Colonoscopy, Inflammatory Bowel Diseases, Microbiota
Conditions
Keywords
Microbiome, Inflammatory bowel disease, Colonoscopy, Artificial intelligence, Screening test
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.
Interventions
DIAGNOSTIC_TESTArtificial Intelligence-assisted Fecal Microbiome Testing
Next-generation sequencing of fecal samples and artificial intelligence analysis of test results
PROCEDUREColonoscopy
Colonoscopy procedure
Sponsors
Izmir Metropolitan Municipality Esrefpasa Hospital
Bozyaka Training and Research Hospital
Tepecik Training and Research Hospital
SB Istanbul Education and Research Hospital
Bursa City Hospital
Istanbul Medipol University Hospital
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
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
Intervention model description
Fecal samples will be obtained from patients who are enrolled for colonoscopy for the clinical suspicion of inflammatory bowel disease
Eligibility
Sex/Gender
ALL
Age
18 Years to 70 Years
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The diagnostic accuracy of the AI-assisted fecal microbiome testing in detecting inflammatory bowel disease compared to colonoscopy | 2 weeks | 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). |
Countries
Turkey (Türkiye)
Contacts
Primary ContactVarol TUNALI, Dr.
Outcome results
None listed