Ignite Creation Date:
2026-03-26 @ 3:17 PM
Ignite Modification Date:
2026-03-26 @ 4:31 PM
Study NCT ID:
NCT07453056
Status:
ENROLLING_BY_INVITATION
Last Update Posted:
2026-03-05
First Post:
2026-02-12
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
The Efficacy of AI-Driven Feces Identification for Bowel Preparation Prior to Colonoscopy
Sponsor:
Fu Jen Catholic University Hospital
Organization:
Study Overview
Official Title:
A Prospective, Randomized, Evaluator Blind, Parallel Study of the Efficacy of AI-driven Feces Identifying for the Bowel Preparation Prior to Colonoscopy
Status:
ENROLLING_BY_INVITATION
Status Verified Date:
2025-05
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The objective is to enhance diagnostic outcomes by ensuring thorough bowel cleanliness through AI-driven stool identifying system for bowel preparation in subjects undergoing colonoscopy.Colonoscopy is a key procedure for the prevention and early detection of colorectal cancer, with its diagnostic accuracy highly dependent on the quality of bowel preparation. Inadequate bowel cleansing can lead to missed lesions, prolonged procedure times, and the need for repeat examinations. Despite public health efforts that have improved screening rates over the past decade, the adequacy of bowel preparation has remained relatively unchanged, posing a persistent clinical challenge.
With the rapid advancement of artificial intelligence (AI) technologies, deep learning-based image recognition has demonstrated outstanding performance in medical imaging applications. Recent studies have shown that AI can assist in real-time evaluation of stool appearance, providing patients with immediate feedback and personalized instructions to improve bowel preparation quality. Integrating AI systems into bowel cleansing protocols has the potential to enhance patient compliance, optimize bowel cleanliness, and consequently improve the diagnostic yield of colonoscopy.
This study aims to evaluate the efficacy of an AI-based stool identifying system (AI-SIS), combined with the use of a Prepackaged Low Residue Diet (PLD) and standard bowel preparation instructions. Through a prospective, randomized, evaluator-blind, parallel-group clinical trial design, the study seeks to generate scientific evidence supporting the integration of AI technology into routine bowel preparation practices.
With the rapid advancement of artificial intelligence (AI) technologies, deep learning-based image recognition has demonstrated outstanding performance in medical imaging applications. Recent studies have shown that AI can assist in real-time evaluation of stool appearance, providing patients with immediate feedback and personalized instructions to improve bowel preparation quality. Integrating AI systems into bowel cleansing protocols has the potential to enhance patient compliance, optimize bowel cleanliness, and consequently improve the diagnostic yield of colonoscopy.
This study aims to evaluate the efficacy of an AI-based stool identifying system (AI-SIS), combined with the use of a Prepackaged Low Residue Diet (PLD) and standard bowel preparation instructions. Through a prospective, randomized, evaluator-blind, parallel-group clinical trial design, the study seeks to generate scientific evidence supporting the integration of AI technology into routine bowel preparation practices.
Detailed Description:
Bowel preparation is the process of removing all stools from the colon in order to have a medical or surgical procedure such as a colonoscopy. It is important to clean the colon of all stools, food particles and anything else that may be present for several reasons. If you are having surgery on or near the colon, having stools presented is a risk for infection and can get in the way of some procedures. There are times when bowel preparation is done purely to prevent potential complications from surgery. For example, if your bowel was nicked during surgery, the infection risk would be dramatically decreased if the bowel was empty. In the case of a colonoscopy, stool in the colon can prevent the surgeon from seeing the tissue that is being inspected and would make it very difficult to introduce the lighted scope into the rectum and colon.
Colonoscopies have been shown to help reduce the incidence of colon cancer and deaths associated with the disease. Over the last decade, public health initiatives have helped drive up the rate of screenings, yet the current rates still fall short of public health targets set forth. Concurrently, the rate of adequate preparing for screening which is associated with colonoscopy effectiveness has changed little over the last 10 years.
With rapid advancements in artificial intelligence (AI), image recognition techniques have emerged as particularly valuable tools in medical applications. Deep learning algorithms, a subfield of AI, have demonstrated impressive performance in analyzing and categorizing complex medical imaging data, enabling efficient real-time decision support in clinical settings. Recent studies have highlighted the promising application of AI-driven image recognition in evaluating stool appearance and bowel cleanliness, providing immediate feedback and personalized instructions to patients undergoing bowel preparation. Incorporating such AI system has the potential to significantly enhance patient compliance, improve bowel preparation effectiveness, and consequently, increase diagnostic accuracy and the overall clinical value of colonoscopy procedures .This study is a prospective, randomized, evaluator-blind, parallel-group clinical trial designed to evaluate the efficacy of an AI-SIS in improving bowel preparation prior to colonoscopy. Participants will be randomly assigned in a 1:1 ratio into two groups, with approximately 170 participants per group, totaling 340 participants.
Group A (Intervention group): Participants will utilize the AI-SIS, designed to provide real-time feedback on bowel cleanliness through image classification of stool into three categories (Grade A: adequate cleansing; Grade B or C: inadequate cleansing requiring further bowel preparation). In addition to the standard hospital-provided educational instructions, this group will use the AI-SIS.
Group B (Control group): Participants will undergo standard bowel preparation and receive only the standard hospital-provided educational instructions without the assistance of the AI-SIS.
All participants will use Bowklean® during the bowel preparation process and adhere to dietary control using a Prepackaged Low Residue Diet (PLD). Both groups will receive identical standard in-hospital educational instructions regarding bowel preparation procedures. Additionally, participants from either group can call an 0800 toll-free hotline for consultation if they encounter any issues during the preparation.
Evaluator blinding will be maintained, with bowel cleanliness assessed using the Boston Bowel Preparation Scale (BBPS). Colonoscopy procedures and subsequent evaluations will be performed by independent, blinded colonoscopists to ensure objective and unbiased results.
Colonoscopies have been shown to help reduce the incidence of colon cancer and deaths associated with the disease. Over the last decade, public health initiatives have helped drive up the rate of screenings, yet the current rates still fall short of public health targets set forth. Concurrently, the rate of adequate preparing for screening which is associated with colonoscopy effectiveness has changed little over the last 10 years.
With rapid advancements in artificial intelligence (AI), image recognition techniques have emerged as particularly valuable tools in medical applications. Deep learning algorithms, a subfield of AI, have demonstrated impressive performance in analyzing and categorizing complex medical imaging data, enabling efficient real-time decision support in clinical settings. Recent studies have highlighted the promising application of AI-driven image recognition in evaluating stool appearance and bowel cleanliness, providing immediate feedback and personalized instructions to patients undergoing bowel preparation. Incorporating such AI system has the potential to significantly enhance patient compliance, improve bowel preparation effectiveness, and consequently, increase diagnostic accuracy and the overall clinical value of colonoscopy procedures .This study is a prospective, randomized, evaluator-blind, parallel-group clinical trial designed to evaluate the efficacy of an AI-SIS in improving bowel preparation prior to colonoscopy. Participants will be randomly assigned in a 1:1 ratio into two groups, with approximately 170 participants per group, totaling 340 participants.
Group A (Intervention group): Participants will utilize the AI-SIS, designed to provide real-time feedback on bowel cleanliness through image classification of stool into three categories (Grade A: adequate cleansing; Grade B or C: inadequate cleansing requiring further bowel preparation). In addition to the standard hospital-provided educational instructions, this group will use the AI-SIS.
Group B (Control group): Participants will undergo standard bowel preparation and receive only the standard hospital-provided educational instructions without the assistance of the AI-SIS.
All participants will use Bowklean® during the bowel preparation process and adhere to dietary control using a Prepackaged Low Residue Diet (PLD). Both groups will receive identical standard in-hospital educational instructions regarding bowel preparation procedures. Additionally, participants from either group can call an 0800 toll-free hotline for consultation if they encounter any issues during the preparation.
Evaluator blinding will be maintained, with bowel cleanliness assessed using the Boston Bowel Preparation Scale (BBPS). Colonoscopy procedures and subsequent evaluations will be performed by independent, blinded colonoscopists to ensure objective and unbiased results.
Study Oversight
Has Oversight DMC:
True
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: