Regular health monitoring can result in early detection of disease, accelerate the delivery of medical care and, therefore, considerably improve patient outcomes for countless medical conditions that affect public health. A substantial unmet need remains for technologies that can transform the status quo of reactive health care to preventive, evidence-based, person-centred care. With this goal in mind, platforms that can be easily integrated into people’s daily lives and identify a range of biomarkers for health and disease are desirable. However, urine — a biological fluid that is produced in large volumes every day and can be obtained with zero pain, without affecting the daily routine of individuals, and has the most biologically rich content — is discarded into sewers on a regular basis without being processed or monitored. Toilet-based health-monitoring tools in the form of smart toilets could offer preventive home-based continuous health monitoring for early diagnosis of diseases while being connected to data servers (using the Internet of Things) to enable collection of the health status of users. In addition, machine learning methods can assist clinicians to classify, quantify and interpret collected data more rapidly and accurately than they were able to previously. Meanwhile, challenges associated with user acceptance, privacy and test frequency optimization should be considered to facilitate the acceptance of smart toilets in society.

定期健康监测可以及早发现疾病，加快医疗服务的提供，从而显着改善影响公众健康的无数医疗状况的患者预后。对于可以将反应性医疗保健现状转变为预防性、循证、以人为本的医疗保健的技术，仍然存在大量未满足的需求。考虑到这一目标，可以轻松融入人们日常生活并识别一系列健康和疾病生物标志物的平台是可取的。然而，尿液——一种每天大量产生的生物液体，可以在零痛苦的情况下获得，不影响个人的日常生活，并且具有最丰富的生物含量——被定期丢弃到下水道中而不进行处理或监控。智能马桶形式的基于厕所的健康监测工具可以提供基于家庭的预防性持续健康监测，以早期诊断疾病，同时连接到数据服务器（使用物联网）以收集用户的健康状况。此外，机器学习方法可以帮助临床医生比以前更快、更准确地对收集的数据进行分类、量化和解释。同时，应考虑与用户接受、隐私和测试频率优化相关的挑战，以促进社会对智能马桶的接受。