Hyperspectral learning of colors for mHealth applications

Optical spectrometers provide detailed molecular and structural information, making them invaluable for a wide range of diagnostic applications. Traditionally, achieving high spectral resolution has relied on advanced hardware solutions, which limits practicality and affordability. We introduce hybrid machine learning frameworks that reconstruct full-resolution spectral information from the blue, green, and red (RGB) values of smartphone photos. Rather than viewing a photo as merely a visual snapshot, our platforms extract spectral data to uncover health indicators and biological or physiological cues. Using tissue optics-informed learning frameworks, we generate hyperspectral datasets, while biophysics-informed hybrid learning frameworks reduce the dependency on extensive training datasets. Unlike conventional data-driven machine learning approaches often championed by tech giants, our methodology integrates domain-specific knowledge from biology and physics of tissue optics and spectroscopy. By imaging peripheral sensing sites, the platforms enable rapid assessment of hemodynamic parameters and tissue oxygenation, both critical health markers. At the intersection of optical spectroscopy, color science, and machine learning, our approach has the potential to transform smartphone cameras into powerful tools for noninvasive diagnostics. The development of hybrid learning algorithms not only minimizes hardware complexity but also enhances explainability, paving the way for accessible and interpretable diagnostic solutions.