  Postoperative wound complications are a significant cause of expense for
hospitals, doctors, and patients. Hence, an effective method to diagnose the
onset of wound complications is strongly desired. Algorithmically classifying
wound images is a difficult task due to the variability in the appearance of
wound sites. Convolutional neural networks (CNNs), a subgroup of artificial
neural networks that have shown great promise in analyzing visual imagery, can
be leveraged to categorize surgical wounds. We present a multi-label CNN
ensemble, Deepwound, trained to classify wound images using only image pixels
and corresponding labels as inputs. Our final computational model can
accurately identify the presence of nine labels: drainage, fibrinous exudate,
granulation tissue, surgical site infection, open wound, staples, steri strips,
and sutures. Our model achieves receiver operating curve (ROC) area under curve
(AUC) scores, sensitivity, specificity, and F1 scores superior to prior work in
this area. Smartphones provide a means to deliver accessible wound care due to
their increasing ubiquity. Paired with deep neural networks, they offer the
capability to provide clinical insight to assist surgeons during postoperative
care. We also present a mobile application frontend to Deepwound that assists
patients in tracking their wound and surgical recovery from the comfort of
their home.
