Bio-X builds on RIT's core technical strengths to address biological, health-care, and medical challenges of the 21st century through interdisciplinary research.
An RIT team is seeking to improve the diagnosis and assessment of psoriasis through the creation of a multimodal, image-based analysis system. The project seeks to improve treatment for individual patients and allow for enhanced longitudinal studies of psoriasis.
"Presently, dermatologists use a method called the Psoriasis Area Severity Index (PASI) score to analyze affected areas of the skin," notes Christye Sisson, professor of biomedical photography at RIT and leader of the project team. "However, the process can be very subjective as each dermatologist could potentially 'grade' patients differently."
The lack of empirical data can make it difficult to compare assessments made by different dermatologists or by different research groups studying the disease.
"Through the use of novel imaging technologies we are seeking to create a standardized and repeatable process that will be more accurate and allow for better assessment across populations," Sisson adds.
Sisson is working with Francisco Tausk, MD, a professor of dermatology at University of Rochester Medical Center, to create an imaging tool based on anomaly detection software which has previously been used in remote sensing applications. The tool will include multiple imaging modalities and assess the area of coverage based on the three criteria currently used by the PASI score: thickness, redness, and scale.
The team is currently testing imaging techniques, including thermal, ultra violet reflectance, and LIDAR, to identify the best method for assessing each criteria. They will then work with the University of Rochester to assess severity on current psoriasis patients and compare it to the PASI scores for each person.
Sisson and Tausk hope to ultimately create a standardized multimodal imaging system that could be implemented by dermatologists and hospitals nationwide.
"By modifying imaging technology that has already been developed we can create a repeatable, quantifiable method for diagnosing and ultimately treating patients with this disease," Sisson says.