Spectral Md, Inc.

University of Texas System

John Michael DiMaio

Wensheng Fan

Jeffrey E Thatcher

Zhicun Gao

Jason Dwight

Brian McCall

Kevin Plant

Ronald Baxter

Peiran Quan

Faliu Yi

Weizhi Li

Weirong Mo

James Watson

Jose Melendez

Roma Moza

Harry Tibbals

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

Certain aspects relate to apparatuses and techniques for non-invasive and non-contact optical imaging that acquires a plurality of images corresponding to both different times and different frequencies. Additionally, alternatives described herein are used with a variety of tissue classification applications including assessing the presence and severity of tissue conditions, such as necrosis and small vessel disease, at a potential or determined amputation site.

Machine learning systems and techniques for multispectral amputation site analysis

Certain aspects relate to apparatuses and techniques for non-invasive optical imaging that acquires a plurality of images corresponding to both different times and different frequencies. Additionally, alternatives described herein are used with a variety of tissue classification applications, including assessing the presence and severity of tissue conditions, such as burns and other wounds.

Reflective mode multi-spectral time-resolved optical imaging methods and apparatuses for tissue classification

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

The present invention is a multi-wavelength diagnostic imager. In one embodiment, the present invention includes an apparatus and method for noninvasive evaluation of a target versus a non-target, comprising: one or more light sources having at least one emission spectra directed at the target wherein the position, orientation and intensity of light sources is varied to control near-surface reflectance and are directed at less than the entire target; and one or more detectors positioned to capture light reflected from the target into two or more spatial images of the target at two or more times, wherein the spatial images are used to distinguish between the target and the non-target.

Multi-wavelength diagnostic imager

The present invention includes an integrated system and methods for patient treatment, the system includes a hospital bed; a plurality of patient diagnostic and treatment devices connected to a network, wherein each of the devices can communicate to a network and exchange information with the network about the care of a patient; and a processor accessible adjacent to the bed and connected to the network to integrate information obtained from the devices through the network with one or more additional sources of information databases, wherein the processor can communicate to one or more patient treatment devices either directly or via the network and the processor directs the one or more patient treatment devices to change the treatment of the patient.

Integrated patient bed system

Growing community of inventors

Dallas, TX, United States of America

John Michael DiMaio