Health Condition 1: E119- Type 2 diabetes mellitus without complications
Conditions
Interventions
Intervention1: nil: nil
Control Intervention1: nil: nil
Sponsors
Dr. Ajina Sam
Eligibility
Inclusion criteria
Inclusion criteria: 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.
Exclusion criteria
Exclusion criteria: 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.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| To predict the risk of diabetes mellitus using AI-driven analysis of pancreatic density and visceral fat from CT scansTimepoint: 24 hours | — |
Secondary
| Measure | Time frame |
|---|---|
| To assess the accuracy & clinical utility of the AI model by correlating the predictions with glycated hemoglobin (HbA1c) levelsTimepoint: 1 week | — |
Countries
India
Contacts
Public ContactAjina Sam W
Saveetha medical college and hospital, Saveetha institute of medical and technical sciences.
Outcome results
None listed