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

Information

  • Research Project
  • 10066353
  • ApplicationId
    10066353
  • Core Project Number
    R01EB025801
  • Full Project Number
    5R01EB025801-04
  • Serial Number
    025801
  • FOA Number
    PAR-16-601
  • Sub Project Id
  • Project Start Date
    1/1/2018 - 7 years ago
  • Project End Date
    11/30/2021 - 3 years ago
  • Program Officer Name
    LASH, TIFFANI BAILEY
  • Budget Start Date
    12/1/2020 - 4 years ago
  • Budget End Date
    11/30/2021 - 3 years ago
  • Fiscal Year
    2021
  • Support Year
    04
  • Suffix
  • Award Notice Date
    12/11/2020 - 4 years ago

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

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)

IC Name
NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING
  • Activity
    R01
  • Administering IC
    EB
  • Application Type
    5
  • Direct Cost Amount
    266443
  • Indirect Cost Amount
    76784
  • Total Cost
    343227
  • Sub Project Total Cost
  • ARRA Funded
    False
  • CFDA Code
    286
  • Ed Inst. Type
    SCHOOLS OF ARTS AND SCIENCES
  • Funding ICs
    NIBIB:343227\
  • Funding Mechanism
    Non-SBIR/STTR RPGs
  • Study Section
    ZRG1
  • Study Section Name
    Special Emphasis Panel
  • Organization Name
    WORCESTER POLYTECHNIC INSTITUTE
  • Organization Department
    BIOSTATISTICS & OTHER MATH SCI
  • Organization DUNS
    041508581
  • Organization City
    WORCESTER
  • Organization State
    MA
  • Organization Country
    UNITED STATES
  • Organization Zip Code
    016092247
  • Organization District
    UNITED STATES