ArticleScientific reports2023
Automatic segmentation and measurement of pressure injuries using deep learning models and a LiDAR camera.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07619430 (AI-Based Wound Monitoring), which is not on this map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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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.
AI-Based Wound Monitoring: Automated Wound Progression Assessment Via Marker-Free Image Sequence
Who cites it
10 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
- Validation of a You Only Look Once-LabelMe Hybrid AI Model for Automated Assessment of Pressure Injury Size and Pocket.International wound journal · 2026Article
- Construction and validation of a multi-function artificial intelligence-assisted system for pressure injury recognition.Frontiers in physiology · 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
- Comparison of mask R-CNN and YOLOv8-seg for improved monitoring of the PCB surface during laser cleaning.Scientific reports · 2025Article
- Comparison of 3D and 2D area measurement of acute burn wounds with LiDAR technique and deep learning model.Frontiers in artificial intelligence · 2025Article
- Mobile Apps for Wound Assessment and Monitoring: Limitations, Advancements and Opportunities.Journal of medical systems · 2024Review
- YOLO-Based Deep Learning Model for Pressure Ulcer Detection and Classification.Healthcare (Basel, Switzerland) · 2023Article
- Principles, applications, and future of artificial intelligence in dermatology.Frontiers in medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 deep learning models and a light detection and ranging (LiDAR) camera. We selected the finest photos of patients with pressure injuries, 528 in total, at National Taiwan University Hospital from 2016 to 2020. The margins of the pressure injuries were labeled by three board-certified plastic surgeons. The labeled photos were trained by Mask R-CNN and U-Net for segmentation. After the segmentation model was constructed, we made an automatic wound area measurement via a LiDAR camera. We conducted a prospective clinical study to test the accuracy of this system. For automatic wound segmentation, the performance of the U-Net (Dice coefficient (DC): 0.8448) was better than Mask R-CNN (DC: 0.5006) in the external validation. In the prospective clinical study, we incorporated the U-Net in our automatic wound area measurement system and got 26.2% mean relative error compared with the traditional manual method. Our segmentation model, U-Net, and area measurement system achieved acceptable accuracy, making them applicable in clinical circumstances.
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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.