A recent study published in the Journal of Healthcare Engineering demonstrated the potential of a deep learning algorithm, Mask-R-CNN, in detecting and accurately classifying pressure injuries. Pressure injuries, a common issue in healthcare, can cause significant harm to patients and cost the healthcare system billions of dollars each year.
The study utilized a unique pressure injury image dataset with high-quality images and ground truth data to train the deep learning algorithm. Results showed that the algorithm achieved 92.6% overall accuracy in pressure injury detection and 93.0% accuracy in segmentation, outperforming many nonexpert clinicians in staging pressure injuries.
This tool has the potential to aid both hospital staff and home caregivers in identifying and treating pressure injuries early, ultimately improving patient outcomes and reducing healthcare costs. While further work is needed to eliminate error and improve the system’s accuracy for small wounds and varied anatomical locations, this research provides promising evidence for the use of deep learning in pressure injury management.
