Deep learning powered optoacoustic mesoscopy for non-invasive diagnostics of skin diseases

Decorative image, HI Deep4OM
Image: Hailong He, Helmholtz Munich

Raster-scan optoacoustic mesoscopy (RSOM) has achieved unprecedented resolutions in the non-invasive assessment of human skin anatomy and vasculature in vivo through the whole skin depth. It shows great potential for diagnostics and therapy monitoring in skin and other diseases. However, automated and quantitative analysis of three-dimensional RSOM datasets remains a challenge and prevents its clinical translation.

Deep4OM aims to develop the first quantitative image analysis framework based on deep learning techniques that is able to automatically analyze the high resolution RSOM images and derive various skin biomarkers for disease classification. The developed method will be tested on RSOM and histological data acquired from patients suffering from various skin diseases. As such, Deep4OM has the potential to change the landscape of non-invasive skin imaging, and could significantly promote the diagnostic and prognostic applications of RSOM in clinical routine.

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