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CTRI Number  CTRI/2025/01/079145 [Registered on: 21/01/2025] Trial Registered Prospectively
Last Modified On: 21/01/2025
Post Graduate Thesis  Yes 
Type of Trial  Observational 
Type of Study   Cross Sectional Study 
Study Design  Single Arm Study 
Public Title of Study   Role of Artificial Intelligence (AI) in the diagnosis of Ulcerative Colitis (UC) 
Scientific Title of Study   Detection and categorization of colonoscopy images of Ulcerative Colitis (UC) patients using Artificial Intelligence (AI) 
Trial Acronym  NIL 
Secondary IDs if Any  
Secondary ID  Identifier 
NIL  NIL 
 
Details of Principal Investigator or overall Trial Coordinator (multi-center study)  
Name  Dr Santanu Mishra 
Designation  PhD Scholar 
Affiliation  Kasturba Medical College, MAHE, Manipal 
Address  Department of Gastroenterology and Hepatology, Kasturba Medical College, MAHE, Manipal

Udupi
KARNATAKA
576104
India 
Phone  7008563528  
Fax    
Email  mishrasantanu95@gmail.com  
 
Details of Contact Person
Scientific Query
 
Name  Dr Shiran Shetty 
Designation  Professor and Unit Head 
Affiliation  Kasturba Medical College, MAHE, Manipal 
Address  Department of Gastroenterology and Hepatology, Kasturba Medical College, MAHE, Manipal

Udupi
KARNATAKA
576104
India 
Phone  8861920517  
Fax    
Email  drshiran@gmail.com  
 
Details of Contact Person
Public Query
 
Name  Dr Shiran Shetty 
Designation  Professor and Unit Head 
Affiliation  Kasturba Medical College, MAHE, Manipal 
Address  Department of Gastroenterology and Hepatology, Kasturba Medical College, MAHE, Manipal

Udupi
KARNATAKA
576104
India 
Phone  8861920517  
Fax    
Email  drshiran@gmail.com  
 
Source of Monetary or Material Support  
Kasturba Medical College, MAHE, Manipal, Udupi, Karnataka, India - 576104 
 
Primary Sponsor  
Name  NA 
Address  NA 
Type of Sponsor  Other [NA] 
 
Details of Secondary Sponsor  
Name  Address 
NIL  NIL 
 
Countries of Recruitment     India  
Sites of Study  
No of Sites = 1  
Name of Principal Investigator  Name of Site  Site Address  Phone/Fax/Email 
Dr Santanu Mishra  Kasturba Medical College, Manipal  Department of Gastroenterology and Hepatology, Kasturba Medical College, MAHE, Manipal
Udupi
KARNATAKA 
7008563528

mishrasantanu95@gmail.com 
 
Details of Ethics Committee  
No of Ethics Committees= 1  
Name of Committee  Approval Status 
Kasturba Medical College and Kasturba Hospital Institutional Ethics Committee  Approved 
 
Regulatory Clearance Status from DCGI  
Status 
Not Applicable 
 
Health Condition / Problems Studied  
Health Type  Condition 
Patients  (1) ICD-10 Condition: K515||Left sided colitis,  
 
Intervention / Comparator Agent  
Type  Name  Details 
Intervention  NIL  NIL 
 
Inclusion Criteria  
Age From  18.00 Year(s)
Age To  80.00 Year(s)
Gender  Both 
Details  1. Participants must be 18 years of age or older.
2. Participants of all genders are eligible.
3. Participants must have a confirmed diagnosis of Inflammatory Bowel Disease – Ulcerative Colitis (IBD-UC).
 
 
ExclusionCriteria 
Details  1. Participants who are unwilling to participate in the study.
2. Patients who do not have complete clinical data.
 
 
Method of Generating Random Sequence   Not Applicable 
Method of Concealment   Not Applicable 
Blinding/Masking   Not Applicable 
Primary Outcome  
Outcome  TimePoints 
1. Highly Accurate and Standardized Diagnostic Model: Development of a reliable AI model for diagnosing UC from colonoscopy or sigmoidoscopy images, supported by standardized imaging protocols to enhance diagnostic precision and consistency.

2. Improved Clinical Efficiency and Patient Outcomes: Automation of UC diagnosis for faster, consistent assessments, leading to timely interventions, better disease management, and improved long-term outcomes for patients. 
18-24 months 
 
Secondary Outcome  
Outcome  TimePoints 
1. Integration with Clinical Data and Cost-Effectiveness: Integration of imaging data with clinical inputs (e.g., lab results, history) to enhance diagnostic accuracy while reducing unnecessary procedures and costs, making UC care more accessible and affordable.

2. Advancing Medical Innovation and Education: Contribution to medical innovation by expanding AI’s role in gastroenterology, coupled with real-time feedback tools for training medical students and clinicians in UC diagnostics. 
24-36 months 
 
Target Sample Size   Total Sample Size="380"
Sample Size from India="380" 
Final Enrollment numbers achieved (Total)= "Applicable only for Completed/Terminated trials"
Final Enrollment numbers achieved (India)="Applicable only for Completed/Terminated trials" 
Phase of Trial   N/A 
Date of First Enrollment (India)   10/02/2025 
Date of Study Completion (India) Applicable only for Completed/Terminated trials 
Date of First Enrollment (Global)  Date Missing 
Date of Study Completion (Global) Applicable only for Completed/Terminated trials 
Estimated Duration of Trial   Years="2"
Months="6"
Days="0" 
Recruitment Status of Trial (Global)   Not Applicable 
Recruitment Status of Trial (India)  Not Yet Recruiting 
Publication Details   N/A 
Individual Participant Data (IPD) Sharing Statement

Will individual participant data (IPD) be shared publicly (including data dictionaries)?  

Response - NO
Brief Summary  

The study will begin by obtaining approval from the Institutional Ethics Committee of Kasturba Medical College, MAHE, Manipal. Ethical clearance ensures that the research follows ethical guidelines, prioritizing participant safety and data privacy. Participants will be fully informed about the study’s purpose, risks, and benefits, and written informed consent will be obtained before any procedures commence.

Recruitment Process

Participants will be recruited from the outpatient and inpatient departments of the Gastroenterology and Hepatology department at Kasturba Medical College. The recruitment will focus on adults diagnosed with Ulcerative Colitis (UC), with participants selected based on predefined inclusion and exclusion criteria. Screening will involve reviewing medical histories, clinical records, and imaging studies to confirm the UC diagnosis. This recruitment process will be conducted over a set period to ensure a representative and adequate sample size for the study.

Consenting Process

After identifying potential participants, they will receive detailed information about the study, including its objectives, procedures, potential risks, and benefits. Informed consent will be obtained before any study-related assessments begin, and participants will be informed of their right to withdraw from the study at any time without affecting their standard medical care.

Assessments and Data Collection

Upon recruitment, participants will undergo an initial assessment, which includes collecting demographic data (such as age and gender) and conducting a clinical evaluation. Laboratory investigations (e.g., complete blood count, C-reactive protein, and fecal calprotectin) will be performed to assess disease activity. The key diagnostic procedure in the study is a colonoscopy/sigmoidoscopy, during which images will be captured for AI analysis. These endoscopic images will be scored using the Mayo Endoscopic Score (MES) to assess the activity of UC.

The Mayo Endoscopic Score (MES) is a straightforward and reliable tool used to gauge the level of inflammation in the colon of people with ulcerative colitis (UC). It’s based on what doctors observe during a colonoscopy and is a key part of the Mayo Score, which is commonly used in both medical practice and research.

The MES breaks down the appearance of the colon into four categories:

·       Grade 0: The colon looks normal or shows no signs of active inflammation.

·       Grade 1: There are mild changes, such as slight redness, a reduced vascular pattern, and some mild fragility in the mucosa.

·     Grade 2: Moderate inflammation is evident, with pronounced redness, the loss of visible blood vessels, fragility, and small erosions.

·       Grade 3: The inflammation is severe, with ulcers and spontaneous bleeding.

The collected images will undergo preprocessing to ensure they are standardized for AI analysis. This preprocessing involves adjusting brightness, contrast, and removing noise. Along with these images, clinical data will be integrated to help in building a personalized AI model that can assist in detecting and categorizing UC according to MES.

Intervention

This is not an interventional study in terms of treatment. However, the AI diagnostic tool will act as an intervention by categorizing the severity of UC based on colonoscopy/sigmoidoscopy images. The AI system will complement the standard diagnostic approach by providing an automated, objective assessment of disease severity. No additional procedures beyond standard care will be imposed on participants.

Study Endpoint and Withdrawal Criteria

The study’s endpoint will focus on successfully developing an AI model capable of detecting and categorizing UC severity. Participants can withdraw from the study at any point, and their data will be anonymized. Withdrawal will not affect their access to clinical care or ongoing medical treatment.

 

 
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