We have developed an efficient skin tumour classifier using a DCNN trained on a relatively small dataset. The DCNN classified images of skin tumours more accurately than board-certified dermatologists. Collectively, the current system may have capabilities for screening purposes in general medical practice, particularly because it requires only a single clinical image for classification.

Background The clinical benefits of hybrid operating rooms are recognized globally. However, appropriate conditions for entry into such rooms must be urgently established, because they exclusively benefit few patients under severe trauma while requiring a significant amount of resources. This paper presents an algorithm to triage trauma patients into a hybrid operating room. Methods This retrospective observational study was conducted using the Japan Trauma Data Bank database comprising information collected between January 2004 and December 2018. A machine-learning-based triage algorithm is developed using the baseline demographics, injury mechanisms, and vital signs obtained from the database. The analysis dataset comprised information regarding 117,771 trauma patients with abbreviated injury scale (AIS) > 3. The performance of the proposed model was compared against those of other statistical models (logistic regression and classification and regression tree [CART] models) while considering the status quo entry condition (systolic blood pressure < 90 mmHg). Results The proposed trauma hybrid-suite entry algorithm (THETA) outperforms other algorithms (PR-AUC: THETA [0.59], logistic regression model [0.22], and CART [0.20]; AUROC: THETA [0.93], logistic regression model [0.88], and CART [0.86]), thereby facilitating appropriate triaging of patients who would potentially benefit from resuscitation performed using angiographic percutaneous techniques and operative resuscitation suites. Conclusions An accurate machine-learning-based algorithm is developed to triage patient entry into hybrid operating rooms via a web application, thereby enabling emergency doctors to utilize limited medical resources more efficiently.