# Use of a Novel Toilet Seat to Passively Collect Digital Biomarkers in Assisted Living Settings

> **NIH NIH R43** · TOI LABS, INC. · 2021 · $299,676

## Abstract

ABSTRACT
In senior living facilities, some of the most common health related problems stem from the GI tract and urinary
system including Clostridium difficile Infections (CDI), Urinary Tract Infections (UTI), Constipation and Colon
Cancer. The best way to lower costs and treat these conditions effectively is early diagnosis and treatment.
Current clinical management for these conditions, as well as many others, include monitoring of specific excreta
characteristics including urine color, urination frequency, urine duration, stool color, stool frequency and stool
consistency. These logs are the best tool doctors currently have to screen for conditions such as UTIs, infectious
diarrhea, dehydration, chronic kidney disease, GI bleeding, GU surgery, inflammatory bowel disease, and
constipation, that can lead to hospitalizations and readmissions. However, these logs are on average 61%
inaccurate at reporting adverse episodes such as diarrhea. Toi Labs has developed the patented TrueLoo
technology to take pictures of excreta and, using machine learning algorithms, classify the toileting event using
Digital Biomarkers (DBMs). The ability to create an excreta log to accurately deliver detailed information to
doctors and healthcare providers can revolutionize healthcare by notifying when further screening (urine or fecal)
is necessary. This novel, low-cost approach of machine learning and image identification technology that
requires no change in behavior of the user will enable currently undetectable links between medical records and
specific excreta patterns. In the future, the machine learning algorithm may be able to determine links between
these excreta logs and the onset of specific diseases. In this study we will be collecting manual excreta records
and automated TrueLoo digital excreta records in memory care and assisted living facilities, and compare them
against each other and against patients’ deidentified medical records. We will determine correlative data between
excreta logs and adverse events to establish conditional threshold for each type of adverse event and, using ML,
try and establish individual conditional thresholds for reporting to caregiving staff. We will compare the manual
and digital logs to assess the difference between speed of adverse episode identification when using TrueLoo
as compared to manual logs.

## Key facts

- **NIH application ID:** 10325655
- **Project number:** 1R43AG074812-01
- **Recipient organization:** TOI LABS, INC.
- **Principal Investigator:** Parmoon Bayat Sarmadi
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $299,676
- **Award type:** 1
- **Project period:** 2021-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10325655

## Citation

> US National Institutes of Health, RePORTER application 10325655, Use of a Novel Toilet Seat to Passively Collect Digital Biomarkers in Assisted Living Settings (1R43AG074812-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10325655. Licensed CC0.

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