WITA &amp;#x2014; Application for wound analysis and management

Filko, Damir; Antonić, Davor; Huljev, Dubravko

doi:10.1109/health.2010.5556533

Cited by 8 publications

(2 citation statements)

References 4 publications

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“…But they consume considerable computational resources with poor accuracy and speed, and there is a doubt about their robustness to the diversity of images. 11 Furthermore, those works that utilize rule-based algorithms for desired area extraction like statistical pattern recognition algorithm 12 require rules or features defined manually. 13 In the past decade, convolution neural networks (CNNs) have been introduced for object detection with possible superior performance in both accuracy and computation time, which greatly enhances the development of medical image assessment.…”

Section: Literature Reviewmentioning

confidence: 99%

Preparing infection detection technology for hospital at home after lower limb external fixation

et al. 2022

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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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Section: Literature Reviewmentioning

confidence: 99%

Preparing infection detection technology for hospital at home after lower limb external fixation

et al. 2022

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“…The first is the sufficient use of large datasets to develop high-performing systems [15]- [17]. Second, appropriate segmentation methods to detect wound edges in detail are selected as some algorithms struggle with detecting small or irregularly shaped wounds [14], [18]. Third, implementing preprocessing stages, such as contrast enhancement, sharpening, and smoothing, is necessary to enhance image quality [19].…”

Section: Introductionmentioning

confidence: 99%

Image Processing-Based Application for Determining Wound Types in Forensic Medical Cases

Wahyuni,

Alvita Widya Kustiawan Putri,

Nisa Agustin Pratiwi Pelu

et al. 2024

JNTE

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Wounds result from physical violence that damages the continuity of body tissues and are frequently observed in forensic medicine and medicolegal science. In forensic medicine and medicolegal science, wounds play a significant role in creating a medicolegal examination and report (VeR) for deceased individuals and living victims. However, research findings indicate that the quality of clinical forensic descriptive results in VeR needs to improve in several hospitals in Indonesia. Meanwhile, high-quality VeR results are crucial in determining penalties for perpetrators in court, and poor VeR results can hinder the legal process. The application of information technology in medicine has yielded numerous tools that can assist experts in carrying out their duties. Likewise, clinical forensics, a generally conservative forensic pathology practice, can be enhanced through image-processing techniques and machine learning. Digital technology support for forensic cases has been available previously, such as in forensic photography; however, its application still needs improvement, and further development is required. This study applied a Yolo V4-based machine learning and image processing algorithm to classify and detect types of wounds. This algorithm was chosen for its high speed and accuracy in classification and detection tasks. The research results showed that the learning model's performance, measured in accuracy, precision, recall, and average F1 score, reached 92%. Usability testing showed that the system performed well and could be helpful with minor improvements.

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Instance-Based Segmentation for Boundary Detection of Neuropathic Ulcers Through Mask-RCNN

Gamage

Wijesinghe

Perera

2019

Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions

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WITA — Application for wound analysis and management

Cited by 8 publications

References 4 publications

Preparing infection detection technology for hospital at home after lower limb external fixation

Preparing infection detection technology for hospital at home after lower limb external fixation

Image Processing-Based Application for Determining Wound Types in Forensic Medical Cases

Instance-Based Segmentation for Boundary Detection of Neuropathic Ulcers Through Mask-RCNN

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