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Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

Published on: December 9, 2010

Nanomaterial Innovations and Machine Learning in Gas Sensing Technologies for Real-Time Health Diagnostics.

Md Harun-Or-Rashid^1,2, Sahar Mirzaei³, Noushin Nasiri^1,2

¹NanoTech Laboratory, School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney, New South Wales 2109, Australia.

|March 10, 2025

Summary

Noninvasive breath sensors detect volatile organic compounds (VOCs) for real-time disease diagnosis. Advances in novel materials and artificial intelligence enhance sensor accuracy for personalized health monitoring.

Keywords:

advanced nanomaterials breath analysis machine learning nanostructured gas sensors personalized health monitoring volatile organic compounds

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Last Updated: May 1, 2026

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Published on: December 9, 2010

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Published on: March 9, 2018

Area of Science:

Materials Science
Biomedical Engineering
Analytical Chemistry

Background:

Breath sensors offer a noninvasive diagnostic approach by detecting volatile organic compounds (VOCs).
Current research focuses on developing advanced materials to improve sensor sensitivity and selectivity for various health conditions.

Purpose of the Study:

To review recent advancements in breath-sensing technologies, focusing on innovative materials.
To examine the role of machine learning (ML) and artificial intelligence (AI) in breath analysis and diagnostics.

Main Methods:

Review of scientific literature on breath-sensing materials (polymers, carbon-based, metal oxides).
Analysis of structural and operational principles of sensor materials for detecting disease biomarkers.
Integration of ML algorithms (CNNs, SVMs) for data interpretation and diagnostic accuracy.

Main Results:

Polymers, graphene, carbon nanotubes, ZnO, and SnO2 show potential for detecting disease-related VOCs like acetone and ammonia.
ML and AI significantly enhance the interpretation of complex breath samples and improve diagnostic accuracy.
Breath sensors can monitor multiple parameters including VOCs, airflow, temperature, and humidity.

Conclusions:

Novel sensor materials and ML-based analytics are crucial for developing sophisticated wearable breath sensors.
AI-powered breath sensors represent a promising platform for personalized, real-time, noninvasive disease detection and monitoring.