American journal of critical care : an official publication, American Association of Critical-Care Nurses
39217110
BACKGROUND: Hospital-acquired pressure injuries (HAPIs) have a major impact on patient outcomes in intensive care units (ICUs). Effective prevention relies on early and accurate risk assessment. Traditional risk-assessment tools, such as the Braden S...
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...
Technology and health care : official journal of the European Society for Engineering and Medicine
40066836
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...
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 reports the first steps toward establishing a computer vision system to help caregivers of bedridden patients detect pressure ulcers (PUs) early. While many previous studies have focused on using convolutional neural networks (CNNs) to ele...
Computer methods and programs in biomedicine
39922124
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...
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...
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...
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...
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...