINIA UNIVERSITY
- **Principal Investigator:** Soumya K Srivastava
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $298,345
- **Award type:** 5
- **Project period:** 2022-09-13 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10910919

## Citation

> US National Institutes of Health, RePORTER application 10910919, SCH: Machine LEarning & MicrofluiDics for Multimodal Sensing of TiCk-bOrne Diseases(MEDICO) (5R01AI174300-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10910919. Licensed CC0.

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