ABSTRACT Inflammatory Bowel Disease (IBD) affects three million US adults and, due to chronicity, requires lifelong medical care, negatively impacts patient quality of life, and results in large healthcare costs. The disease course is characterized by remissions and flare-ups, thus disease management by medication and dietary interventions requires frequent monitoring. Less invasive than endoscopy, longitudinal tracking of the fecal biomarker calprotectin is increasingly used to achieve and maintain remission. Remote disease management through app-based diaries of bowel movements to assess disease activity and at-home measurement of calprotectin have been found effective when implemented consistently; however, patient adherence is as low as 30%. We propose a technology that enables automatic and seamless monitoring of bowel movements and calprotectin, thereby greatly increasing patient adherence to tracking regimens. The innovation is a Smart Sampling Toilet system: an engineered portal retrofitted to a conventional toilet that, discreetly and post-flush, inspects stool appearance and enables hands-free stool sampling for analysis. The system has been demonstrated in the laboratory with human stool specimens across the range of stool forms, including loose stools. An artificial intelligence (AI) method has been developed for stool image analysis and classification. The objective of this study is to demonstrate feasibility of onsite measurement of calprotectin by the Smart Sampling Toilet, a prototype system for use by human subjects that integrates stool morphology analysis. Specific Aim 1 is a benchtop study using commercial lateral flow assays to assess sensitivity and specificity of calprotectin measurements from stool sampled through the engineered portal. After a spiking study, we will measure calprotectin on clinical specimens from patients with IBD through a collaboration with the Duke University Gastroenterology IBD Clinic. In Specific Aim 2, we will deploy a Smart Toilet prototype outside of the laboratory for use by human subjects to refine the AI algorithm for stool morphology classification. We will evaluate specimens both from healthy subjects using the toilet and from flushing clinical specimens obtained from IBD patients. The data obtained in this study will be the basis for a Phase 2 study with the Smart Toilet system installed for use by patients through the Duke IBD clinic. Ultimately, we envision Smart Toilet systems installed in the home to benefit IBD disease management and improve patient quality of life by removing barriers to stool-data access and enabling improved clinical outcomes.