An Image Classification Approach to Pre-Determine Extent of Development of Post-Operative Necrosis in Skin Flaps.

Purpose - Clinicians and care workers currently cannot determine the final extent of necrosis once it begins. If the area is relatively small, it may heal like a normal wound, whereas re-operation may be the only option if it spreads. This paper represents work toward a predictive algorithm using full-thickness random rat skin flap photographs to determine whether the tissue will develop irretrievable necrosis.Methods - Using post-surgery images taken over a series of days of ischemic flaps, features were extracted, selected, and input into a classification algorithm to see if it could provide information on the future condition of the flaps. We split our data into two groups: flaps that underwent normal healing and slow healing. When consulting with a specialist, it was observed that the resulting dermal damage was not severe when a flap had ≤ 40% necrosis over its length on the final day. Three classifiers were implemented: K-Nearest Neighbor (KNN), Random Forest, and Support Vector Machine (SVM).Results - The trained KNN was able to correctly determine whether a flap developed a necrotic area larger or less than 40% of its length with an accuracy of 91% using only images one day post-surgery. Under leave-one-out cross-validation, the Random Forest and SVM achieved accuracies of 82% and 79.5%, respectively, using images spanning ten days post-surgery.Conclusion - We have shown that a classifier can accurately determine whether ischemic skin flaps will develop severe necrotic tissue.Clinical Relevance- An algorithm that assesses early timepoint images and predicts necrosis spread is not only a vital tool for patients and clinicians but is also an extremely important tool to accelerate research into necrosis reduction strategies that ultimately may find application for more life-threatening necrosis-related conditions.