At present, the ubiquity method to diagnose the severity of diabetic feet (DF) depends on professional podiatrists. However, in most cases, professional podiatrists have a heavy workload, especially in underdeveloped and developing countries and regions, and there are often insufficient podiatrists to meet the rapidly growing treatment needs of DF patients. It is necessary to develop a medical system that assists in diagnosing DF in order to reduce part of the workload for podiatrists and to provide timely relevant information to patients with DF. In this paper, we have developed a system that can classify and locate Wagner ulcers of diabetic foot in real-time. First, we proposed a dataset of 2688 diabetic feet with annotations. Then, in order to enable the system to detect diabetic foot ulcers in real time and accurately, this paper is based on the YOLOv3 algorithm coupled with image fusion, label smoothing, and variant learning rate mode technologies to improve the robustness and predictive accuracy of the original algorithm. Finally, the refinements on YOLOv3 was used as the optimal algorithm in this paper to deploy into Android smartphone to predict the classes and localization of the diabetic foot with real-time. The experimental results validate that the improved YOLOv3 algorithm achieves a mAP of 91.95%, and meets the needs of real-time detection and analysis of diabetic foot Wagner Ulcer on mobile devices, such as smart phones. This work has the potential to lead to a paradigm shift for clinical treatment of the DF in the future, to provide an effective healthcare solution for DF tissue analysis and healing status.
DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification
Manchester Metropolitan University（曼彻斯特城市大学）；University of Manchester（曼彻斯特大学）；Lancashire Teaching Hospital（兰开夏教学医院）；Lancashire Care NHS Foundation Trust（兰开夏护理NHS基金会信托）
Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers
Faculty of Science and Engineering, Manchester Metropolitan University（曼彻斯特城市大学科学与工程学院）；Lanchashire Teaching Hospitals（兰开夏教学医院）；Waikato District Health Board（怀卡托地区卫生局）
WoundNet-Ensemble: A Novel IoMT System Integrating Self-Supervised Deep Learning and Multi-Model Fusion for Automated, High-Accuracy Wound Classification and Healing Progression Monitoring
The Catholic University of America（美国天主教大学）；Center for Advanced Research in Computer Engineering（计算机工程高级研究中心）
UlcerGPT: A Multimodal Approach Leveraging Large Language and Vision Models for Diabetic Foot Ulcer Image Transcription
KITE Research Institute（凯特研究所）；University Health Network（大学健康网络）；Institute of Biomedical Engineering, University of Toronto（多伦多大学生物医学工程研究所）；University of Toronto Mississauga（多伦多大学密西沙加分校）
Boosting EfficientNets Ensemble Performance via Pseudo-Labels and Synthetic Images by pix2pixHD for Infection and Ischaemia Classification in Diabetic Foot Ulcers
University of Applied Sciences and Arts Dortmund (FH Dortmund)（多特蒙德应用科学与艺术大学）；University Hospital Essen（埃森大学医院）
ConMatFormer: A Multi-attention and Transformer Integrated ConvNext based Deep Learning Model for Enhanced Diabetic Foot Ulcer Classification
Department of Information Technology（信息科技系）；Charles Darwin University（查尔斯·达尔文大学）；Department of Computer Science and Engineering（计算机科学与工程系）；University of Liberal Arts Bangladesh（孟加拉自由大学）；BRAC University（BRAC大学）；Center for Advanced Analytics (CAA), COE for Artificial Intelligence, Faculty of Engineering & Technology (FET)（高级分析中心（CAA）、人工智能学院、工程与技术学院）；Multimedia University Melaka（马六甲多媒体大学）；American International University-Bangladesh（美国国际大学-孟加拉）
Recognition of Ischaemia and Infection in Diabetic Foot Ulcers: Dataset and Techniques
Manchester Metropolitan University（曼彻斯特城市大学）；Lancashire Teaching Hospital（兰开夏教学医院）；University of Manchester（曼彻斯特大学）；Manchester Royal Infirmary（曼彻斯特皇家医院）；Sun Yat-sen University（中山大学）
Pressure Ulcer Categorisation using Deep Learning: A Clinical Trial to Evaluate Model Performance
Liverpool John Moores University（利物浦约翰摩尔斯大学）；Mersey Care NHS Foundation Trust（默西医疗NHS基金会信托）
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。
Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary
Manchester Metropolitan University（曼彻斯特城市大学）；Lancashire Teaching Hospitals NHS Foundation Trust（兰开夏教学医院NHS基金会信托）；Waikato District Health Board（怀卡托地区卫生局）；University of Southern California（南加利福尼亚大学）
Synthesizing Diabetic Foot Ulcer Images with Diffusion Model
Institute of Biomedical Engineering, University of Toronto（多伦多大学生物医学工程研究所）；University Health Network（多伦多大学健康网络）；Zivot Limb Preservation Centre, Peter Lougheed Centre（齐沃特肢体保护中心（彼得·劳埃德中心））；Cumming School of Medicine, University of Calgary（卡尔加里大学卡明医学院）
Automated multi-class wound assessment using dedicated instance segmentation models for boundary detection and classification
Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology（密苏里科技大学机械与航空航天工程系）；Intelligent System Center, Missouri University of Science and Technology（密苏里科技大学智能系统中心）；Center for Biomedical Research, Missouri University of Science and Technology（密苏里科技大学生物医学研究中心）；Wound Care Department, Charles George Department of Veterans Affairs Medical Center（查尔斯·乔治退伍军人事务医疗中心伤口护理部）
Gastrointestinal Mucosal Problems Classification with Deep Learning
School of Medicine, Islamic Azad University Medical Sciences Najaf Abad（伊斯兰阿扎德大学纳贾夫阿巴德医学科学校区医学院）；School of Medicine, Isfahan University of Medical Sciences（伊斯法罕医科大学医学院）；Department of Computer Engineering, Science and Research Branch, Islamic Azad University（伊斯兰阿扎德大学科学研究校区计算机工程系）
Integrated Image and Location Analysis for Wound Classification: A Deep Learning Approach
University of Wisconsin-Milwaukee（威斯康星大学密尔沃基分校）；Advancing the Zenith of Healthcare (AZH) Wound and Vascular Center（医疗保健提升巅峰伤口与血管中心）；University of Wisconsin Milwaukee（威斯康星大学密尔沃基分校）
