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Coagulation

Coagulation

Colloidal solids are solid particles suspended in solution. They are usually negatively charged, attracting a compact primary layer of positively charged ions, which attract more counterions to form an electrical double layer. Electrostatic repulsion between the charged double layers prevents the particles from colliding, stabilizing the colloids. These solids are often undesirable because they can contain toxins that are difficult to remove. Coagulation is a technique that helps aggregate and...

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Related Experiment Video

Updated: Nov 15, 2025

Leveraging Turbidity and Thromboelastography for Complementary Clot Characterization

Leveraging Turbidity and Thromboelastography for Complementary Clot Characterization

Published on: June 4, 2020

Using machine learning to identify clotted specimens in coagulation testing.

Kui Fang¹, Zheqing Dong¹, Xiling Chen¹

¹Clinical Laboratory, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, P.R. China.

Clinical Chemistry and Laboratory Medicine

|March 4, 2021

Summary

Machine learning (ML) can effectively identify clotted specimens in coagulation testing, preventing inaccurate results and improving clinical decisions. This automated approach enhances laboratory efficiency and sample quality assessment.

Keywords:

blood clot coagulation assays laboratory automation machine learning neural network quality control

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Last Updated: Nov 15, 2025

Leveraging Turbidity and Thromboelastography for Complementary Clot Characterization

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Published on: June 4, 2020

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Published on: March 19, 2016

Author Spotlight: A Novel Method for Comprehensive Cell Component Analysis of Cerebral Blood Clots

Author Spotlight: A Novel Method for Comprehensive Cell Component Analysis of Cerebral Blood Clots

Published on: July 21, 2023

Area of Science:

Clinical pathology
Medical laboratory science
Artificial intelligence in healthcare

Background:

Blood clots in samples can lead to inaccurate coagulation test results.
Current methods for detecting clots are inefficient and often manual.
Inaccurate testing can result in improper clinical decisions by healthcare providers.

Purpose of the Study:

To demonstrate the feasibility of using machine learning (ML) to automatically detect clotted specimens.
To develop and validate ML models for identifying sample status in coagulation testing.
To improve the accuracy and efficiency of clinical laboratory diagnostics.

Main Methods:

Retrospective analysis of 192 clotted and 2,889 no-clot-detected (NCD) samples.
Training and validation of standard and momentum backpropagation neural networks (BPNNs) using a five-fold cross-validation method.
Assessment of model predictive performance on a separate testing dataset.

Main Results:

Intrinsic distinctions were observed between clotted and NCD specimens in testing results and t-SNE analysis.
Standard BPNN achieved an area under the ROC curve of 0.966 (95% CI, 0.958-0.974).
Momentum BPNN achieved an area under the ROC curve of 0.971 (95% CI, 0.9641-0.9784), demonstrating high accuracy in identifying sample status.

Conclusions:

ML algorithms can successfully identify sample status (clotted vs. NCD) in coagulation testing.
This study presents a proof-of-concept for ML in evaluating clinical laboratory sample quality.
The developed ML approach has the potential to significantly advance clinical laboratory automation and diagnostic reliability.