HyperHealth: a pilot study on AI-driven COVID-19 detection using hyperspectral fingertip images

Area of Science:

• Biomedical Engineering
• Artificial Intelligence
• Spectroscopy

Background:

• The need for rapid infectious disease screening persists despite COVID-19 advancements.
• Limited data from COVID-19 positive individuals hinders traditional AI diagnostic methods.
• Hyperspectral Imaging (HSI) shows potential for disease detection.

Purpose of the Study:

• To develop and validate a novel AI-integrated HSI framework for detecting COVID-19 status.
• To demonstrate the feasibility of using fingertip biometric data for infection detection.
• To establish the first publicly available dataset of HSI images from COVID-19 positive individuals.

Main Methods:

• Integration of HSI with Artificial Intelligence (AI) analysis.
• Analysis of high-dimensional spectral signatures from fingertip images.
• Utilized Support Vector Machine (SVM) and Logistic Regression for classification.

Main Results:

• The AI-HSI framework successfully identified distinctive spectral patterns linked to COVID-19.
• Classification algorithms achieved high accuracy in identifying COVID-19 status from HSI images.
• Demonstrated the potential of hyperspectral features for non-invasive health monitoring.

Conclusions:

• The developed AI-HSI framework shows significant potential for non-invasive, real-time infectious disease screening.
• This approach can aid in developing advanced AI-powered diagnostics.
• The creation of a public HSI dataset for COVID-19 advances biometric spectral imaging research.