ArticleFrontiers in medical technology2022
An artificial intelligence-enabled smartphone app for real-time pressure injury assessment.
Article in Frontiers in medical technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 49 citations in OpenAlex.
- Artificial Intelligence in Burn and Wound Care: Image Analysis, Prediction, and Clinical Integration.Journal of burn care & research : official publication of the American Burn Association · 2026Review
- Artificial Intelligence-Supported Virtual Nursing at Home: A Systematic Review.Florence Nightingale journal of nursing · 2026Article
- Effects of a transformer-based AI-based application to support incontinence-associated dermatitis and pressure injury assessment, nursing care and documentation: Controlled pilot intervention study.International journal of nursing studies advances · 2026Article
- 3D Measurement of Chronic Wounds in Routine Care: A Review with Practical Guidance for Smartphone Photogrammetry.Annals of biomedical engineering · 2026Review
- Novel two-stage deep learning framework for automated pressure injury classification.BMJ health & care informatics · 2026Article
- Comparing YOLO and U-net deep learning algorithms in chronic wound image segmentation.BMC medical imaging · 2026Article
- Clinical Validation of Object Detection Models for AI-Based Pressure Injury Stage Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Development and preliminary clinical validation of a mobile health application for pressure injury staging in ICU patients.Frontiers in medicine · 2026Article
- Investigation of Pressure Injuries With Visual ChatGPT Integration: A Descriptive Cross-Sectional Study.Journal of advanced nursing · 2026Article
- Digital and Smart Technologies for Early Pressure Injury Detection and Prevention in Stroke and Mobility-Impaired Patients: A Scoping Review.Nursing research and practice · 2026Review
- Improving Clinical Generalization of Pressure Ulcer Stage Classification Through Saliency-Guided Data Augmentation.Diagnostics (Basel, Switzerland) · 2025Article
- Improving pressure injury assessment skills of nursing students' using in-situ simulation and moulage: a randomized controlled trial.BMC nursing · 2025Article
- 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 · 2025Article
- Diagnostic Accuracy of Microsoft's Copilot Artificial Intelligence in Chronic Wound Assessment: A Comparative Study.Plastic and reconstructive surgery. Global open · 2025Article
- Article
- Convolutional Neural Network Models for Visual Classification of Pressure Ulcer Stages: Cross-Sectional Study.JMIR medical informatics · 2025Article
- Design and development of an mHealth application for pressure ulcer care and caregiver support.Frontiers in digital health · 2025Article
- Why do pressure injuries still occur? A multicenter qualitative study of nurses and caregivers.JAAD international · 2024Article
- Hotspots and Global Trends of Nursing Research on Mobile Applications in Nursing From 2012 to 2023: A Bibliometric Analysis.Computers, informatics, nursing : CIN · 2024Article
- Mobile Apps for Wound Assessment and Monitoring: Limitations, Advancements and Opportunities.Journal of medical systems · 2024Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The management of chronic wounds in the elderly such as pressure injury (also known as bedsore or pressure ulcer) is increasingly important in an ageing population. Accurate classification of the stage of pressure injury is important for wound care planning. Nonetheless, the expertise required for staging is often not available in a residential care home setting. Artificial-intelligence (AI)-based computer vision techniques have opened up opportunities to harness the inbuilt camera in modern smartphones to support pressure injury staging by nursing home carers. In this paper, we summarise the recent development of smartphone or tablet-based applications for wound assessment. Furthermore, we present a new smartphone application (app) to perform real-time detection and staging classification of pressure injury wounds using a deep learning-based object detection system, YOLOv4. Based on our validation set of 144 photos, our app obtained an overall prediction accuracy of 63.2%. The per-class prediction specificity is generally high (85.1%-100%), but have variable sensitivity: 73.3% (stage 1 vs. others), 37% (stage 2 vs. others), 76.7 (stage 3 vs. others), 70% (stage 4 vs. others), and 55.6% (unstageable vs. others). Using another independent test set, 8 out of 10 images were predicted correctly by the YOLOv4 model. When deployed in a real-life setting with two different ambient brightness levels with three different Android phone models, the prediction accuracy of the 10 test images ranges from 80 to 90%, which highlight the importance of evaluation of mobile health (mHealth) application in a simulated real-life setting. This study details the development and evaluation process and demonstrates the feasibility of applying such a real-time staging app in wound care management.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.