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

Abstract

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.

1 . (canceled) 
     
     
         2 . A multispectral imaging system for wound triaging, the multispectral system comprising:
 at least one light emitter configured to emit each of first and second wavelengths of light to illuminate patient tissue;   a light detection element configured to collect light emitted from the at least one light emitter and reflected from the patient tissue;   one or more processors in communication with the at least one light emitter and the light detection element and configured to:
 control the at least one light emitter to emit each of the first and second wavelengths of light toward a tissue region of a patient, the tissue region including a wound; 
 receive multispectral image data from the light detection element, the multispectral image data including at least a first image corresponding to light emitted at the first wavelength of light reflected from the tissue region and at least a second image corresponding to light emitted at the second wavelength of light reflected from the tissue region; 
 input the first and second images into a machine learning model trained to evaluate wound tissue; 
 generate, using the machine learning model, a quantitative output representing a characteristic of the wound of the patient; and 
 based on the quantitative output of the machine learning model, output information for triaging the patient. 
   
     
     
         3 . The multispectral imaging system of  claim 2 , wherein the machine learning model is trained to quantify a healing potential of wound tissue, wherein the quantitative output represents the healing potential of the wound. 
     
     
         4 . The multispectral imaging system of  claim 2 , wherein the machine learning model is trained to classify areas of imaged tissue into one or more tissue classes, wherein the qualitative output represents at least one tissue class associated with the wound, and wherein the information for triaging the patient includes an area of the wound. 
     
     
         5 . The multispectral imaging system of  claim 4 , wherein the wound is a burn, and wherein the one or more processors are configured to determine a percentage burned surface area of the patient based on the area of the wound. 
     
     
         6 . The multispectral imaging system of  claim 5 , wherein the information for triaging the patient includes a classified image representing the percentage burned surface area of the patient. 
     
     
         7 . The multispectral imaging system of  claim 5 , wherein the one or more processors are configured to:
 determine an additional percentage burned surface area of an additional patient based on an additional output of the machine learning model when provided with additional images captured at least at the first and second wavelengths of an additional tissue region of the additional patient; and   perform mass-casualty burn care triaging of at least the patient and the additional patient based on the percentage burned surface area of the patient and the additional percentage burned surface area of the additional patient.   
     
     
         8 . The multispectral imaging system of  claim 5 , wherein the one or more processors are configured to determine a treatment for the patient based at least partly on the percentage burned surface area of the patient, wherein the information for triaging the patient includes an indication of the treatment. 
     
     
         9 . The multispectral imaging system of  claim 8 , wherein the one or more processors are configured to determine an amount of fluid to administer to the patient based at least partly on the percentage burned surface area of the patient, wherein the treatment includes the amount of fluid. 
     
     
         10 . The multispectral imaging system of  claim 2 , wherein the one or more processors are configured to:
 control the at least one light emitter and the detector to capture photoplethysmographic data representing changes in blood volume in the tissue region over a period of time; and   additionally input the photoplethysmographic data into the machine learning model.   
     
     
         11 . A method for wound triaging, the method comprising:
 controlling at least one light emitter to emit each of first and second wavelengths of light to illuminate a tissue region of a patient, the tissue region including a wound;   receiving multispectral image data from a light detection element configured to collect light emitted from the at least one light emitter and reflected from the patient tissue, the multispectral image data including at least a first image corresponding to light emitted at the first wavelength of light reflected from the tissue region and at least a second image corresponding to light emitted at the second wavelength of light reflected from the tissue region;   inputting the first and second images into a machine learning model trained to evaluate wound tissue;   generating, using the machine learning model, a quantitative output representing a characteristic of the wound of the patient; and   triaging the patient based on the quantitative output of the machine learning model.   
     
     
         12 . The method of  claim 11 , wherein triaging the patient comprises determining a treatment for the patient. 
     
     
         13 . The method of  claim 12 , further comprising determining, based on the quantitative output of the machine learning model, that the patient requires a surgical procedure. 
     
     
         14 . The method of  claim 11 , wherein the machine learning model is trained to classify areas of imaged tissue into one or more tissue classes, wherein the qualitative output represents a number of pixels associated with at least one tissue class of the wound, the method further comprising determining an area of the wound based on the number of pixels. 
     
     
         15 . The method of  claim 14 , wherein the wound is a burn, the method further comprising determining a percentage burned surface area of the patient based on the area of the wound. 
     
     
         16 . The method of  claim 15 , further comprising outputting a classified image representing the percentage burned surface area of the patient. 
     
     
         17 . The method of  claim 15 , further comprising:
 determining an additional percentage burned surface area of an additional patient based on an additional output of the machine learning model when provided with additional images captured at least at the first and second wavelengths of an additional tissue region of the additional patient; and   performing mass-casualty burn care triaging of at least the patient and the additional patient based on the percentage burned surface area of the patient and the additional percentage burned surface area of the additional patient.   
     
     
         18 . The method of  claim 15 , wherein triaging the patient comprises determining a treatment for the patient based at least partly on the percentage burned surface area of the patient. 
     
     
         19 . The method of  claim 18 , further comprising determining an amount of fluid to administer to the patient based at least partly on the percentage burned surface area of the patient, wherein the treatment includes the amount of fluid. 
     
     
         20 . The method of  claim 11 , wherein the machine learning model is trained to quantify a healing potential of wound tissue, the method further comprising determining the healing potential of the wound based on the quantitative output of the machine learning model. 
     
     
         21 . The method of  claim 20 , wherein the wound is one of a diabetic foot ulcer and an amputation site, the method further comprising predicting the healing potential of the one of the diabetic foot ulcer and the amputation site.