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Updated: Mar 27, 2026
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Detection of Invasive Pulmonary Aspergillosis in Haematological Malignancy Patients by using Lateral-flow Technology
Published on: March 22, 2012
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Machine learning-augmented lateral flow assays for point-of-care infectious disease diagnostics
Cagla Parmaksizoglu1,2, Isil Cakiroglu1,2, Nazente Atceken1,2,3,4,5
1School of Biomedical Sciences and Engineering, Koç University, Istanbul, 34450, Turkiye. stasoglu@ku.edu.tr.
Lab on a Chip
|March 25, 2026
Summary
Innovations in lateral flow assays (LFAs) enhance infectious disease diagnostics. Artificial intelligence (AI) and machine learning (ML) improve quantitative analysis, making LFAs more reliable point-of-care (PoC) tools.
Area of Science:
- Biomedical Diagnostics
- Nanotechnology in Medicine
- Computational Biology
Background:
- Lateral flow assays (LFAs) are crucial for point-of-care (PoC) infectious disease diagnostics due to their speed and low cost.
- Traditional LFAs suffer from limited sensitivity, qualitative results, and subjective interpretation.
- Recent advancements aim to overcome these limitations for improved pathogen detection.
Purpose of the Study:
- To review recent innovations enhancing LFA analytical capabilities.
- To explore the role of AI and machine learning (ML) in digital LFA interpretation.
- To discuss future directions for advanced LFA platforms.
Main Methods:
- Nanomaterial engineering and advanced signal amplification strategies.
- Development of multiplex assay designs and novel labels (e.g., CRISPR-assisted).
- Application of AI/ML, specifically convolutional neural networks (CNNs), for image analysis of LFAs.
Main Results:
- Innovations significantly improve detection performance for various pathogens.
- AI/ML-based image analysis enables objective, quantitative signal extraction, reducing variability.
- Digital LFA interpretation enhances sensitivity and standardization.
Conclusions:
- Advanced LFAs with AI integration offer a path towards digitally connected, quantitative, and reliable PoC diagnostics.
- Further optimization, standardization, and regulatory alignment are needed for ML-enabled platforms.
- Future research should focus on integrating robust assay engineering with AI-driven analytics.
