Augmented Decision-Making in wound Care: Evaluating the clinical utility of a Deep-Learning model for pressure injury staging

Background: Precise categorization of pressure injury (PI) stages is critical in determining the appropriate treatment for wound care. However, the expertise necessary for PI staging is frequently unavailable in residential care settings. Objective: This study aimed to develop a convolutional neural network (CNN) model for classifying PIs and investigate whether its implementation can allow physicians to make better decisions for PI staging. Methods: Using 3,098 clinical images (2,614 and 484 from internal and external datasets, respectively), a CNN was trained and validated to classify PIs and other related dermatoses. A two-part survey was conducted with 24 dermatology residents, ward nurses, and medical students to determine whether the implementation of the CNN improved initial PI classification decisions. Results: The top-1 accuracy of the model was 0.793 (95% confidence interval [CI], 0.778-0.808) and 0.717 (95% CI, 0.676-0.758) over the internal and external testing sets, respectively. The accuracy of PI staging among participants was 0.501 (95% CI, 0.487-0.515) in Part I, improving by 17.1% to 0.672 (95% CI, 0.660-0.684) in Part II. Furthermore, the concordance between participants increased significantly with the use of the CNN model, with Fleiss' kappa of 0.414 (95% CI, 0.410-0.417) and 0.641 (95% CI, 0.638-0.644) in Parts I and II, respectively. Conclusions: The proposed CNN model can help classify PIs and relevant dermatoses. In addition, augmented decision-making can improve consultation accuracy while ensuring concordance between the clinical decisions made by a diverse group of health professionals.