AIMC Topic: Pressure Ulcer
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Development of an explainable machine learning model for predicting device-related pressure injuries in clinical settings.
BMC medical informatics and decision making
Jul 9, 2025
BACKGROUND: Device-related pressure injury (DRPI) is a prevalent and severe problem for patients using medical devices. Timely identification of patients at high risk of DRPI is crucial for healthcare providers to make informed decisions and prevent ...
Preventing pressure ulcers by increasing pressure: An unorthodox alternating-pressure mattress.
Science robotics
Jun 18, 2025
Despite the devastating effects of pressure ulcers (PUs), little is understood about how they can be prevented using alternating-pressure (AP) mattresses. Such mattresses typically aim to minimize the pressures imparted while alternating between diff...
Controlled Pilot Intervention Study on the Effects of an AI-Based Application to Support Incontinence-Associated Dermatitis and Pressure Injury Assessment, Nursing Care and Documentation: Study Protocol.
Research in nursing & health
Apr 16, 2025
Artificial Intelligence (AI)-based applications have significant potential to differentiate between pressure injuries (PI) and incontinence-associated dermatitis (IAD), common challenges in nursing practice. Within the KIADEKU overall project, we are...
Convolutional Neural Network Models for Visual Classification of Pressure Ulcer Stages: Cross-Sectional Study.
JMIR medical informatics
Mar 25, 2025
BACKGROUND: Pressure injuries (PIs) pose a negative health impact and a substantial economic burden on patients and society. Accurate staging is crucial for treating PIs. Owing to the diversity in the clinical manifestations of PIs and the lack of ob...
Explainable SHAP-XGBoost models for pressure injuries among patients requiring with mechanical ventilation in intensive care unit.
Scientific reports
Mar 22, 2025
pressure injuries are significant concern for ICU patients on mechanical ventilation. Early prediction is crucial for enhancing patient outcomes and reducing healthcare costs. This study aims to develop a predictive model using machine learning techn...
Evaluation methods of pressure injury stages: A systematic review and meta-analysis.
Journal of tissue viability
Mar 21, 2025
BACKGROUND: Pressure injury is prevalent in clinical settings and demands precise staging for optimal care. Subjectivity and imprecision in traditional visual assessments have sparked the creation of advanced technology-based evaluation tools.
Machine learning based finite element analysis for personalized prediction of pressure injury risk in patients with spinal cord injury.
Computer methods and programs in biomedicine
Feb 7, 2025
BACKGROUND AND OBJECTIVE: Patients with spinal cord injury (SCI), are prone to pressure injury (PI) in the soft tissues of buttocks. Early prediction of PI holds the potential to reduce the occurrence and progression of PI. This study proposes a mach...
Development and application of an intelligent pressure injury assessment system using AI image recognition.
Technology and health care : official journal of the European Society for Engineering and Medicine
Dec 9, 2024
BackgroundPressure injuries are a significant concern in clinical settings, requiring accurate assessment to prevent complications. Traditional assessment methods are often subjective and time-consuming.ObjectiveThis study aimed to develop and evalua...
A novel technique for rapid determination of pressure injury stages using intelligent machine vision.
Geriatric nursing (New York, N.Y.)
Nov 14, 2024
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...
A Predictive Model of Pressure Injury in Children Undergoing Living Donor Liver Transplantation Based on Machine Learning Algorithm.
Journal of advanced nursing
Sep 10, 2024
AIMS: The aim of our study was to formulate and validate a prediction model using machine learning algorithms to forecast the risk of pressure injuries (PIs) in children undergoing living donor liver transplantation (LDLT).
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