使用深度学习进行压疮分类：评估模型性能的临床试验
Pressure Ulcer Categorisation using Deep Learning: A Clinical Trial to Evaluate Model Performance
摘要：
压疮是患者和医疗保健专业人员面临的挑战。在英国，每年有70万人受到压力溃疡的影响。治疗他们每天花费国民健康服务380万英镑。其病因是复杂和多因素的。然而，有证据表明，老年、与疾病有关的久坐不动的生活方式和不健康的饮食习惯之间存在着密切的联系。压疮是由皮肤直接接触床或椅子而不经常改变位置引起的。尿失禁和大便失禁、糖尿病以及限制体位和营养的损伤也是已知的风险因素。指南和治疗方法是存在的，但它们的实施和成功在不同的医疗环境中各不相同。这主要是因为医疗保健从业者有a）在处理压力溃疡的经验很少，和b）一般缺乏对压力溃疡治疗的理解。如果管理不善，压疮会导致严重的疼痛，生活质量差，以及显著的医疗费用。在本文中，我们报告了Mersey Care NHS Foundation Trust进行的一项临床试验的结果，该试验评估了更快的基于区域的卷积神经网络和移动平台的性能，该平台对压疮进行分类和记录。神经网络将I、II、III和IV类压疮、深部组织损伤和不可分期的压疮分类。由地区护士拍摄的压疮照片通过4/5G通信传输到推理服务器进行分类。分类图像被存储和审查，以评估模型的预测和相关性，作为临床决策和标准化报告的工具。使用@.75置信度评分阈值，研究结果生成的平均精度=0.6796，召回率=0.6997，F1评分=0.6786，有45个假阳性。
英文摘要：
Pressure ulcers are a challenge for patients and healthcare professionals. In the UK, 700,000 people are affected by pressure ulcers each year. Treating them costs the National Health Service £3.8 million every day. Their etiology is complex and multifactorial. However, evidence has shown a strong link between old age, disease-related sedentary lifestyles and unhealthy eating habits. Pressure ulcers are caused by direct skin contact with a bed or chair without frequent position changes. Urinary and faecal incontinence, diabetes, and injuries that restrict body position and nutrition are also known risk factors. Guidelines and treatments exist but their implementation and success vary across different healthcare settings. This is primarily because healthcare practitioners have a) minimal experience in dealing with pressure ulcers, and b) a general lack of understanding of pressure ulcer treatments. Poorly managed, pressure ulcers lead to severe pain, poor quality of life, and significant healthcare costs. In this paper, we report the findings of a clinical trial conducted by Mersey Care NHS Foundation Trust that evaluated the performance of a faster region-based convolutional neural network and mobile platform that categorised and documented pressure ulcers. The neural network classifies category I, II, III, and IV pressure ulcers, deep tissue injuries, and unstageable pressure ulcers. Photographs of pressure ulcers taken by district nurses are transmitted over 4/5G communications to an inferencing server for classification. Classified images are stored and reviewed to assess the model's predictions and relevance as a tool for clinical decision making and standardised reporting. The results from the study generated a mean average Precision=0.6796, Recall=0.6997, F1-Score=0.6786 with 45 false positives using an @.75 confidence score threshold.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
