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- Title
AI Applied to Volatile Organic Compound (VOC) Profiles from Exhaled Breath Air for Early Detection of Lung Cancer.
- Authors
Vinhas, Manuel; Leitão, Pedro M.; Raimundo, Bernardo S.; Gil, Nuno; Vaz, Pedro D.; Luis-Ferreira, Fernando
- Abstract
Simple Summary: Lung cancer stands as a serious health challenge, prompting the exploration of innovative detection methods. Volatile organic compounds (VOCs) found in exhaled breath air are becoming a relevant opportunity for early cancer detection, including lung cancer, without invasive procedures or high costs. Unlike traditional approaches, which target specific compounds, this study analyzes overall compositional profiles, maximizing detection efficiency. The results highlight the potential of AI-driven techniques in revolutionizing early cancer detection for clinical use. Volatile organic compounds (VOCs) are an increasingly meaningful method for the early detection of various types of cancers, including lung cancer, through non-invasive methods. Traditional cancer detection techniques such as biopsies, imaging, and blood tests, though effective, often involve invasive procedures or are costly, time consuming, and painful. Recent advancements in technology have led to the exploration of VOC detection as a promising non-invasive and comfortable alternative. VOCs are organic chemicals that have a high vapor pressure at room temperature, making them readily detectable in breath, urine, and skin. The present study leverages artificial intelligence (AI) and machine learning algorithms to enhance classification accuracy and efficiency in detecting lung cancer through VOC analysis collected from exhaled breath air. Unlike other studies that primarily focus on identifying specific compounds, this study takes an agnostic approach, maximizing detection efficiency over the identification of specific compounds focusing on the overall compositional profiles and their differences across groups of patients. The results reported hereby uphold the potential of AI-driven techniques in revolutionizing early cancer detection methodologies towards their implementation in a clinical setting.
- Subjects
RESEARCH funding; EARLY detection of cancer; ARTIFICIAL intelligence; RESPIRATION; RESEARCH evaluation; DESCRIPTIVE statistics; LUNG tumors; ORGANIC compounds; MACHINE learning; BREATH tests; ALGORITHMS; MEDICAL care costs
- Publication
Cancers, 2024, Vol 16, Issue 12, p2200
- ISSN
2072-6694
- Publication type
Article
- DOI
10.3390/cancers16122200