Systems, methods, and devices for diagnostics based on live un-scattering computational imaging

Machine-learning based medical image processing techniques using NIR and SWIR signals address the limitations of current imaging methods by enabling high-resolution, non-invasive, and portable detection of subsurface tissue features, facilitating early disease detection.

WO2026128670A1 Publication Date: 2026-06-18OSELVA INC
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Patent Information

Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
OSELVA INC
Filing Date
2025-12-11
Publication Date
2026-06-18

AI Technical Summary

⚠Technical Problem

Current medical imaging techniques for deep-tissue live imaging of human samples are limited by depth and resolution, requiring specialized equipment and skilled operators, and are not suitable for portable, low-cost, or non-invasive applications, especially for micron-scale features below the surface.

⚗Method used

Employing machine-learning based medical image processing techniques that emit electromagnetic signals, such as Near Infrared (NIR) and Short-Wave Infrared (SWIR), to penetrate tissues, correct for scattering, and reconstruct high-resolution images using machine learning models to identify micron-scale features of interest.

🎯Benefits of technology

Enables high-resolution, low-cost, and non-invasive imaging of subsurface tissue features, allowing early detection of medical conditions and disease states, and is suitable for portable and wearable devices.

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Abstract

Methods for identifying micron-scale features below a surface of a tissue include emitting from one or more emitters at the surface of the tissue electromagnetic signals capable of penetrating tissue of different densities; obtaining from a sensor positioned at the surface of the tissue electromagnetic signals reflected from or transmitted through tissues of different densities as one or more obtained images; inputting the one or more obtained images into a machine learning model, wherein the machine learning model is trained to: identify scattering of certain electromagnetic signals comprising each of the one or more obtained images; correct for the scattering to create one or more images tissues at one or more subsurface depths; and identify micron-scale features of interest within the one or more images of tissues at the one or more subsurface depths.
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