ArticleHealthcare (Basel, Switzerland)2023
YOLO-Based Deep Learning Model for Pressure Ulcer Detection and Classification.
Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Methods for Diagnostic and Decision-Making Assistance in Chronic Wounds: A Systematic Review.Journal of medical systems · 2025Pooled it
- EPI-Sense: an integrated hemodynamic sensing and analysis system for early pressure injury warning.Journal of translational medicine · 2026Article
- Unimodal and Multimodal Deep Learning for Pressure Injury Identification: Scoping Review.JMIR medical informatics · 2026Article
- Validation of a You Only Look Once-LabelMe Hybrid AI Model for Automated Assessment of Pressure Injury Size and Pocket.International wound journal · 2026Article
- Impact of Imaging Protocols on Thermal Detection of Pressure Injuries: Threshold versus Deep Learning Across Skin Tones.medRxiv : the preprint server for health sciences · 2026Article
- [An Improved Faster R-CNN Method for Wound Detection].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Article
- Novel two-stage deep learning framework for automated pressure injury classification.BMJ health & care informatics · 2026Article
- A Systematic Literature Review of You Only Look Once Architectures (v1-v12) in Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Review
- 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
- PEYOLO: a wrist fracture detection network based on multi-level receptive field feature extraction and cross-scale fusion.Frontiers in medicine · 2026Article
- Construction and validation of a multi-function artificial intelligence-assisted system for pressure injury recognition.Frontiers in physiology · 2026Article
- Automatic measuring of coronary atherosclerosis from medicolegal autopsy photographs based on deep learning techniques.Forensic science, medicine, and pathology · 2025Article
- Improving Clinical Generalization of Pressure Ulcer Stage Classification Through Saliency-Guided Data Augmentation.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning for digital pathology: A critical overview of methodological framework.Journal of pathology informatics · 2025Review
- Impact of Imaging Protocols on Convolutional Neural Network-Based Pressure Injury Detection.Research square · 2025Article
- Major Common Hallmarks and Potential Epigenetic Drivers of Wound Chronicity and Recurrence: Hypothesis and Reflections.International journal of molecular sciences · 2025Review
- GESC-YOLO: Improved Lightweight Printed Circuit Board Defect Detection Based Algorithm.Sensors (Basel, Switzerland) · 2025Article
- Article
- Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Pressure ulcers are significant healthcare concerns affecting millions of people worldwide, particularly those with limited mobility. Early detection and classification of pressure ulcers are crucial in preventing their progression and reducing associated morbidity and mortality. In this work, we present a novel approach that uses YOLOv5, an advanced and robust object detection model, to detect and classify pressure ulcers into four stages and non-pressure ulcers. We also utilize data augmentation techniques to expand our dataset and strengthen the resilience of our model. Our approach shows promising results, achieving an overall mean average precision of 76.9% and class-specific mAP50 values ranging from 66% to 99.5%. Compared to previous studies that primarily utilize CNN-based algorithms, our approach provides a more efficient and accurate solution for the detection and classification of pressure ulcers. The successful implementation of our approach has the potential to improve the early detection and treatment of pressure ulcers, resulting in better patient outcomes and reduced healthcare costs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.