Machine learning systems and techniques for multispectral amputation site analysis

• US 10,750,992 B2
• Filed: 10/31/2019
• Issued: 08/25/2020
• Est. Priority Date: 03/02/2017
• Status: Active Grant

• Current Assignee: Spectral Md Inc.
• Original Assignee: Spectral Md Inc.
• Inventors: Fan, Wensheng, DiMaio, John Michael, Thatcher, Jeffrey
• Primary Examiner(s): Thomson, Bill
• Assistant Examiner(s): Asghar, Aminah
• Application Number:
  US16/670,911
• Publication Number:
  US 20200138360A1
• Time in Patent Office: 299 Days
• CPC Class Codes: A61B 5/0064 Body surface scanning
• CPC Class Codes: A61B 5/0075 by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy A61B5/0071 takes precedence
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• CPC Class Codes: A61B 5/026 Measuring blood flow A61B3/1233, A61B3/1241 take precedence
• CPC Class Codes: A61B 5/0261 using optical means, e.g. infrared light
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/441 Skin evaluation, e.g. for skin disorder diagnosis
• CPC Class Codes: A61B 5/443 Evaluating skin constituents, e.g. elastin, melanin, water
• CPC Class Codes: A61B 5/445 Evaluating skin irritation or skin trauma, e.g. rash, eczema, wound, bed sore
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G06V 10/60 relating to illumination properties, e.g. using a reflectance or lighting model
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

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.

Claims

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 non-laser, 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 to 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 healing classification score of the pixel associated with tissue healing potential by inputting the reflectance intensity value into the classifier; and

generate, based on the healing classification score of each pixel, an overall healing score associated with tissue healing potential for the plurality of pixels depicting the tissue region.

2. The system of claim 1, wherein the one or more processors are further configured to determine, based on the healing classification score of each pixel, a recommended location of an amputation within the tissue region.

3. The system of claim 1, wherein the one or more processors are further configured to associate each pixel of the plurality of pixels into one of a plurality of tissue categories comprising a healthy tissue category, a necrotic tissue category, and a vascularly diseased tissue category, and to generate, based on the association of each pixel, a mapping of the plurality of tissue categories over the plurality of pixels depicting the tissue region.

4. The system of claim 3, wherein generating the mapping comprises identifying regions of the plurality of pixels depicting tissue associated with at least one tissue category of the healthy tissue category, the necrotic tissue category, and the vascularly diseased tissue category.

5. The system of claim 4, wherein the one or more processors are configured to output the image for display, wherein the plurality of pixels depicting the tissue regions are displayed in different colors according to the associated tissue category.

6. The system of claim 1, wherein a second subset of the plurality of signals represent light of a same wavelength reflected from the tissue region at a plurality of different times, and wherein the one or more processors are configured to, for each pixel of the plurality of pixels depicting the tissue region:

determine, based on the second subset of the plurality of signals, a photoplethysmography (PPG) amplitude value at the pixel; and

determine the healing classification score of the pixel based additionally on inputting the PPG amplitude value into the classifier.

7. The system of claim 1, wherein the one or more processors are configured to output a visual representation of the healing classification scores for display to a user.

8. The system of claim 7, wherein the visual representation comprises the image having each pixel displayed with a particular visual representation selected based on the healing classification score of the pixel.

9. The system of claim 1, wherein the one or more processors are configured to determine, based on the plurality of signals, a melanin index of the tissue region.

10. The system of claim 9, wherein the one or more processors are configured to select the classifier based on at least the melanin index.

11. The system of claim 1, wherein the one or more processors are further configured to associate each pixel of the plurality of pixels into one of a plurality of tissue categories comprising viable tissue, necrotic tissue category, and tissue having small vessel disease, based on the healing classification score of each pixel.

12. The system of claim 1, wherein the one or more processors are further configured to identify at least one second patient health metric value corresponding to the patient, and wherein the healing classification score of each pixel is determined by inputting the reflectance intensity value and the at least one second patient health metric value into the classifier.

13. A tissue classification method comprising:

selecting, based on at least one patient health metric value, 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;

receiving a plurality of signals from at least one light detection element positioned to receive light reflected from a tissue region, a first subset of the plurality of signals representing light emitted as non-laser, spatially-even light at a plurality of wavelengths and reflected from the tissue region;

generating, based on at least a portion 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;

determining, 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

determining a healing classification score of the pixel associated with tissue healing potential by inputting the reflectance intensity value into the classifier; and

generating, based on the healing classification score of each pixel, an overall healing score associated with tissue healing potential for the plurality of pixels depicting the tissue region.

14. The method of claim 13, further comprising determining, based on the healing classification score of each pixel, a recommended location of an amputation within the tissue region.

15. The method of claim 13, further comprising associating each pixel of the plurality of pixels into one of a plurality of tissue categories comprising a healthy tissue category, a necrotic tissue category, and a vascularly diseased tissue category, and generating, based on the association of each pixel, a mapping of the plurality of tissue categories over the plurality of pixels depicting the tissue region.

16. The method of claim 15, further comprising outputting the image for display, wherein the plurality of pixels depicting the tissue regions are displayed in different colors according to the associated tissue category.

17. The method of claim 13, further comprising associating each pixel of the plurality of pixels into one of a plurality of tissue categories comprising viable tissue, necrotic tissue category, and tissue having small vessel disease, based on the healing classification score of each pixel.

18. The method of claim 13, further comprising identifying at least one second patient health metric value corresponding to a patient having the tissue region, wherein the healing classification score of each pixel is determined by inputting the reflectance intensity value and the at least one second patient health metric value into the classifier.

19. A method of identifying a recommended location of an amputation, the method comprising:

selecting a patient having a tissue region in need of amputation;

programmatically controlling, via one or more hardware processors, an imaging system to capture data representing a plurality of images of the tissue region, the data representing the plurality of images including a first subset each captured using light of a different one of a number of different wavelengths emitted as non-laser-spatially even light and reflected from the tissue region;

generating, based on at least one of the plurality of images, an image having a plurality of pixels depicting the tissue region;

for each pixel of the plurality of pixels depicting the tissue region;

determining, based on the first subset of the data representing the plurality of images, a reflectance intensity value at the pixel at each of the plurality of wavelengths, and

determining a healing classification score of the pixel associated with tissue healing potential by at least inputting the reflectance intensity value into a classifier; and

identifying, based on the healing classification score of each pixel, the recommended location of the amputation within the tissue region.

20. The method of claim 19, further comprising generating, based on the healing classification score of each pixel, a mapping of a plurality of tissue categories over the plurality of pixels depicting the tissue region.

21. The method of claim 20, wherein the plurality of tissue categories include at least a healthy tissue category, a necrotic tissue category, and a vascularly diseased tissue category.

22. The method of claim 20, further comprising outputting a visual representation of the mapping to a user, wherein the visual representation includes a visual identification of the recommended location for the amputation.

23. The method of claim 19, further comprising generating, based on the healing classification score of each pixel, an overall healing score associated with tissue healing potential for the plurality of pixels depicting the tissue region.

24. The method of claim 19, wherein the plurality of images include a second subset sequentially captured at a number of times, the method further comprising, for each pixel of the plurality of pixels depicting the tissue region:

determining, based on the second subset of the data representing the plurality of images, a photoplethysmography (PPG) amplitude value at the pixel; and

determining the healing classification score of the pixel based additionally on inputting the PPG amplitude value into the classifier.

25. The method of claim 19, further comprising associating each pixel of the plurality of pixels into one of a plurality of tissue categories comprising viable tissue, necrotic tissue category, and tissue having small vessel disease, based on the healing classification score of each pixel.

26. The method of claim 19, further comprising identifying at least one patient health metric value corresponding to the patient, wherein the healing classification score of each pixel is determined by inputting the reflectance intensity value and the at least one patient health metric value into the classifier.