Image Processing and Machine Leaning for Prediction of Wound Healing
Can one tell if a wound is healing? As basic as this question is, it is not easily answered using current practice for the treatment of diabetes-related foot ulcers (DFU). It is well known that DFU surface area changes at 4 weeks are highly predictive of subsequent wound closure.(Sheehan, Jones et al. 2003, Robson, Cooper et al. 2006, Steed, Attinger et al. 2006, Snyder, Cardinal et al. 2010) Simply providing this information to wound care providers improves wound healing outcomes.(Kurd, Hoffstad et al. 2009) Additionally, the quality and quantity of the tissue in the wound bed also offers important prognostic information.(Sherman 2003, Valenzuela-Silva, Tuero- Iglesias et al. 2013) Unfortunately outside of the research setting, accurate measurement of wound surface area changes and quantification of the wound base are out of reach in the busy wound practice setting. Currently, clinicians estimate wound size by estimating wound width and length using a scalpel after wound treatment. There is a great deal of measurement error in assuming all wounds are rectangles. Rarely do clinicians take the next step in estimating wound surface area and changes over 2 and 4 weeks; crucial time points for wound healing assessment.(Sheehan, Jones et al. 2006)
Our long-term goal is to implement cognitive computing advancements in image processing and machine learning into a busy wound care setting to fundamentally change the quality of wound care. We have developed wound segmentation and deep neural network models that can accurately predict infection and wound healing.(Wang, Yan et al. 2015) The image segmentation methods provide accurate “hands-free” measurement of wound surface area. Our objective here is to refine our algorithms and test the suitability of image analysis as a biomarker for healing or infection. We will do this by performing image analyses of DFU treated by total contact cast (TCC) and by comparing the image features to those not treated by TCC. Our working hypothesis is there will be unique features from image analysis that can accurately predict infection or healing beyond surface area changes over time, known clinical predictors (Brownrigg, Hinchliffe et al. 2016) or candidate biomarkers. The rationale for the proposed research is that, image analysis features along with surface area changes over time will accurately identify non-healers at an early stage where more advanced wound care strategies can result in improved patient outcomes.