SCH:Smartphone Wound Image Parameter Analysis and Decision Support in Mobile Env

项目总结（见说明）： 在美国，慢性伤口影响着650万患者，估计治疗费用为250亿美元。我们的团队提出了一项研究，以推进我们现有的NSF资助的智能手机伤口分析系统，该系统可以帮助患者监测他们的糖尿病足溃疡，为他们提供关于愈合进展的即时反馈。我们的伤口系统分析患者伤口的智能手机图像，检测伤口面积和组织成分，并通过将当前图像与过去图像进行比较来生成专有的愈合评分。我们设想的慢性伤口评估系统将支持护理团队在访问患者时做出基于证据的决策，并将伤口护理推向数字客观性。我们将数字客观性定义为从图像中自主提取的伤口评估指标的综合，以生成客观的可操作反馈，使未接受伤口专家培训的临床医生能够提供“标准化的伤口护理”。数字客观性与目前基于医生经验的主观、目视检查伤口的做法形成对比。第一个目标是开发图像处理算法，以减轻在某些临床或家庭环境中以及从任意相机角度和距离拍摄伤口时由非理想照明引起的伤口分析错误。虽然我们以前的伤口系统在理想条件下工作良好，但非理想照明会导致很大的误差，并且在极端情况下会将健康皮肤检测为伤口区域。第二个目标是扩展我们现有的伤口分析系统，该系统仅针对糖尿病伤口，以处理动脉，静脉和压力溃疡，扩大潜在用户。第三个目标是综合算法，自主生成可操作的伤口决策规则，这些规则是从实际伤口临床医生的决策中学习到的。这项研究是伍斯特理工学院（WPI）（图像处理，机器学习和智能手机编程的技术专业知识）和马萨诸塞州医学院（UMMS）（伤口临床专业知识和伤口患者招募以验证我们的工作）的联合工作

PROJECT SUMMARY (See instructions): Chronic wounds affect 6.5 million patients in the U.S., with an estimated treatment cost of $25 billion. Our team proposes research to advance our existing NSF-funded smartphone wound analysis system, which helps patients monitor their diabetic foot ulcers, providing them with instant feedback on healing progress. Our wound system analyzes a smartphone image of the patients' wound, detects the wound area and tissue composition, and generates a proprietary healing score by comparing the current image with a past image. Our envisioned chronic wound assessment system will support evidence-based decisions by the care team while visiting patients, and move wound care toward digital objectivity. We define digital objectivity as the synthesis of wound assessment metrics that are extracted autonomously from images in order to generate objective actionable feedback, enabling clinicians not trained as wound specialists to deliver "standardized wound care". Digital objectivity contrasts with the current practice of subjective, visual inspection of wounds based on physician experience. The first aim will develop image processing algorithms to mitigate wound analysis errors caused by non-ideal lighting in some clinical or home settings, and when the wound is photographed from arbitrary camera angles and distance. While our previous wound system worked well in ideal conditions, non-ideal lighting caused large errors and healthy skin was detected as the wound area in extreme cases. The second aim extends our existing wound analysis system that targets only diabetic wounds to handle arterial, venous and pressure ulcers, expanding the potential user. The third aim will synthesize algorithms that autonomously generate actionable wound decision rules that are learned from decisions taken by actual wound clinicians. This research is joint work of Worcester Polytechnic Institute (WPI) (technical expertise in image processing, machine learning and smartphone programming) and University of Massachusetts Medical School (UMMS) (clinical expertise on wounds, and wound patient recruitment to validate our work)