Smart Toilet with Artificial Intelligence for Remote Management of Inflammatory Bowel Disease

摘要 炎症性肠病(IBD)影响着300万美国成年人，由于慢性，需要 终身医疗，对患者的生活质量产生负面影响，并导致大规模医疗保健 成本。病程的特点是病情缓解和发作，因此是疾病 通过药物和饮食干预进行管理需要经常监测。侵入性较小 与内窥镜检查相比，对粪便生物标志物钙保护素的纵向跟踪越来越多地被用于 实现并保持减刑。通过基于应用程序的日记远程管理疾病 评估疾病活动性的大便运动和家庭测量钙保护素已经被 如果始终如一地实施，就会发现有效；然而，患者的依从性低至30%。 我们提出了一种技术，能够自动和无缝地监测肠道运动 和钙保护素，从而大大提高了患者对跟踪方案的依从性。这个 创新是一种智能采样马桶系统：一个改装成传统马桶的工程门户 马桶，小心地和冲完水后，检查粪便外观并实现免提粪便 取样以供分析。该系统已在实验室中用人类粪便进行了演示。 各种粪便形式的标本，包括松散的粪便。人工智能(AI) 建立了粪便图像分析和分类的方法。 这项研究的目的是论证现场测量钙保护素的可行性。 智能采样马桶，人类受试者使用的集成粪便的原型系统 形态分析。具体目标1是一项使用商业侧向流动分析的台式研究 评估粪便中钙保护素测定的敏感性和特异性 工程设计的门户。在一项尖峰研究之后，我们将在临床标本上测量钙保护素 与杜克大学胃肠病诊所合作的IBD患者。 在具体目标2中，我们将在实验室外部署智能马桶原型，供 对人类粪便形态分类的人工智能算法进行改进。我们将评估 来自使用厕所的健康受试者和冲洗临床标本的标本 取自IBD患者。 这项研究中获得的数据将成为智能马桶系统第二阶段研究的基础 通过杜克大学的IBD诊所安装供患者使用。最终，我们设想智能马桶 安装在家中的系统，有利于IBD疾病管理和提高患者质量 通过消除访问粪便数据的障碍并改善临床结果，改善生活。

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.