# Rapid platelet dysfunction detection in whole blood samples using machine learning powered micro-clot imaging.

> **NIH NIH R33** · STASYS MEDICAL CORPORATION · 2022 · $408,442

## Abstract

Abstract
This project will optimize a point-of-care (POC) platelet force monitoring technology for clinical application in
trauma care. The leading causes of death and disability after trauma are related to hemorrhage and traumatic
brain injury with intracranial hemorrhage (ICH). Platelets are critical to hemostasis by inducing clot formation via
adhesion, aggregation, and contraction at wounds. Platelets often become dysfunctional after trauma which
worsens internal bleeding and ICH progression and increasing morbidity. POC platelet assays have not been
incorporated in practice due to:1) lack of large cohort ED patient testing; 2) poor accuracy in transfusion prediction
and 3) extended processing times. We have made an innovative POC technology to test platelet function by
directly measuring platelet contractile forces on microfluidic force sensors. Advantages of our POC test vs.
existing assays: 1) rapid, direct activation and measures of platelet functions and 2) innovative machine vision
with deep potential for machine learning insight. However, this technology needs optimization and validation in a
large major ED trauma cohort and remains untested after ICH. Our pilot data suggests platelet contractile forces
are sensitive to a range of relevant activation pathways and mechanisms and force is significantly decreased in
trauma patients requiring blood transfusion. Further, in prior clinical trials, platelet transfusion has been found to
be harmful when used indiscriminately. Building on this unmet scientific need, we will determine if our POC
technology is predictive of hemorrhagic complications in trauma patients, informing a personalized transfusion
strategy. Our overarching hypothesis is our POC platelet force monitor technology is an efficient indicator of
bleeding complications after trauma and ICH. Aim 1: Optimize the platelet force monitor optics to improve
platelet force sensor performance. We hypothesize the addition of a second fluorescent imaging channel can
improve our current platelet force sensor performance. Aim 2: Use machine learning (ML) image analysis to
improve detection of platelet dysfunction and prediction of trauma outcomes. We hypothesize image-based
ML models can improve test performance. We will compare the accuracy of direct platelet force measurements
(Aim 1) vs. ML-enhancement measurements for detecting platelet dysfunction and predicting outcomes. Aim 3:
Validate our platelet function algorithm for predicting blood transfusion needs, mortality, and the
progression of traumatic ICH in a prospective cohort of severely-injured ED trauma patients. We
hypothesize platelet force will be a powerful predictor of blood transfusion needs, mortality, and progression of
ICH. In the ED we will apply our algorithm (both the original and optimized algorithm from Aim 1) to blood from
trauma patients and compare the predicted transfusion requirements against actual transfusion (Aim-3a) and
measure the association between measure...

## Key facts

- **NIH application ID:** 10505271
- **Project number:** 4R33HL156508-02
- **Recipient organization:** STASYS MEDICAL CORPORATION
- **Principal Investigator:** Lucas H Ting
- **Activity code:** R33 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $408,442
- **Award type:** 4N
- **Project period:** 2021-04-19 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10505271

## Citation

> US National Institutes of Health, RePORTER application 10505271, Rapid platelet dysfunction detection in whole blood samples using machine learning powered micro-clot imaging. (4R33HL156508-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10505271. Licensed CC0.

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