AIMC Topic: Breath Tests
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Ppb-Level Ammonia Sensor for Exhaled Breath Diagnosis Based on UV-DOAS Combined with Spectral Reconstruction Fitting Neural Network.
ACS sensors
Jul 30, 2024
Ammonia (NH) in exhaled breath (EB) has been a biomarker for kidney function, and accurate measurement of NH is essential for early screening of kidney disease. In this work, we report an optical sensor that combines ultraviolet differential optical ...
Smell cancer by machine learning-assisted peptide/MXene bioelectronic array.
Biosensors & bioelectronics
Jul 10, 2024
Non-invasive detection of tumors is of utmost importance to save lives. Nonetheless, identifying tumors through gas analysis is a challenging task. In this work, biosensors with remarkable gas-sensing characteristics were developed using a self-assem...
Pioneering noninvasive colorectal cancer detection with an AI-enhanced breath volatilomics platform.
Theranostics
Jul 8, 2024
The sensitivity and specificity of current breath biomarkers are often inadequate for effective cancer screening, particularly in colorectal cancer (CRC). While a few exhaled biomarkers in CRC exhibit high specificity, they lack the requisite sensit...
Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study.
Respiratory research
May 10, 2024
BACKGROUND: Although electronic nose (eNose) has been intensively investigated for diagnosing lung cancer, cross-site validation remains a major obstacle to be overcome and no studies have yet been performed.
Graphene and metal-organic framework hybrids for high-performance sensors for lung cancer biomarker detection supported by machine learning augmentation.
Nanoscale
May 9, 2024
Conventional diagnostic methods for lung cancer, based on breath analysis using gas chromatography and mass spectrometry, have limitations for fast screening due to their limited availability, operational complexity, and high cost. As potential repla...
The Classification of VOCs Based on Sensor Images Using a Lightweight Neural Network for Lung Cancer Diagnosis.
Sensors (Basel, Switzerland)
Apr 28, 2024
The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection, which detects the patient's exhaled VOCs, combined with sensor arrays of convolutional neural net...
User authentication system based on human exhaled breath physics.
PloS one
Apr 22, 2024
This work, in a pioneering approach, attempts to build a biometric system that works purely based on the fluid mechanics governing exhaled breath. We test the hypothesis that the structure of turbulence in exhaled human breath can be exploited to bui...
Ultra-sensitive analysis of exhaled biomarkers in ozone-exposed mice via PAI-TOFMS assisted with machine learning algorithms.
Journal of hazardous materials
Mar 27, 2024
Ground-level ozone ranks sixth among common air pollutants. It worsens lung diseases like asthma, emphysema, and chronic bronchitis. Despite recent attention from researchers, the link between exhaled breath and ozone-induced injury remains poorly un...
Artificial intelligence can dynamically adjust strategies for auxiliary diagnosing respiratory diseases and analyzing potential pathological relationships.
Journal of breath research
Aug 25, 2023
Respiratory diseases are one of the leading causes of human death and exacerbate the global burden of non-communicable diseases. Finding a method to assist clinicians pre-diagnose these diseases is an urgent task. Existing artificial intelligence-bas...
Assessment of Helicobacter pylori infection by deep learning based on endoscopic videos in real time.
Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
Mar 3, 2023
BACKGROUND AND AIMS: Endoscopic assessment of Helicobacter pylori infection is a simple and effective method. Here, we aimed to develop a deep learning-based system named Intelligent Detection Endoscopic Assistant-Helicobacter pylori (IDEA-HP) to ass...
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