STTR Phase I: Intelligent Scoring of Inflammatory Skin Disease Progression

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The broader impact and commercial potential of this Small Business Technology Transfer(STTR) Phase I project is to empower providers with the unprecedented ability to treat patientswith chronic inflammatory skin disease effectively and at a lower cost. Today, primary carephysicians often refer patients with these conditions to dermatologists for the initial diagnosisand multiple follow-up appointments, especially when patients are prescribed high cost, hightouch therapies like biologics or procedures, such as phototherapy. By introducing thisautomated severity scoring system, these providers will have the expertise of dermatologists attheir fingertips, giving them the ability to assess disease severity and provide systemictreatments previously available only through specialists. It will also establish more objectiveassessments, evaluating patient progress to adjust dosage and treatments as necessary. Forpharmaceutical companies, the ability to have non-specialists make these severity assessmentsduring clinical trials reduces expensive and restrictive staffing requirements. Less expensive trials canhelp expedite new drugs to market. This innovation will enhance scientific and technologicalunderstanding by applying known machine learning techniques in a novel manner to solve adifficult visual problem.This Small Business Technology Transfer (STTR) Phase I project will prototype a clinical toolthat can assess images of a chronic skin inflammation, determine its severity, and suggesttreatment. This system will be able to guide users with minimal training through the imagecollection process, determine if the information is sufficient, and request additional information ifrequired via novel machine learning techniques. Currently, clinicians trying to assess theseverity of chronic inflammatory skin diseases rely on a series of estimations and manualweighted averages, a time consuming and biased process. This Phase I research will explorewhat information is necessary for an algorithm to determine the severity of a psoriasis case. Itwill determine if it is possible for a machine to guide an imager through imaging regions ofinterest at higher detail, rather than the entire body at large. It will also explore how quickly andpractically such a calculation can be performed. The end goal of the project is a workingprototype that can consistently score a few psoriasis cases and serve as a proof of concept forexpansion of the prototype in future work.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.