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Effectiveness of the AI-Supporter in Reducing Urinary Tract Infections

NCT06613503 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 60

Last updated 2024-09-26

No results posted yet for this study

Summary

The "AI Supporter," an intelligent excretion management robot, leverages artificial intelligence-based vision recognition to autonomously detect and cleanse affected areas, followed by drying and changing the diaper, thereby reducing caregiver strain and enhancing care quality. This study aims to assess the efficacy of the "AI Supporter" in decreasing the incidence of urinary tract infections and incontinence-associated dermatitis among incontinent patients, in addition to exploring its cost-effectiveness.

Adopting an experimental (two groups) and longitudinal design, this research utilizes both convenience and random sampling strategies. The study anticipates recruiting 60 female subjects who have been confined to bed for more than three months with urinary and/or fecal incontinence. Participants will intermittently use the AI Supporter over a 14-day period. Measurement tools include routine urine analysis.

Conditions

Interventions

DEVICE

AI-supporter

rticipants in the experimental group will use the AI-supporter, an intelligent excretion management robot. This device utilizes AI-driven visual recognition technology to automatically detect urine and feces, followed by a cleaning and drying process. When the AI-supporter detects excretion, it activates an automated sequence that washes, dries, and sanitizes the perineal area without requiring the caregiver to remove the diaper. The AI-supporter also records relevant data, such as the time, frequency, and weight of excretion, for further analysis. This intervention is designed to reduce the incidence of urinary tract infections (UTIs) and incontinence-associated dermatitis (IAD), as well as lessen the workload for caregivers

Sponsors & Collaborators

  • China Medical University Hospital

    lead OTHER

Principal Investigators

  • Kwo-Chen Lee, ph.D · 011+886+4+22053366#7102

Study Design

Allocation
RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
SINGLE
Model
PARALLEL

Eligibility

Min Age
20 Years
Sex
FEMALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-07-22
Primary Completion
2025-10-31
Completion
2025-10-31

Countries

  • Taiwan

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06613503 on ClinicalTrials.gov