ArticleSensors (Basel, Switzerland)2024
Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 42 citations in OpenAlex.
- Grad-CAM based deep learning analytics for image-level colon disease classification based on graph neural networks and vision transformers.Frontiers in physiology · 2026Article
- Breathomics in Diabetes Management: A Noninvasive Approach for Precision Health Monitoring.Journal of diabetes research · 2026Review
- Machine Learning-Driven E-Nose-Based Diabetes Detection: Sensor Selection and Feature Reduction Study.Sensors (Basel, Switzerland) · 2025Article
- Artificial intelligence-driven transformative applications in disease diagnosis technology.Medical review (2021) · 2025Review
- Review
- Chemical Nose-Based Non-Invasive Detection of Breast Cancer Using Exhaled Breath.Sensors (Basel, Switzerland) · 2025Article
- Theoretical Study of Gas Sensing toward Acetone by a Single-Atom Transition Metal (Sc, Ti, V, and Cr)-Doped InPACS omega · 2024Article
- Enhanced Diabetes Detection and Blood Glucose Prediction Using TinyML-Integrated E-Nose and Breath Analysis: A Novel Approach Combining Synthetic and Real-World Data.Bioengineering (Basel, Switzerland) · 2024Article
- Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions.ACS sensors · 2024Review
- Enhance Ethanol Sensing Performance of Fe-Doped Tetragonal SnOSensors (Basel, Switzerland) · 2024Article
- Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study.Respiratory research · 2024Article
- AI-Driven Sensing Technology: Review.Sensors (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers for disease identification and medical diagnostics. In the context of diabetes mellitus, the noninvasive detection of acetone, a primary biomarker using electronic noses (e-noses), has gained significant attention. However, employing e-noses requires pre-trained algorithms for precise diabetes detection, often requiring a computer with a programming environment to classify newly acquired data. This study focuses on the development of an embedded system integrating Tiny Machine Learning (TinyML) and an e-nose equipped with Metal Oxide Semiconductor (MOS) sensors for real-time diabetes detection. The study encompassed 44 individuals, comprising 22 healthy individuals and 22 diagnosed with various types of diabetes mellitus. Test results highlight the XGBoost Machine Learning algorithm's achievement of 95% detection accuracy. Additionally, the integration of deep learning algorithms, particularly deep neural networks (DNNs) and one-dimensional convolutional neural network (1D-CNN), yielded a detection efficacy of 94.44%. These outcomes underscore the potency of combining e-noses with TinyML in embedded systems, offering a noninvasive approach for diabetes mellitus detection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.