FULL DETAILS (Read-only)  -> Click Here to Create PDF for Current Dataset of Trial
CTRI Number  CTRI/2024/10/076014 [Registered on: 29/10/2024] Trial Registered Prospectively
Last Modified On: 24/10/2024
Post Graduate Thesis  Yes 
Type of Trial  Observational 
Type of Study   Cross Sectional Study 
Study Design  Other 
Public Title of Study   Diabetes risk detection using CT abdomen scans 
Scientific Title of Study   API module to Potential Prediction of Diabetes Risk through AI-Enhanced Assessment of Pancreatic Density and Visceral Fat on Computed Tomography Scans, Correlating with Glycated Haemoglobin Levels 
Trial Acronym  NIL 
Secondary IDs if Any  
Secondary ID  Identifier 
NIL  NIL 
 
Details of Principal Investigator or overall Trial Coordinator (multi-center study)  
Name  Ajina Sam 
Designation  Post graduate 
Affiliation  Saveetha medical college and hospital, Saveetha institute of medical and technical sciences. 
Address  Room no.50, Department of radiology, Saveetha medical college and hospital, Saveetha institute of medical and technical sciences, Saveetha nagar, Thandala, Chennai, India

Chennai
TAMIL NADU
602105
India 
Phone  9994673827  
Fax    
Email  ajinasam20@gmail.com  
 
Details of Contact Person
Scientific Query  
Name  Praveen Sharma K 
Designation  Professor 
Affiliation  Saveetha medical college and hospital, Saveetha institute of medical and technical sciences. 
Address  Room no. 50, Department of Radiology, Saveetha medical college and hospital, Saveetha institute of medical and technical sciences, Saveetha nagar, Thandala, Chennai, India

Chennai
TAMIL NADU
602105
India 
Phone  9962335288  
Fax    
Email  kpraveensharma.kps@gmail.com  
 
Details of Contact Person
Public Query  
Name  Ajina Sam W 
Designation  Post graduate 
Affiliation  Saveetha medical college and hospital, Saveetha institute of medical and technical sciences. 
Address  Room no.50, Department of radiology, Saveetha medical college and hospital, Saveetha institute of medical and technical sciences, Saveetha nagar, Thandala, Chennai, India

Chennai
TAMIL NADU
602105
India 
Phone  9994673827  
Fax    
Email  ajinasam20@gmail.com  
 
Source of Monetary or Material Support  
Saveetha Medical College Hospital, Saveetha Nagar, Thandalam, Chennai-602105 
 
Primary Sponsor  
Name  Dr. Ajina Sam 
Address  Saveetha Medical College Hospital, Saveetha Nagar, Thandalam, Chennai-602105  
Type of Sponsor  Other [SELF] 
 
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 Ajina Sam  Saveetha Medical College Hospital  Room no 50, Department of Radiology, Saveetha Medical College Hospital, Saveetha Nagar, Thandalam, Chennai.
Chennai
TAMIL NADU 		 9994673827

ajinasam20@gmail.com 
 
Details of Ethics Committee  
No of Ethics Committees= 1  
Name of Committee  Approval Status 
Saveetha Medical College and 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: E119||Type 2 diabetes mellitus without complications,  
 
Intervention / Comparator Agent  
Type  Name  Details 
Intervention  nil  nil 
Comparator Agent  nil  nil 
 
Inclusion Criteria  
Age From  15.00 Year(s)
Age To  90.00 Year(s)
Gender  Both 
Details  1.Population Criteria: Individuals with varying degrees of risk for diabetes. Patients who have undergone CT scans for abdominal imaging. Patients with available glycated haemoglobin (HbA1c) levels, including normal, prediabetic, and diabetic ranges.
2.Medical History: Patients with medical records indicating a history of diabetes or prediabetes. Patients with no prior history of diabetes to evaluate predictive capabilities.
3.CT Scan Data: High-quality CT scan images with clear visualization of pancreatic structures and visceral fat.
4.Ethnicity and Demographics: Consideration of diverse ethnic and demographic backgrounds to ensure the generalizability of the predictive model.
 
 
ExclusionCriteria 
Details  1.Pregnancy: Excluding pregnant individuals to avoid radiation exposure.
2.Presence of Other Chronic Diseases: Excluding individuals with chronic diseases (e.g., cancer) that may confound the relationship between pancreatic density, visceral fat, and diabetes risk.
3.Recent Major Surgery: Excluding individuals who have undergone recent major surgery, particularly those involving the pancreas or abdominal organs, as this can affect pancreatic density and visceral fat distribution.
4.Unreliable CT Scan Quality: Excluding individuals with CT scans of poor quality or artifacts that may compromise accurate assessment of pancreatic density and visceral fat.
5.Inability to Provide Relevant Laboratory Data: Excluding individuals who cannot provide laboratory data, which is essential for correlating CT findings with glycated hemoglobin levels and diabetes risk. 
 
Method of Generating Random Sequence   Computer generated randomization 
Method of Concealment   An Open list of random numbers 
Blinding/Masking   Participant and Investigator Blinded 
Primary Outcome  
Outcome  TimePoints 
To predict the risk of diabetes mellitus using AI-driven analysis of pancreatic density and visceral fat from CT scans  24 hours 
 
Secondary Outcome  
Outcome  TimePoints 
To assess the accuracy & clinical utility of the AI model by correlating the predictions with glycated hemoglobin (HbA1c) levels  1 week 
 
Target Sample Size   Total Sample Size="100"
Sample Size from India="100" 
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)   05/11/2024 
Date of Study Completion (India) Applicable only for Completed/Terminated trials 
Date of First Enrollment (Global)  05/11/2024 
Date of Study Completion (Global) Applicable only for Completed/Terminated trials 
Estimated Duration of Trial   Years="1"
Months="6"
Days="0" 
Recruitment Status of Trial (Global)   Not Yet Recruiting 
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 - YES
  1. What data in particular will be shared?
    Response - Individual participant data that underlie the results reported in this article, after de-identification (text, tables, figures, and appendices).

  2. What additional supporting information will be shared?
    Response -  Study Protocol
    Response -  Statistical Analysis Plan

  3. Who will be able to view these files?
    Response - Researchers whose proposed use of the data has been approved by an independent review committee identified for this purpose.

  4. For what types of analyses will this data be available?
    Response - To achieve aims in the approved proposal.

  5. By what mechanism will data be made available?
    Response (Others) -  Link to be provided upon request

  6. For how long will this data be available start date provided 30-10-2024 and end date provided 31-03-2025?
    Response - Beginning 3 months and ending 5 years following article publication.

  7. Any URL or additional information regarding plan/policy for sharing IPD? 
    Additional Information - NIL
Brief Summary  

  An innovative API module designed to predict the risk of diabetes mellitus through the analysis of pancreatic density and visceral fat area extracted from CT abdomen scans. The module employs advanced artificial intelligence algorithms to assess these parameters, which are crucial indicators of diabetes risk.

Pancreatic density is quantified in Hounsfield Units (HU), while visceral fat area is measured at the level of the umbilicus in square centimeters (cm²) from CT scans. These measurements are then compared to established normal ranges. The correlation between pancreatic density, visceral fat area, and the risk of diabetes mellitus has been extensively studied and validated in medical literature.

The API module utilizes machine learning algorithms trained on large datasets to accurately interpret and analyze CT scan images. By correlating pancreatic density and visceral fat area with known risk factors for diabetes mellitus, the module generates a binary prediction: either a positive indication for diabetes risk ("yes") or a negative indication ("no").

This invention aims to provide healthcare professionals with a reliable and efficient tool for early diabetes risk assessment, enabling timely intervention and personalized patient care. The scalability and accessibility of the API module ensure its seamless integration into existing healthcare systems, facilitating widespread adoption in clinical settings.

By harnessing the power of AI and advanced imaging techniques, this patent application represents a significant advancement in diabetes risk assessment, ultimately contributing to improved patient outcomes and healthcare management.

 
Close