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Pressure Ulcer

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Using Artificial Intelligence for the Early Detection of Micro-Progression of Pressure Injuries in Hospitalized Patients: A Preliminary Nursing Perspective Evaluation.

Studies in health technology and informatics
This study established a predictive model for the early detection of micro-progression of pressure injuries (PIs) from the perspective of nurses. An easy and programing-free artificial intelligence modeling tool with professional evaluation capabilit...

Simultaneous Segmentation and Classification of Pressure Injury Image Data Using Mask-R-CNN.

Computational and mathematical methods in medicine
BACKGROUND: Pressure injuries (PIs) impose a substantial burden on patients, caregivers, and healthcare systems, affecting an estimated 3 million Americans and costing nearly $18 billion annually. Accurate pressure injury staging remains clinically c...

Automatic segmentation and measurement of pressure injuries using deep learning models and a LiDAR camera.

Scientific reports
Pressure injuries are a common problem resulting in poor prognosis, long-term hospitalization, and increased medical costs in an aging society. This study developed a method to do automatic segmentation and area measurement of pressure injuries using...

In-Bed Posture Classification Using Deep Neural Network.

Sensors (Basel, Switzerland)
In-bed posture monitoring has become a prevalent area of research to help minimize the risk of pressure sore development and to increase sleep quality. This paper proposed 2D and 3D Convolutional Neural Networks, which are trained on images and video...

Visual classification of pressure injury stages for nurses: A deep learning model applying modern convolutional neural networks.

Journal of advanced nursing
AIMS: To develop a deep learning model for pressure injury stages classification based on real-world photographs and compare its performance with that of clinical nurses to seek the opportunity of its application in clinical settings.

Leveraging artificial intelligence and decision support systems in hospital-acquired pressure injuries prediction: A comprehensive review.

Artificial intelligence in medicine
BACKGROUND: Hospital-acquired pressure injuries (HAPIs) constitute a significant challenge harming thousands of people worldwide yearly. While various tools and methods are used to identify pressure injuries, artificial intelligence (AI) and decision...

Risk predictions of hospital-acquired pressure injury in the intensive care unit based on a machine learning algorithm.

International wound journal
Pressure injury (PI), or local damage to soft tissues and skin caused by prolonged pressure, remains controversial in the medical world. Patients in intensive care units (ICUs) were frequently reported to suffer PIs, with a heavy burden on their life...

Augmented Decision-Making in wound Care: Evaluating the clinical utility of a Deep-Learning model for pressure injury staging.

International journal of medical informatics
BACKGROUND: Precise categorization of pressure injury (PI) stages is critical in determining the appropriate treatment for wound care. However, the expertise necessary for PI staging is frequently unavailable in residential care settings.

Enhancing Pressure Injury Surveillance Using Natural Language Processing.

Journal of patient safety
OBJECTIVE: This study assessed the feasibility of nursing handoff notes to identify underreported hospital-acquired pressure injury (HAPI) events.

Evaluating Natural Language Processing Packages for Predicting Hospital-Acquired Pressure Injuries From Clinical Notes.

Computers, informatics, nursing : CIN
Incidence of hospital-acquired pressure injury, a key indicator of nursing quality, is directly proportional to adverse outcomes, increased hospital stays, and economic burdens on patients, caregivers, and society. Thus, predicting hospital-acquired ...