Design of Deep Learning-Based Pressure Injury Stage Classification Device - Priharti, Wahmisari; Mukhtar, Husneni; Prastha Giriwara, I Made; Andi Majesta, I Made; Bena Bimantara, Wayan Abin (Universitas Syiah Kuala, 2025)
Pressure injury or pressure ulcers could occur due to continuous pressure on the bony prominence of the skin and tissue. Geriatric patients, especially those with limited mobility and several comorbidities, are more susceptible to pressure injury. Stage classification of pressure injury is currently carried out qualitatively and requires clear communication with the patient. This is often not possible in elderly patients due to lack perception of pain causing late detection of pressure injury until they have reached a severe level and can endanger the patient's life. This study proposes a non-contact device in the form of a camera integrated with a convolutional neural network (CNN) model with MobileNet architecture to classify the level of pressure injury. Testing showed a classification accuracy of 83.3% with an average classification duration of 2.24 s. This aiding device is considered to have great potential to improve faster and more accurate pressure injury assessment in clinical settings.