# Maternal mHealth blood hemoglobin analysis with informed deep learning

> **NIH NIH R01** · PURDUE UNIVERSITY · 2024 · $472,558

## Abstract

PROJECT SUMMARY/ABSTRACT
Blood hemoglobin (Hgb) testing is a common clinical laboratory test during routine patient care and screening.
In particular, blood Hgb tests are essential for the diagnosis and management of anemia. Globally, over 40% of
pregnant women are anemic, adversely affecting maternal and fetal health outcomes through increased
morbidity and mortality. A range of treatments for anemia are well-established and readily available even in
low- and middle-income countries. In these settings, the main challenge is that anemia is not detected or
detected too late. For pregnant women in resource-limited settings who require several Hgb tests during all
trimesters, conventional invasive blood Hgb tests are not only painful and iatrogenic, but are also expensive
and often inaccessible. Existing noninvasive devices and smartphone-based technologies for measuring blood
Hgb levels often rely on costly specialized equipment and complex smartphone attachments, thus hampering
practical translation from research to clinical practice in resource-limited settings. Based on the preliminary
results generated by our transdisciplinary team, we hypothesize that blood Hgb levels can be accurately and
precisely predicted from a red-green-blue (RGB) image of the inner eyelid (palpebral conjunctiva) acquired
using a smartphone camera with no additional attachments, and that this mobile health (mHealth) application
can be fully integrated with an existing electronic health record (EHR) system in low-resource settings.
Specifically, an informed learning approach will enable us to incorporate a physical or biological understanding
into the learning algorithms to overcome the limitations of purely data-driven machine learning. Our team,
consisting of experts in optical spectroscopy and machine learning, biomedical informatics and implementation
science, and maternal and public health, proposes three aims to achieve the project goals. In Aim 1, we will
develop a robust, simple, frontend data acquisition method for various mHealth and digital health settings. A
tissue-specific color gamut design and true color recovery will provide the first-of-its-kind systematic
methodology to realize color accuracy that will be highly sensitive to blood Hgb. In Aim 2, we will perfect the
core mHealth computational algorithm using clinical data of black African pregnant women. Sub-algorithms of
automated inner eyelid demarcation, advanced spectral learning, and blood Hgb content computation will
enable fully automated, highly accurate, and precise blood Hgb estimations. Tissue optics-informed spectral
learning will capture strong nonlinearity between RGB values and spectral intensity directly in the spectral
domain. In Aim 3, we will integrate mHealth blood Hgb technology with a widely used EHR and evaluate the
backend performance. The proposed connected mHealth technology will demonstrate the possibility of offering
mobility, simplicity, and affordability for rapid and scalabl...

## Key facts

- **NIH application ID:** 10827858
- **Project number:** 5R01EB033788-02
- **Recipient organization:** PURDUE UNIVERSITY
- **Principal Investigator:** Young L Kim
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $472,558
- **Award type:** 5
- **Project period:** 2023-05-01 → 2027-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10827858

## Citation

> US National Institutes of Health, RePORTER application 10827858, Maternal mHealth blood hemoglobin analysis with informed deep learning (5R01EB033788-02). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10827858. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*