Pressure ulcers, by definition, are caused by external forces on the tissues, often in the regions of bony prominences. Wheelchair users are at risk to develop sitting-acquired pressure ulcers, which occur in the regions of the ischial tuberosities, ...
Medical & biological engineering & computing
Jun 15, 2018
A 3D convolution neural network (CNN) of deep learning architecture is supplied with essential visual features to accurately classify and segment granulation, necrotic eschar, and slough tissues in pressure ulcer color images. After finding a region ...
Computer methods and programs in biomedicine
Mar 3, 2018
BACKGROUND AND OBJECTIVES: This paper presents a new approach for automatic tissue classification in pressure injuries. These wounds are localized skin damages which need frequent diagnosis and treatment. Therefore, a reliable and accurate systems fo...
Chronic wound is an abnormal disease condition of localized injury to the skin and its underlying tissues having physiological impaired healing response. Assessment and management of such wound is a significant burden on the healthcare system. Curren...
Studies in health technology and informatics
Aug 7, 2025
Pressure injury assessment and documentation are crucial but time-consuming tasks in healthcare settings, with current inter-rater reliability among assessors only reaching 60-70%. This study presents an automated approach using the Florence-2 vision...
AIMS: This study was to create an interpretable machine learning model to predict the risk of mortality within 90 days for ICU patients suffering from pressure ulcers.
Studies in health technology and informatics
May 15, 2025
The KIADEKU project combines datascience and the clinical expertise of wound experts to develop and evaluate an AI-application for incontinence-associated-dermatitis (IAD) and pressure ulcer (PU) wound care. The evaluation study is a controlled, non-...
Studies in health technology and informatics
May 15, 2025
Hospital-acquired pressure injuries (HAPIs) are common complications that impact patient outcomes and strain healthcare resources. The Braden Scale is the standard tool for assessing HAPI risk, but it has limitations, including a high false-positive ...
OBJECTIVE: To develop a generalizable and accurate method for automatically analyzing wound images captured in clinical practice and extracting key wound characteristics such as surface area measurement.
This study investigates the use of machine learning (ML) models combined with a Synthetic Minority Over-sampling Technique (SMOTE) and its variants to predict perioperative pressure injuries (PIs) in an imbalanced dataset. PIs are a significant healt...
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