{"title":"基于深度学习的压力损伤面评估模型的开发。","authors":"Ankang Liu, Hualong Ma, Yanying Zhu, Qinyang Wu, Shihai Xu, Wei Feng, Haobin Liang, Jian Ma, Xinwei Wang, Xuemei Ye, Yanxiong Liu, Chao Wang, Xu Sun, Shijun Xiang, Qiaohong Yang","doi":"10.1111/jocn.17645","DOIUrl":null,"url":null,"abstract":"<p><strong>Aim: </strong>To develop a deep learning-based smart assessment model for pressure injury surface.</p><p><strong>Design: </strong>Exploratory analysis study.</p><p><strong>Methods: </strong>Pressure injury images from four Guangzhou hospitals were labelled and used to train a neural network model. Evaluation metrics included mean intersection over union (MIoU), pixel accuracy (PA), and accuracy. Model performance was tested by comparing wound number, maximum dimensions and area extent.</p><p><strong>Results: </strong>From 1063 images, the model achieved 74% IoU, 88% PA and 83% accuracy for wound bed segmentation. Cohen's kappa coefficient for wound number was 0.810. Correlation coefficients were 0.900 for maximum length (mean difference 0.068 cm), 0.814 for maximum width (mean difference 0.108 cm) and 0.930 for regional extent (mean difference 0.527 cm<sup>2</sup>).</p><p><strong>Conclusion: </strong>The model demonstrated exceptional automated estimation capabilities, potentially serving as a crucial tool for informed decision-making in wound assessment.</p><p><strong>Implications and impact: </strong>This study promotes precision nursing and equitable resource use. The AI-based assessment model serves clinical work by assisting healthcare professionals in decision-making and facilitating wound assessment resource sharing.</p><p><strong>Reporting method: </strong>The STROBE checklist guided study reporting.</p><p><strong>Patient or public contribution: </strong>Patients provided image resources for model training.</p>","PeriodicalId":50236,"journal":{"name":"Journal of Clinical Nursing","volume":" ","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of a Deep Learning-Based Model for Pressure Injury Surface Assessment.\",\"authors\":\"Ankang Liu, Hualong Ma, Yanying Zhu, Qinyang Wu, Shihai Xu, Wei Feng, Haobin Liang, Jian Ma, Xinwei Wang, Xuemei Ye, Yanxiong Liu, Chao Wang, Xu Sun, Shijun Xiang, Qiaohong Yang\",\"doi\":\"10.1111/jocn.17645\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aim: </strong>To develop a deep learning-based smart assessment model for pressure injury surface.</p><p><strong>Design: </strong>Exploratory analysis study.</p><p><strong>Methods: </strong>Pressure injury images from four Guangzhou hospitals were labelled and used to train a neural network model. Evaluation metrics included mean intersection over union (MIoU), pixel accuracy (PA), and accuracy. Model performance was tested by comparing wound number, maximum dimensions and area extent.</p><p><strong>Results: </strong>From 1063 images, the model achieved 74% IoU, 88% PA and 83% accuracy for wound bed segmentation. Cohen's kappa coefficient for wound number was 0.810. Correlation coefficients were 0.900 for maximum length (mean difference 0.068 cm), 0.814 for maximum width (mean difference 0.108 cm) and 0.930 for regional extent (mean difference 0.527 cm<sup>2</sup>).</p><p><strong>Conclusion: </strong>The model demonstrated exceptional automated estimation capabilities, potentially serving as a crucial tool for informed decision-making in wound assessment.</p><p><strong>Implications and impact: </strong>This study promotes precision nursing and equitable resource use. The AI-based assessment model serves clinical work by assisting healthcare professionals in decision-making and facilitating wound assessment resource sharing.</p><p><strong>Reporting method: </strong>The STROBE checklist guided study reporting.</p><p><strong>Patient or public contribution: </strong>Patients provided image resources for model training.</p>\",\"PeriodicalId\":50236,\"journal\":{\"name\":\"Journal of Clinical Nursing\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2025-01-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Clinical Nursing\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1111/jocn.17645\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NURSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Clinical Nursing","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/jocn.17645","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NURSING","Score":null,"Total":0}
Development of a Deep Learning-Based Model for Pressure Injury Surface Assessment.
Aim: To develop a deep learning-based smart assessment model for pressure injury surface.
Design: Exploratory analysis study.
Methods: Pressure injury images from four Guangzhou hospitals were labelled and used to train a neural network model. Evaluation metrics included mean intersection over union (MIoU), pixel accuracy (PA), and accuracy. Model performance was tested by comparing wound number, maximum dimensions and area extent.
Results: From 1063 images, the model achieved 74% IoU, 88% PA and 83% accuracy for wound bed segmentation. Cohen's kappa coefficient for wound number was 0.810. Correlation coefficients were 0.900 for maximum length (mean difference 0.068 cm), 0.814 for maximum width (mean difference 0.108 cm) and 0.930 for regional extent (mean difference 0.527 cm2).
Conclusion: The model demonstrated exceptional automated estimation capabilities, potentially serving as a crucial tool for informed decision-making in wound assessment.
Implications and impact: This study promotes precision nursing and equitable resource use. The AI-based assessment model serves clinical work by assisting healthcare professionals in decision-making and facilitating wound assessment resource sharing.
Reporting method: The STROBE checklist guided study reporting.
Patient or public contribution: Patients provided image resources for model training.
期刊介绍：
The Journal of Clinical Nursing (JCN) is an international, peer reviewed, scientific journal that seeks to promote the development and exchange of knowledge that is directly relevant to all spheres of nursing practice. The primary aim is to promote a high standard of clinically related scholarship which advances and supports the practice and discipline of nursing. The Journal also aims to promote the international exchange of ideas and experience that draws from the different cultures in which practice takes place. Further, JCN seeks to enrich insight into clinical need and the implications for nursing intervention and models of service delivery. Emphasis is placed on promoting critical debate on the art and science of nursing practice.
JCN is essential reading for anyone involved in nursing practice, whether clinicians, researchers, educators, managers, policy makers, or students. The development of clinical practice and the changing patterns of inter-professional working are also central to JCN''s scope of interest. Contributions are welcomed from other health professionals on issues that have a direct impact on nursing practice.
We publish high quality papers from across the methodological spectrum that make an important and novel contribution to the field of clinical nursing (regardless of where care is provided), and which demonstrate clinical application and international relevance.
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