Machine learning systems and techniques for multispectral amputation site analysis

Abstract

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.

1 . A tissue classification system comprising:
 at least one light emitter configured to emit light at each of a plurality of wavelengths to illuminate a tissue region, each of the at least one light emitter being configured to emit spatially-even light;   at least one light detection element configured to collect the light after being emitted from the at least one light emitter and reflected from the tissue region;   one or more processors in communication with the at least one light emitter and the at least one light detection element and configured to:
 identify at least one patient health metric value corresponding to a patient having the tissue region, 
 use the at least one patient health metric value select a classifier from among a plurality of classifiers, each of the plurality of classifiers trained from a different subset of a set of training data, wherein the classifier is selected based on having been trained with a subset of the set of the training data including data from other patients having the at least one patient health metric value; 
 control the at least one light emitter to sequentially emit each of the plurality of wavelengths of light; 
 receive a plurality of signals from the at least one light detection element, a first subset of the plurality of signals representing light emitted at the plurality of wavelengths and reflected from the tissue region; 
 generate, based on at least some of the plurality of signals, an image having a plurality of pixels depicting the tissue region; 
 for each pixel of the plurality of pixels depicting the tissue region:
 determine, based on the first subset of the plurality of signals, a reflectance intensity value at the pixel at each of the plurality of wavelengths, and 
 determine a classification of the pixel by inputting the reflectance intensity value into the classifier, the classification associating the pixel with one of a plurality of tissue categories; and 
 
 generate, based on the classification of each pixel, a mapping of the plurality of tissue categories over the plurality of pixels depicting the tissue region. 
   
     
     
         2 - 38 . (canceled)