title: Non-invasive optical imaging techniques for burn-injured tissue detection for debridement surgery.
abstract: Burn debridement is a challenging technique that requires significant skill to identify regions requiring excision and appropriate excision depth. A machine learning tool is being developed in order to assist surgeons by providing a quantitative assessment of burn-injured tissue. Three noninvasive optical imaging techniques capable of distinguishing between four kinds of tissue-healthy skin, viable wound bed, deep burn, and shallow burn-during serial burn debridement in a porcine model are presented in this paper. The combination of all three techniques considerably improves the accuracy of tissue classification, from 0.42 to almost 0.77.
authors.forename: Juan
authors.initials: J
authors.lastname: Heredia-Juesas
authors.forename: Jeffrey E
authors.initials: JE
authors.lastname: Thatcher
authors.lastname: Yang Lu
authors.forename: John J
authors.initials: JJ
authors.lastname: Squiers
authors.forename: Darlene
authors.initials: D
authors.lastname: King
authors.lastname: Wensheng Fan
authors.forename: J Michael
authors.initials: JM
authors.lastname: DiMaio
authors.forename: Jose A
authors.initials: JA
authors.lastname: Martinez-Lorenzo
mesh_terms: D000818:Animals; D002056:Burns / Q000000981:diagnostic imaging* / Q000601:surgery; D003646:Debridement / Q000379:methods*; D062109:Dermatologic Surgical Procedures; D004195:Disease Models, Animal; D061848:Optical Imaging / Q000379:methods*; D012867:Skin / Q000000981:diagnostic imaging*; D025321:Surgery, Computer-Assisted / Q000379:methods*; D013552:Swine