What does this research mean for the field?

The AI-enhanced wireless breath sensing platform achieves up to 96% classification accuracy in detecting lung cancer using optimized sensor arrays and multimodal machine learning. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to improve early detection of lung cancer through an innovative breath sensing platform using AI techniques.

March 7, 2026

Artificial Intelligence-Enhanced Optimization of Wireless Breath Sensor Arrays for Detection of Lung Cancer Using Fuzzy Logic-Guided Genetic Algorithm and Multimodal Machine Learning

Key Result

The AI-optimized wireless breath sensor array achieved up to 96% accuracy in detecting lung cancer from breath samples, with reduced sensor complexity.

Key Points

The aim is to improve early detection of lung cancer through an innovative breath sensing platform using AI techniques.
Developed a wireless breath sensing platform with nanostructured chemiresistive sensor arrays.
Utilized a Fuzzy logic-guided Genetic Algorithm for sensor optimization and VOC detection.
Analyzed breath samples from lung cancer patients and non-cancer participants using various machine learning models.
Implemented a real-time data transmission system for effective monitoring.
Achieved high classification accuracy of up to 96% in detecting lung cancer.
Reduced the size of the sensor array while maintaining diagnostic performance.
Demonstrated lower costs and improved scalability for real-world application.

Structured PICO

Does an AI-enhanced wireless breath sensor array accurately detect lung cancer compared to non-cancer controls?

Population

82 participants, comprising lung cancer patients (n=35) and non-cancer participants (n=47)

Intervention

Wireless breath sensing platform combining nanostructured chemiresistive sensor arrays with an AI-driven Fuzzy logic-guided Genetic Algorithm (Fuzzy-GA) and multimodal machine learning

Comparator

Non-cancer control participants

Outcome

Classification accuracy for lung cancer detectionsurrogate

An AI-optimized wireless breath sensor array demonstrates high accuracy (up to 96%) for the non-invasive detection of lung cancer.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

Early detection of lung cancer remains critical for improving patient survival, yet current imaging-based screening methods are costly, invasive, and limited in accessibility. Here, we present a fully integrated wireless breath sensing platform that combines nanostructured chemiresistive (NC) sensor arrays with an AI-driven Fuzzy logic-guided Genetic Algorithm (Fuzzy-GA) for optimized volatile organic compound (VOC) detection. The sensor array features nanoparticle structured interfaces, enabling selective VOC adsorption to generate unique breath patterns. Data are captured via a portable low-current multichannel electronics module with real-time wireless transmission. Fuzzy-GA optimization identifies the most informative sensors, reducing array size while maintaining high diagnostic performance. Breath samples from lung cancer patients (n = 35) and non-cancer participants (n = 47) were analyzed using multiple supervised machine learning models (KNN, SVM, Random Forest, XGBoost, and CNN). This represents the first application of Fuzzy-GA to optimize breath sensor arrays. The optimized system, validated using breath samples from lung cancer patients and non-lung cancer controls, achieved high classification accuracy (up to 96%) with reduced system complexity, lower cost, and improved scalability for real-world deployment. The platform offers a clinically viable, non-invasive diagnostic tool with potential for at-home monitoring and broader disease detection.

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