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Related Experiment Video

Updated: Jan 18, 2026

Development of a Lateral Flow Immunochromatographic Strip for Rapid and Quantitative Detection of Small Molecule Compounds

Development of a Lateral Flow Immunochromatographic Strip for Rapid and Quantitative Detection of Small Molecule Compounds

Published on: November 13, 2021

2SLOD-HCG: HCG Test Strip Concentration Prediction Network.

Qi Hu¹, Jinshu Zhao², Shimin Kan¹

¹School of Artificial Intelligence, Changchun University of Science and Technology, Changchun 130022, China.

Sensors (Basel, Switzerland)

|September 13, 2025

Summary

This study introduces 2SLOD-HCG, a novel AI network for accurately detecting human chorionic gonadotropin (HCG) levels from test strips. It improves upon existing methods by enhancing feature perception for more reliable pregnancy diagnostics.

Keywords:

2SLOD-HCG concentration detection lightweight attention mechanism multi-scale fusion

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Published on: November 13, 2021

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A Precision Medicine Tool for Measurement and Monitoring of Hemoglobin S in Sickle Cell Disease Patients Receiving Transfusion Therapy

A Precision Medicine Tool for Measurement and Monitoring of Hemoglobin S in Sickle Cell Disease Patients Receiving Transfusion Therapy

Area of Science:

Biomedical engineering
Medical diagnostics
Artificial intelligence in healthcare

Background:

Human chorionic gonadotropin (HCG) is a critical biomarker for pregnancy detection.
Current HCG test strip interpretation faces challenges including user error, AI limitations, and variable image quality.
Accurate and accessible HCG detection is vital for timely diagnosis of pregnancy-related conditions.

Purpose of the Study:

To develop a robust and accurate AI-based method for HCG test strip concentration detection.
To overcome limitations of existing AI detection methods and improve reliability under diverse imaging conditions.
To enhance the diagnostic capabilities for early pregnancy, multiple pregnancies, and ectopic pregnancies using automated test strip analysis.

Main Methods:

Proposed 2SLOD-HCG, a novel network featuring an enhanced spatial pyramid pooling (SPP) module for multi-scale information integration.
Incorporated an elastic variational cross-FPN with lightweight transformer blocks for improved global feature perception.
Applied a SimAM attention mechanism to emphasize critical local features in test strip images.
Created a dataset of 50,000 augmented test strip images under varied lighting and mobile photography conditions.

Main Results:

The 2SLOD-HCG network demonstrated superior accuracy and robustness compared to YOLO-based baselines.
The model excelled at detecting small, crucial color-developing regions on HCG test strips.
Performance improvements were noted across various lighting conditions and mobile photography scenarios.

Conclusions:

The 2SLOD-HCG network offers a significant advancement in automated HCG test strip analysis.
This approach enhances the reliability and accuracy of AI-driven pregnancy diagnostics.
The method shows promise for more accessible and precise early pregnancy evaluations.