Automated assessment of wound tissue

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.