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

Study Overview
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Brief Summary
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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
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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.

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

Conditions
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Microbiota, Colonoscopy, Inflammatory Bowel Diseases

Intervention / Treatment
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* Diagnostic Test: Artificial Intelligence-assisted Fecal Microbiome Testing
* Procedure: Colonoscopy


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 70 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Intervention Model: Single Group Assignment
Interventional 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)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Colonoscopy<br>Fecal samples will be obtained from patients who are enrolled for colonoscopy procedure for the suspicion of inflammatory bowel disease | Diagnostic Test: Artificial Intelligence-assisted Fecal Microbiome Testing<br>* Next-generation sequencing of fecal samples and artificial intelligence analysis of test results<br>Procedure: Colonoscopy<br>* Colonoscopy procedure<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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). | 2 weeks | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Microbiome, Inflammatory bowel disease, Colonoscopy, Artificial intelligence, Screening test