Automated assessment of wound tissue

1 . A method of assessing a wound in a subject, the method comprising:

receiving, by a processor, one or more optical coherence tomography images of the wound; and

analyzing, by said processor, the one or more optical coherence tomography images using a deep learning model that has been trained to classify pixels in an optical coherence tomography image of a wound between a plurality of classes comprising a plurality of classes associated with different types of wound tissue, thereby obtaining for each image analyzed, an indication of the location of tissue likely to belong to each of the different types of wound tissue in the respective image,

wherein the plurality of classes associated with different types of wound tissue comprise at least a class associated with neoepidermis, a class associated with clot tissue and a class associated with granulation tissue, and said processor analyzing the one or more optical coherence tomography images of the wound using the deep learning model comprises said processor obtaining for each image analyzed an indication of the location of likely neoepidermis, likely clot tissue and likely granulation tissue in the respective image,

wherein the plurality of classes further comprise one or more classes selected from: a class associated with intact tissue, and a class associated with background.

2 . The method of claim 1 , wherein:

the method further comprises said processor training said deep learning model using a plurality of training optical coherence tomography images, wherein areas of each training image showing visual features indicative of the presence of the different types of wound tissues are labelled accordingly; and/or

the deep learning model takes as input a single image and analyzing the one or more optical coherence tomography images comprises providing each of the one or more optical coherence tomography images individually as input to the deep learning model.

3 . The method of claim 1 , wherein the indication of tissue likely to belong to each of the different types of wound tissue in the respective image is obtained by said processor as one or more segmentation maps, wherein a segmentation map is an image of the same size as the image analyzed, with pixels classified in a particular class assigned a different value from pixels that have not been classified in the particular class.

4 . The method of claim 1 , wherein:

each optical coherence tomography image of the wound shows signal from the surface of the skin of the subject to a maximum depth.

5 . The method of claim 4 , wherein the maximum depth is between 1 and 2 mm.

6 . The method of claim 1 , wherein the deep learning model is a convolutional neural network, and/or wherein the deep learning network is a u-net or a generative adversarial network, and/or wherein the deep learning network comprises a contracting path that reduces spatial information and increases feature information, and an expansive path that combines features and spatial information.

7 . The method of claim 1 , further comprising said processor applying one or more post-processing steps to the output of the deep learning model, wherein the post-processing steps comprise one or more of: smoothing the boundaries of the areas comprising pixels identified as belonging to one or more classes, and re-labelling pixels identified as belonging to one or more classes where the pixels satisfy one or more criteria applying to the neighboring pixels.

8 . The method of claim 1 , further comprising said processor determining, using the output from the deep learning model, the surface area corresponding to the pixels identified by the deep learning model as likely to belong to at least one of the different types of wound tissue in the one or more images, and the at least one type of wound tissue is selected from the group consisting of: neoepidermis, clot tissue, and granulation tissue.

9 . The method of claim 1 , further comprising:

the processor determining the volume of at least one of the different types of wound tissue in the wound, by:

analyzing a plurality of images of optical coherence tomography images of the wound using the deep learning model;

determining, using the output from the deep learning model, for each of the plurality of images, the surface area corresponding to the pixels identified as likely to belong to the respective one of the different types of wound tissue, such as the surface area corresponding to the pixels identified as likely neoepidermis, the surface area corresponding to the pixels identified by the deep learning model as likely clot tissue, and/or the surface area corresponding to the pixels identified by the deep learning model as likely granulation tissue; and

multiplying the determined surface area(s) in each image by a predetermined distance.

10 . The method of claim 1 , wherein the subject is a human subject and/or wherein the wound is a skin wound, and/or wherein the wound is a traumatic wound, a surgical wound, or a skin ulcer.

11 . A system for automated assessment of wound tissue, the system comprising: at least one processor, and at least one non-transitory computer readable medium containing instructions that, when executed by the at least one processor, cause the at least one processor to perform the method of claim 1 .

12 . The system of claim 11 , wherein the system further comprises optical coherence tomography imaging means in communication with the processor.

13 . A method for the treatment or prophylaxis of wounds in a patient susceptible to develop chronic wounds, comprising assessing the wound using the method of claim 1 .

14 . The method of claim 13 , wherein the method comprises repeating the step of assessing the wound of the patient after a period of time and/or after administering to said patient a therapeutically effective amount of a compound or composition for the treatment of wounds, and/or wherein the method comprises adjusting a course of treatment of the patient depending on the results of the assessment of the wound.

15 . A non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause the at least one processor to perform the method of claim 1 .

16 . The method of claim 1 , wherein the plurality of classes associated with different types of wound tissue further comprise a class associated with collagen and/or a class associated with liquid blood and wherein analyzing the one or more optical coherence tomography images of the wound using the deep learning model further comprises obtaining for each image analyzed an indication of the location of likely collagen and/or likely liquid blood in the respective image.

17 . The method of claim 1 , wherein the plurality of classes consists of classes associated with each of neoepidermis, clot tissue, granulation tissue, liquid blood, collagen, intact tissue and background.

18 . The method of claim 1 , wherein a plurality of optical coherence tomography images of the wound are received and analyzed by said processor, together forming a stack of images that show signal across an area of the surface of the skin of the subject.

19 . The method of claim 18 , wherein the method further comprises the processor combining the indications of the location of the tissue likely to belong to each of the different types of wound tissue, in the respective images to obtain a three-dimensional map of the location of tissue likely to belong to each of the different types of wound tissue.

20 . The method of claim 1 , further comprising the processor determining the width of the wound based on a dimension of the location(s) of tissue identified as likely to belong to one or more of the different types of wound tissue in at least one of the one or more images.

21 . The method of claim 20 , wherein the one or more of the different types of wound tissue include neoepidermis, clot and granular tissue.