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

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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)