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Related Experiment Video

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Uncontrolled Hemorrhagic Shock Modeled via Liver Laceration in Mice with Real Time Hemodynamic Monitoring

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Detection of Internal Hemorrhage via Sequential Inference: An In Silico Feasibility Study.

Yekanth Ram Chalumuri¹, Xin Jin¹, Ali Tivay¹

¹Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA.

Diagnostics (Basel, Switzerland)

|September 14, 2024

Summary

This study introduces novel algorithms for detecting internal hemorrhage using noninvasive hematocrit monitoring. These advanced methods accurately estimate hemorrhage rates, even during fluid resuscitation, outperforming traditional hematocrit-based detection.

Keywords:

Kalman filter detection hemorrhage observer sequential inference virtual patient

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Last Updated: Jun 13, 2025

Uncontrolled Hemorrhagic Shock Modeled via Liver Laceration in Mice with Real Time Hemodynamic Monitoring

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A Murine Model of Subarachnoid Hemorrhage

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Published on: November 21, 2013

Multimodality Diagnosis of Mesenteric Ischemia

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Published on: July 21, 2023

Area of Science:

Biomedical Engineering
Physiological Monitoring
Medical Device Technology

Background:

Internal hemorrhage poses a significant clinical challenge, often complicated by fluid resuscitation.
Current methods for hemorrhage detection can be invasive or lack accuracy during resuscitation.
Continuous, noninvasive monitoring of hematocrit is desirable for early hemorrhage detection.

Purpose of the Study:

To develop and evaluate sequential inference algorithms for detecting and estimating internal hemorrhage rates.
To address the challenge of differentiating hemorrhage from fluid resuscitation effects on hematocrit.
To enable continuous, noninvasive monitoring of internal hemorrhage.

Main Methods:

Development of two sequential inference algorithms: Luenberger observer and extended Kalman filter.
Algorithms utilize fluid resuscitation dose and hematocrit measurements as inputs.
In silico evaluation using 100 virtual patients with varied hemorrhage and resuscitation rates.

Main Results:

Sequential inference algorithms significantly outperformed naive hematocrit decrease detection (F1 scores: Luenberger 0.80, Kalman 0.76 vs. hematocrit 0.59 at 1% noise).
Extended Kalman filter provided comparable detection accuracy and superior hemorrhage rate estimation compared to the Luenberger observer.
Algorithm performance is robust to low hematocrit noise (≤1%) and benefits from personalization.

Conclusions:

Sequential inference algorithms offer a feasible approach for continuous, noninvasive internal hemorrhage detection and rate estimation.
The extended Kalman filter shows promise for accurate hemorrhage rate estimation during resuscitation.
Minimizing measurement noise and personalizing algorithms are key for clinical translation.