AIMC Topic: Breath Tests
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Deep Learning Image Analysis of Nanoplasmonic Sensors: Toward Medical Breath Monitoring.
ACS applied materials & interfaces
Nov 23, 2022
Sensing biomarkers in exhaled breath offers a potentially portable, cost-effective, and noninvasive strategy for disease diagnosis screening and monitoring, while high sensitivity, wide sensing range, and target specificity are critical challenges. W...
A pilot study for the prediction of liver function related scores using breath biomarkers and machine learning.
Scientific reports
Feb 7, 2022
Volatile organic compounds (VOCs) present in exhaled breath can help in analysing biochemical processes in the human body. Liver diseases can be traced using VOCs as biomarkers for physiological and pathophysiological conditions. In this work, we pro...
Deep learning enabled classification of real-time respiration signals acquired by MoSSe quantum dot-based flexible sensors.
Journal of materials chemistry. B
Aug 16, 2021
Respiration rate is a vital parameter which is useful for the earlier identification of diseases. In this context, various types of devices have been fabricated and developed to monitor different breath rates. However, the disposability and biocompat...
Assessment of an Exhaled Breath Test Using High-Pressure Photon Ionization Time-of-Flight Mass Spectrometry to Detect Lung Cancer.
JAMA network open
Mar 1, 2021
IMPORTANCE: Exhaled breath is an attractive option for cancer detection. A sensitive and reliable breath test has the potential to greatly facilitate diagnoses and therapeutic monitoring of lung cancer.
Recent Advancements and Future Prospects on E-Nose Sensors Technology and Machine Learning Approaches for Non-Invasive Diabetes Diagnosis: A Review.
IEEE reviews in biomedical engineering
Jan 22, 2021
Diabetes mellitus, commonly measured through an invasive process which although is accurate, has manifold drawbacks especially when multiple reading are required at regular intervals. Accordingly, there is a need to develop a dependable non-invasive ...
Breath biopsy of breast cancer using sensor array signals and machine learning analysis.
Scientific reports
Jan 8, 2021
Breast cancer causes metabolic alteration, and volatile metabolites in the breath of patients may be used to diagnose breast cancer. The objective of this study was to develop a new breath test for breast cancer by analyzing volatile metabolites in t...
A comparison of regularized logistic regression and random forest machine learning models for daytime diagnosis of obstructive sleep apnea.
Medical & biological engineering & computing
Aug 17, 2020
A major challenge in big and high-dimensional data analysis is related to the classification and prediction of the variables of interest by characterizing the relationships between the characteristic factors and predictors. This study aims to assess ...
Using machine learning for real-time BAC estimation from a new-generation transdermal biosensor in the laboratory.
Drug and alcohol dependence
Aug 1, 2020
BACKGROUND: Transdermal biosensors offer a noninvasive, low-cost technology for the assessment of alcohol consumption with broad potential applications in addiction science. Older-generation transdermal devices feature bulky designs and sparse sampli...
Modeling motivation for alcohol in humans using traditional and machine learning approaches.
Addiction biology
Jul 29, 2020
Given the significant cost of alcohol use disorder (AUD), identifying risk factors for alcohol seeking represents a research priority. Prominent addiction theories emphasize the role of motivation in the alcohol seeking process, which has largely bee...
Correlating exhaled aerosol images to small airway obstructive diseases: A study with dynamic mode decomposition and machine learning.
PloS one
Jan 31, 2019
BACKGROUND: Exhaled aerosols from lungs have unique patterns, and their variation can be correlated to the underlying lung structure and associated abnormities. However, it is challenging to characterize such aerosol patterns and differentiate their ...
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