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Review
. 2023 Dec 15;13(24):3673.
doi: 10.3390/diagnostics13243673.

Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions

Ioannis K Gallos  1 , Dimitrios Tryfonopoulos  1 , Gidi Shani  2 , Angelos Amditis  1 , Hossam Haick  2 , Dimitra D Dionysiou  1
Affiliations
Review

Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions

Ioannis K Gallos et al. Diagnostics (Basel). .

Abstract

Early detection of colorectal cancer is crucial for improving outcomes and reducing mortality. While there is strong evidence of effectiveness, currently adopted screening methods present several shortcomings which negatively impact the detection of early stage carcinogenesis, including low uptake due to patient discomfort. As a result, developing novel, non-invasive alternatives is an important research priority. Recent advancements in the field of breathomics, the study of breath composition and analysis, have paved the way for new avenues for non-invasive cancer detection and effective monitoring. Harnessing the utility of Volatile Organic Compounds in exhaled breath, breathomics has the potential to disrupt colorectal cancer screening practices. Our goal is to outline key research efforts in this area focusing on machine learning methods used for the analysis of breathomics data, highlight challenges involved in artificial intelligence application in this context, and suggest possible future directions which are currently considered within the framework of the European project ONCOSCREEN.

Keywords: ONCOSCREEN; artificial intelligence; automated diagnosis; breathomics; colorectal cancer; machine learning; manifold learning; validation; volatile organic compounds.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Schematic representation of the topics discussed within the scope of this study.
See this image and copyright information in PMC

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