194. Early salivary diagnosis using Raman Spectroscopy and Machine Learning
Contributed abstract in session FB-4: Artificial Intelligence, stream Regular talks.
Friday, 11:00-12:30Room: Room S3
Authors (first author is the speaker)
| 1. | Marco Piazza
|
| Computer Science, University of Milan-Bicocca | |
| 2. | Enza Messina
|
| DISCo - Department of Informatics, Systems and Communication, University of Milano Bicocca | |
| 3. | Mauro Passacantando
|
| Department of Business and Law, University of Milano-Bicocca |
Abstract
Raman Spectroscopy is a technique based on the inelastic scattering of a monochromatic light beam used to observe low-frequency modes in a target molecular system. The scattering pattern can be viewed as a sort of ”fingerprint” that encodes information about the chemical composition of a given target.
Recently, Raman spectra of biofluids, and in particular saliva, have been proposed for medical diagnosis, leveraging the power of Machine Learning for automatic sample classification.
In this talk, we will discuss the initial results of the CORSAI project, which focuses on utilizing this combined technology to develop non-invasive and portable early diagnostic tools. In particular, we address the problem of the black-box nature of ML algorithms by integrating into the classification pipeline Explainability methods based on Game Theory. This serves a double purpose: providing insights into the classification reasons for a specific sample and guiding medical research towards the identification of novel biomarkers associated with a specific disease. To assess the validity of the proposed system we evaluated it on the diagnosis of COVID-19, as well as on Chronic Obstructive Pulmonary Disease (COPD). The selected case studies are characterized by specific challenges that we discuss and address within our method.
Keywords
- Artificial Intelligence
- Decision support
Status: accepted
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