AIMC Topic: Pressure Ulcer
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Classification of pressure ulcer tissues with 3D convolutional neural network.
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 ...
Tissue classification and segmentation of pressure injuries using convolutional neural networks.
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...
Fuzzy spectral clustering for automated delineation of chronic wound region using digital images.
Computers in biology and medicine
Apr 23, 2017
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...
Automated Pressure Injury Assessment and Documentation Generation Using Vision-Language Model.
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...
Interpretable Machine Learning Prediction Model for Predicting Mortality Risk of ICU Patients With Pressure Ulcers Based on the Braden Scale: A Clinical Study Based on MIMIC-IV.
Journal of clinical nursing
Aug 1, 2025
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.
Controlled Intervention Study on Effects of an AI-Based App to Support Wound Care: First Results.
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-...
Real-World Deployment of a ML Pipeline for Pressure Wounds Prediction.
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 ...
Utilizing Image Processing Techniques for Wound Management and Evaluation in Clinical Practice: Establishing the Feasibility of Implementing Artificial Intelligence in Routine Wound Care.
Advances in skin & wound care
May 14, 2025
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.
Investigating perioperative pressure injuries and factors influencing them with imbalanced samples using a Synthetic Minority Over-sampling Technique.
Bioscience trends
May 9, 2025
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...
Optimizing Chart Review Efficiency in Pressure Injury Evaluation Using ChatGPT.
Annals of plastic surgery
Apr 1, 2025
INTRODUCTION: Wound care is an essential discipline in plastic surgery, especially as the prevalence of chronic wounds, such as pressure injuries, is increasing. The escalating volume of patient data and the numerous variables influencing wound outco...
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