Abstract
? 2021 The AuthorsBackground: The clinical characterization of the biological status of complex wounds remains a considerable challenge. Digital photography provides a non–invasive means of obtaining wound information and is currently employed to assess wounds qualitatively. Advances in machine learning (ML) image processing provide a means of identifying “hidden” features in pictures. This pilot study trains a convolutional neural network (CNN) to predict gene expression based on digital photographs of wounds in a canine model of volumetric muscle loss (VML). Materials and Methods: Images of volumetric muscle loss injuries and tissue biopsies were obtained in a canine model of VML. A CNN was trained to regress gene expression values as a function of the extracted image segment (color and spatial distribution). Performance of the CNN was assessed in a held-back test set of images using Mean Absolute Percentage Error (MAPE). Results: The CNN was able to predict the gene expression of certain genes based on digital images, with a MAPE ranging from ～10% to ～30%, indicating the presence and identification of distinct, and identifiable patterns in gene expression throughout the wound. Conclusions: These initial results suggest promise for further research regarding this novel use of ML regression on medical images. Specifically, the use of CNNs to determine the mechanistic biological state of a VML wound could aid both the design of future mechanistic interventions and the design of trials to test those therapies. Future work will expand the CNN training and/or test set, with potential expansion to predicting functional gene modules.
NSTL
Elsevier