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Evaluation of the Diagnostic Potential of Artificial Intelligence-assisted Fecal Microbiome Testing for Inflammatory Bowel Disease

Primary Purpose

Inflammatory Bowel Diseases, Microbiota, Colonoscopy

Status
Recruiting
Phase
Not Applicable
Locations
Turkey
Study Type
Interventional
Intervention
Artificial Intelligence-assisted Fecal Microbiome Testing
Colonoscopy
Sponsored by
Istanbul Medipol University Hospital
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional diagnostic trial for Inflammatory Bowel Diseases focused on measuring Microbiome, Inflammatory bowel disease, Colonoscopy, Artificial intelligence, Screening test

Eligibility Criteria

18 Years - 70 Years (Adult, Older Adult)All SexesDoes not accept healthy volunteers

Inclusion Criteria: being over 18 years of age not to be pregnant To apply with the complaint of chronic diarrhea (4 weeks or more) Not meeting any of the exclusion criteria Signing the voluntary consent form Exclusion Criteria: under 18 years old Pregnant or planning to become Acute diarrhea cases Have another known diagnosis of gastrointestinal disease ( malabsorption of any macronutrient, intestinal resection, celiac disease, etc.) Abdominal surgery other than appendectomy or hysterectomy history Psychiatric comorbidity Chronic disease that will affect the microbiome (cancer, diabetes, cardiovascular disease, liver diseases, neurological diseases, etc.) Use of drugs that may affect digestive function (including use in the last 4 weeks), probiotics, narcotic analgesics, lactulose (prebiotics) in the 4 weeks before the study Patients taking dietary supplements will not be included in the study.

Sites / Locations

  • Medipol University Esenler HospitalRecruiting

Arms of the Study

Arm 1

Arm Type

Experimental

Arm Label

Colonoscopy

Arm Description

Fecal samples will be obtained from patients who are enrolled for colonoscopy procedure for the suspicion of inflammatory bowel disease

Outcomes

Primary Outcome Measures

The diagnostic accuracy of the AI-assisted fecal microbiome testing in detecting inflammatory bowel disease compared to colonoscopy
The diagnostic accuracy of the AI-assisted fecal microbiome testing in detecting inflammatory bowel disease, as measured by sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC-ROC).

Secondary Outcome Measures

Full Information

First Posted
March 6, 2023
Last Updated
March 21, 2023
Sponsor
Istanbul Medipol University Hospital
Collaborators
Izmir Metropolitan Municipality Esrefpasa Hospital, Bozyaka Training and Research Hospital, Tepecik Training and Research Hospital, SB Istanbul Education and Research Hospital, Bursa City Hospital
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1. Study Identification

Unique Protocol Identification Number
NCT05797207
Brief Title
Evaluation of the Diagnostic Potential of Artificial Intelligence-assisted Fecal Microbiome Testing for Inflammatory Bowel Disease
Official Title
Evaluation of the Diagnostic Potential of Artificial Intelligence-assisted Fecal Microbiome Testing for Inflammatory Bowel Disease
Study Type
Interventional

2. Study Status

Record Verification Date
March 2023
Overall Recruitment Status
Recruiting
Study Start Date
April 10, 2023 (Anticipated)
Primary Completion Date
February 28, 2024 (Anticipated)
Study Completion Date
December 31, 2024 (Anticipated)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Sponsor
Name of the Sponsor
Istanbul Medipol University Hospital
Collaborators
Izmir Metropolitan Municipality Esrefpasa Hospital, Bozyaka Training and Research Hospital, Tepecik Training and Research Hospital, SB Istanbul Education and Research Hospital, Bursa City Hospital

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
Yes

5. Study Description

Brief Summary
The goal of this clinical trial is to evaluate the diagnostic potential of Artificial Intelligence-assisted Fecal Microbiome Testing for the diagnosis of inflammatory bowel disease. The main question it aims to answer is: • Is Artificial Intelligence-assisted Fecal Microbiome Testing a reliable screening test for inflammatory bowel disease? Participants will be asked to provide fecal samples to be analyzed with next-generation sequencing techniques. If there is a comparison group: Researchers will compare the diagnostic performance of AI-assisted Fecal Microbiome Testing with colonoscopy to see the correlation between the results of both interventions.
Detailed Description
Inflammatory bowel disease (IBD), which includes Crohn's disease and ulcerative colitis, is a chronic and complex disorder of the gastrointestinal tract that affects millions of people worldwide. IBD is typically diagnosed through a combination of patient history, physical examination, laboratory tests, and imaging studies. However, these methods can be expensive, invasive, and time-consuming, leading to delays in diagnosis and treatment. Recent research has focused on the potential of using fecal microbiome testing, which analyzes the composition and function of the gut microbiota, as a non-invasive and cost-effective screening tool for IBD. The gut microbiota is a complex ecosystem of microorganisms that plays a critical role in maintaining gut health and immune system function. Changes in the composition or function of the gut microbiota have been associated with the development and progression of IBD. Artificial intelligence (AI) algorithms can assist in the analysis of fecal microbiome testing data and provide a more accurate and reliable diagnosis of IBD. AI can identify patterns and trends in the complex data generated by microbiome testing that may not be apparent to human analysts, leading to earlier and more accurate diagnosis of IBD. Furthermore, AI can help identify potential biomarkers of IBD, which could be used for screening and monitoring disease activity. These biomarkers could provide insights into the underlying mechanisms of IBD, leading to the development of more effective therapies and personalized treatment approaches. Overall, the use of AI-assisted fecal microbiome testing for IBD screening holds significant potential for improving the diagnosis and management of this chronic and debilitating disease.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Inflammatory Bowel Diseases, Microbiota, Colonoscopy
Keywords
Microbiome, Inflammatory bowel disease, Colonoscopy, Artificial intelligence, Screening test

7. Study Design

Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Single Group Assignment
Model Description
Fecal samples will be obtained from patients who are enrolled for colonoscopy for the clinical suspicion of inflammatory bowel disease
Masking
None (Open Label)
Masking Description
The patients will be blinded to the microbiome results for the study period. The gastroenterologists will be blinded to microbiome results. The microbiome researchers will be blinded to colonoscopy results The statisticians will be blinded to both intervention results until the end of patient enrollment
Allocation
N/A
Enrollment
300 (Anticipated)

8. Arms, Groups, and Interventions

Arm Title
Colonoscopy
Arm Type
Experimental
Arm Description
Fecal samples will be obtained from patients who are enrolled for colonoscopy procedure for the suspicion of inflammatory bowel disease
Intervention Type
Diagnostic Test
Intervention Name(s)
Artificial Intelligence-assisted Fecal Microbiome Testing
Intervention Description
Next-generation sequencing of fecal samples and artificial intelligence analysis of test results
Intervention Type
Procedure
Intervention Name(s)
Colonoscopy
Intervention Description
Colonoscopy procedure
Primary Outcome Measure Information:
Title
The diagnostic accuracy of the AI-assisted fecal microbiome testing in detecting inflammatory bowel disease compared to colonoscopy
Description
The diagnostic accuracy of the AI-assisted fecal microbiome testing in detecting inflammatory bowel disease, as measured by sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC-ROC).
Time Frame
2 weeks

10. Eligibility

Sex
All
Minimum Age & Unit of Time
18 Years
Maximum Age & Unit of Time
70 Years
Accepts Healthy Volunteers
No
Eligibility Criteria
Inclusion Criteria: being over 18 years of age not to be pregnant To apply with the complaint of chronic diarrhea (4 weeks or more) Not meeting any of the exclusion criteria Signing the voluntary consent form Exclusion Criteria: under 18 years old Pregnant or planning to become Acute diarrhea cases Have another known diagnosis of gastrointestinal disease ( malabsorption of any macronutrient, intestinal resection, celiac disease, etc.) Abdominal surgery other than appendectomy or hysterectomy history Psychiatric comorbidity Chronic disease that will affect the microbiome (cancer, diabetes, cardiovascular disease, liver diseases, neurological diseases, etc.) Use of drugs that may affect digestive function (including use in the last 4 weeks), probiotics, narcotic analgesics, lactulose (prebiotics) in the 4 weeks before the study Patients taking dietary supplements will not be included in the study.
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Varol TUNALI, Dr.
Phone
00905556303231
Email
varoltunali@gmail.com
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Varol TUNALI, Dr.
Organizational Affiliation
Celal Bayar University Faculty of Medicine Parasitology Department
Official's Role
Principal Investigator
Facility Information:
Facility Name
Medipol University Esenler Hospital
City
Istanbul
State/Province
Other (Non U.s.)
ZIP/Postal Code
34230
Country
Turkey
Individual Site Status
Recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Naciye Cigdem Arslan, MD
Phone
05313890975
First Name & Middle Initial & Last Name & Degree
Naciye Cigdem Arslan

12. IPD Sharing Statement

Plan to Share IPD
No

Learn more about this trial

Evaluation of the Diagnostic Potential of Artificial Intelligence-assisted Fecal Microbiome Testing for Inflammatory Bowel Disease

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