Machine Learning-Augmented Lateral Flow Assays for Point-of-Care Infectious Disease Diagnostics

Cagla Parmaksizoglu; Isil Cakiroglu; Nazente Atçeken; Eden Morales-Narvaez; Ali K. Yetisen; Savas Tasoglu

doi:10.1039/d5lc01124h

January 1, 2026Lab on a ChipOpen Access

Machine Learning-Augmented Lateral Flow Assays for Point-of-Care Infectious Disease Diagnostics

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Key Points

The aim is to enhance lateral flow assays with machine learning to improve their effectiveness for diagnosing infectious diseases.
Incorporated machine learning algorithms into traditional lateral flow assays.
Evaluated diagnostic performance through comparative analysis.
Tested the system across various infectious disease biomarkers.
Machine learning-augmented LFAs demonstrated higher sensitivity and specificity compared to conventional LFAs.
Results showed quicker reading times and increased accuracy in diagnostics.

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Implication

Machine learning enhances diagnostic accuracy in point-of-care settings for infectious diseases, indicating improved outcomes.

Abstract

Lateral flow assays (LFAs) are among the most widely used point-of-care (PoC) diagnostic platforms for infectious diseases due to their rapid operation, low cost, and user-friendly architecture. However, conventional LFAs...

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Cite This Study

Parmaksizoglu et al. (2026) studied this question.

synapsesocial.com/papers/69bb9257496e729e6297f947 https://doi.org/10.1039/d5lc01124h

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Machine Learning-Augmented Lateral Flow Assays for Point-of-Care Infectious Disease Diagnostics

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Machine Learning-Augmented Lateral Flow Assays for Point-of-Care Infectious Disease Diagnostics

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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