[Deep Learning-Based Identification of Common Complication Features of Surgical Incisions].

Objective: In recent years, due to the development of accelerated recovery after surgery and day surgery in the field of surgery, the average length-of-stay of patients has been shortened and patients stay at home for post-surgical recovery and healing of the surgical incisions. In order to identify, in a timely manner, the problems that may appear at the incision site and help patients prevent or reduce the anxiety they may experience after discharge, we used deep learning method in this study to classify the features of common complications of surgical incisions, hoping to realize patient-directed early identification of complications common to surgical incisions. Methods: A total of 1 224 postoperative photographs of patients' surgical incisions were taken and collected at a tertiary-care hospital between June 2021 and March 2022. The photographs were collated and categorized according to different features of complications of the surgical incisions. Then, the photographs were divided into training, validation, and test sets at the ratio of 8∶1∶1 and 4 types of convolutional neural networks were applied in the training and testing of the models. Results: Through the training of multiple convolutional neural networks and the testing of the model performance on the basis of a test set of 300 surgical incision images, the average accuracy of the four ResNet classification network models, SE-ResNet101, ResNet50, ResNet101, and SE-ResNet50, for surgical incision classification was 0.941, 0.903, 0.896, and 0.918, respectively, the precision was 0.939, 0.898, 0.868, and 0.903, respectively, and the recall rate was 0.930, 0.880, 0.850, and 0.894, respectively, with the SE-Resnet101 network model showing the highest average accuracy of 0.941 for incision feature classification. Conclusion: Through the combined use of deep learning technology and images of surgical incisions, problematic features of surgical incisions can be effectively identified by examining surgical incision images. It is expected that patients will eventually be able to perform self-examination of surgical incisions on smart terminals.

[Postoperative Sleep Disturbance in Patients Undergoing Arthroplasty: Risk Prediction Modeling Based on Logistic Regression].

Objective: To construct a risk predictive model for postoperative sleep disturbance (PSD) in patients undergoing arthroplasty by using logistic regression. Methods: We retrospectively collected the data of 4286 patients who underwent joint replacement surgeries at a tertiary-care hospital in Chengdu, China between January 1, 2017 and September 30, 2021. With 3001 cases in the training set and 1285 cases in the test set, we constructed the model by using a logistic regression algorithm to screen for predictors in Matlab, displaying the predicted risks of postoperative sleep disturbance with nomographs. The performance of the model was assessed by the area under the curve ( AUC) of the receiver operating characteristic curve, accuracy, precision, recall, F1 value, and calibration curve. Results: A total of 9 predictors, including post-admission preoperative sleep disturbance, ward type, body mass index, smoking status, range of diseases, joint mobility (flexion), joint mobility (extension), preoperative last hemoglobin, and type of surgery, were eventually included in the study for predictive modeling . The performance assessment findings of the predictive model were as follows, AUC value, 0.708 (95% confidence interval: 0.677-0.740), accuracy, 75.20%, precision, 65.80%, recall, 43.70%, and F1 value, 0.525. The calibration curve showed good agreement between the predicted probabilities and the actual data. Conclusion: The model constructed in the study has good predictive efficacy and the nomographs are simple and easy to use. With this model, health workers can make preoperative prediction of the risk of PSD in arthroplasty patients based on the predictors, which facilitates early prevention and reduces the risk of postoperative sleep disturbance in patients.