Spectral Md, Inc.

Jason Dwight

Zhicun Gao

John Michael DiMaio

Wensheng Fan

Jeffrey E Thatcher

Brian McCall

Ronald Baxter

Peiran Quan

Faliu Yi

Kevin Plant

Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, determine reflectance intensity values for at least a subset of the pixels, determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values, and generate a predicted or assessed healing parameter associated with the wound over a predetermined time interval.

Machine learning systems and methods for assessment, healing prediction, and treatment of wounds

Generally described, one or more aspects of the present application correspond to systems and techniques for spectral imaging using a multi-aperture system with curved multi-bandpass filters positioned over each aperture. The present disclosure further relates to techniques for implementing spectral unmixing and image registration to generate a spectral datacube using image information received from such imaging systems. Aspects of the present disclosure relate to using such a datacube to analyze the imaged object, for example to analyze tissue in a clinical setting, perform biometric recognition, or perform materials analysis.

System and method for high precision multi-aperture spectral imaging

Machine learning systems and method for assessment, healing prediction, and treatment of wounds

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Dallas, TX, United States of America

Jason Dwight