ArticleDigital health
Preparing infection detection technology for hospital at home after lower limb external fixation.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- YOLOv10-based detection of melanocytic nevi: reverse exclusion optimization for melanoma screening.Frontiers in artificial intelligence · 2025Article
- Can pin-site inflammation be detected with thermographic imaging? A cross-sectional study from the USA and Denmark of patients treated with external fixators.Acta orthopaedica · 2024Article
- An assessment of the inter-rater and intra-rater reliability of the modified Gordon pin infection classification system.Digital healthArticle
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Patients with severe bone fractures and complex bone deformities are treated by orthopedic surgeons with external fixation for several months. During this long treatment period, there is a high risk of inflammation and infection at the superficial skin area (pin site). This can develop into a devastating, sometimes fatal, and always costly condition of deep bone infection. Objective: For pin site infection surveillance, thermography technology could be the solution to build an objective and continuous home-based remote monitoring tool to avoid frequent nursing care and hospital visits. However, future studies of infection monitoring require a preliminary step to automate the process of locating and detecting the pin sites in thermal images reliably for temperature measurement, and this step is the aim of this study. Methods: This study presents an automatic approach for identifying and annotating pin sites on visible images using bounding boxes and transferring them to the corresponding thermal images for temperature measurement. The pin site is detected by applying deep learning-based object detection architecture YOLOv5 with a novel loss evaluation and regression method, control distance intersection over union. Furthermore, we address detecting pin sites in a practical environment (home setting) accurately through transfer learning. Results and conclusion: The proposed model offers the pin site detection in 1.8 ms with a high precision of 0.98 and enables temperature information extraction. Our work for automatic pin site annotation on thermography paves the way for future research on infection assessment on thermography.
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Registered trials
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