A novel technique for rapid determination of pressure injury stages using intelligent machine vision

Abstract

A developed intelligent machine vision system combined with deep-learning algorithms was attempted to determine pressure injury (PI) stages rapidly. A total of 500 images were selected according to the color and texture characteristics of probable PI sites closely related to fie PI stages based on the guidance of PI experts. Each target box of the PI site was labeled by the same researcher for label consistency. Characteristic values of pressure injuries were extracted from segmented images for further model construction. In developing the rapid determination models, five you just look once (YOLO) pattern recognition models (i.e., YOLO8n, YOLO8s, YOLO8m, YOLO8l, and YOLO8x) were constructed, and they were optimized among 100 epochs. Compared with other models, the YOLO8l model showed the best result, with the precision values among pressure injury stage I to V (i.e., PI_I, PI_II, PI_III, PI_IV, and PI_V) of 0.98, 0.97, 0.95, 0.95, and 0.94, respectively. The overall results suggest that this intelligent machine vision system is useful for PI stage determination and perhaps other disease diagnoses closely related to color and texture characteristics.