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Updated: Mar 27, 2026

Detection of Invasive Pulmonary Aspergillosis in Haematological Malignancy Patients by using Lateral-flow Technology

Detection of Invasive Pulmonary Aspergillosis in Haematological Malignancy Patients by using Lateral-flow Technology

Published on: March 22, 2012

Machine learning-augmented lateral flow assays for point-of-care infectious disease diagnostics.

Cagla Parmaksizoglu^1,2, Isil Cakiroglu^1,2, Nazente Atceken^1,2,3,4,5

¹School of Biomedical Sciences and Engineering, Koç University, Istanbul, 34450, Turkiye. stasoglu@ku.edu.tr.

|March 25, 2026

Summary

This summary is machine-generated.

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.

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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.