Smartphones are uniquely poised to measure psoriatic disease owing to their capacity for high-quality skin imaging, ability to measure joint function with gyro/accelerometry-based tasks, and their ubiquitous use in the patient population. The Psorcast Study will be a remote, nationwide observational study to collect multidimensional, high-resolution time-series data from psoriasis patients’ smartphone sensors. The Psorcast app measures symptoms, disease-influencing factors, and outcomes as a foundational dataset for creating personalized forecasts of disease activity. The overarching hypothesis driving this approach is that patient-generated smartphone measurements of psoriatic disease can complement clinical measures to drive earlier detection, intervention, and to optimize disease management.
Central to the success of the Psorcast Study will be the ability to utilize these large, aggregated datasets to investigate the automation of symptom detection from image data and passive monitoring of functional movements. Similarly, these datasets will serve as a foundation for predictive machine learning models of therapeutic responder/non-responder status and risk stratification for the development of psoriatic arthritis.
While Psorcast is driven in part by novel technology and algorithm development, the innovation of this study stems from the collaborative, interdisciplinary team combining clinical expertise, design, engineering, and data science. Importantly, all measurement tool software and de-identified data sets from this study will be made openly available to maximize the impact of this work and catalyze collaborative science.