# A Unified Framework for Automatic Detection of Wound Infection with Artificial Intelligence
## 4 Discussion  
We have designed a novel wound image analysis system for surgical patients undergoing elective procedures, including laparotomy, minimal invasive surgery, and hernia. This artificial-intelligence-implemented algorithm recognizes not only wound borders, but also potential features of the wound infection, and calculates the length of the wound by comparison with a reference color card. The reason why we chose CNN as a model is that it has become dominant in various computer vision tasks with pre-training models and good performances in feature extractors (inception model or ResNets). With the pre-training model, you can extract useful attributes with its trained weights and tune your CNN model by feeding data on each level for the new task. This study demonstrates the feasibility of using expert-based machine learning methods to predict wound infection, with acceptable results. To the best of our knowledge, this is one of the innovative systems focusing on the automatic detection of wound area and diagnosis of wound infection. Traditionally, patients discharged from surgery receive little care, until they report for a routine clinic follow-up [5]. Self-care is a unique problem for surgical patients rather than medical patients during this period. Among surgical patients under the early recovery and discharge program, wound complications are more likely to occur after discharge than before discharge [20]. Without appropriate management and early detection of wound complications, patients may be particularly vulnerable after discharge, given their lack of clinical experience, their increased medical costs, and the likelihood of hospital readmission. In summary, mHealth is a scalable and flexible platform supporting the practice of medical and perioperative care with the provision of mobile devices [8; 21; 22; 23], which improve the quality of perioperative care and have proliferated [24; 25]. Although some academic centers have developed the mHealth system for postoperative wound care with positive results, it places an additional strain and workload on their provider teams, even if they are enthusiastic about it [5; 7; 26; 27; 25]. The advantage of our method is that it provides automatic detection of wound infection, which may alleviate the providers' workload to a certain degree. Figure 5: Receiver operating characteristic curves of the detection of wound infection between the four models (SVM: blue; RF: yellow; GBC: green; our model: red). The receiver operating characteristic curve is a probability curve, and the area under the receiver operating characteristic curve (AUC) characterizes the degree of separability. One model with a higher AUC score is better at detecting the aimed outcome. AUC values in these four models range from 0.444 to 0.833; our model achieves the best performance. In this study, we used expert-based annotation of wound features to improve the performance because of the limited number of wound pictures. Traditionally, inpatient wounds are assessed periodically, but photographs are not taken or documented in the medical records unless complications occur. Furthermore, almost no data on wounds after discharge are available for analysis. It is necessary for surgical professionals to systemically collect perioperative wound photographs. Because of the shortage of available wound photographs, our initial task was to construct a cloud-based server to store wound data obtained by mobile phones. Next, we performed expert-based annotation of wound features to improve the prediction outcomes of deep learning. With this infrastructure, we can continue to collect wound data, which may further improve the accuracy of detection of wound complications [28; 29]. One recent report questioned the accuracy of detection of wound complications by using wound photographs and clinical information from surgical professionals [25].