Zhou Jin, Luo Yuying, Darcy Julia W, Lafata Kyle J, Ruiz Jose R, Grego Sonia
Duke University, Durham, North Carolina, USA.
Mount Sinai Centre for Gastrointestinal Physiology & Motility, New York, New York, USA.
Neurogastroenterol Motil. 2025 Jan;37(1):e14954. doi: 10.1111/nmo.14954. Epub 2024 Nov 1.
Patients' report of bowel movement consistency is unreliable. We demonstrate the feasibility of long-term automated stool image data collection using a novel Smart Toilet and evaluate a deterministic computer-vision analytic approach to assess stool form according to the Bristol Stool Form Scale (BSFS).
Our smart toilet integrates a conventional toilet bowl with an engineered portal to image feces in a predetermined region of the plumbing post-flush. The smart toilet was installed in a workplace bathroom and used by six healthy volunteers. Images were annotated by three experts. A computer vision method based on deep learning segmentation and mathematically defined hand-crafted features was developed to quantify morphological attributes of stool from images.
474 bowel movements images were recorded in total from six subjects over a mean period of 10 months. 3% of images were rated abnormal with stool consistency BSFS 2 and 4% were BSFS 6. Our image analysis algorithm leverages interpretable morphological features and achieves classification of abnormal stool form with 94% accuracy, 81% sensitivity and 95% specificity.
Our study supports the feasibility and accuracy of long-term, non-invasive automated stool form monitoring with the novel smart toilet system which can eliminate the patient burden of tracking bowel forms.
患者对排便稠度的报告并不可靠。我们展示了使用新型智能马桶进行长期自动粪便图像数据收集的可行性，并评估了一种确定性计算机视觉分析方法，以根据布里斯托大便分类法（BSFS）评估大便形态。
我们的智能马桶将传统马桶与一个设计好的入口相结合，以便在冲水后管道的预定区域对粪便进行成像。该智能马桶安装在一个工作场所的卫生间，供六名健康志愿者使用。图像由三位专家进行标注。开发了一种基于深度学习分割和数学定义的手工特征的计算机视觉方法，以从图像中量化粪便的形态学属性。
在平均10个月的时间里，共记录了来自六名受试者的474张排便图像。3%的图像被评定为大便稠度BSFS 2异常，4%为BSFS 6。我们的图像分析算法利用可解释的形态学特征，对异常大便形态的分类准确率达到94%，灵敏度为81%，特异性为95%。
我们的研究支持了使用新型智能马桶系统进行长期、非侵入性自动大便形态监测的可行性和准确性，该系统可以消除患者追踪大便形态的负担。
