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Artificial Intelligence to Improve Cardiometabolic Risk Evaluation Using CT Scans

NCT05058690 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 180

Last updated 2025-02-13

No results posted yet for this study

Summary

To validate the ability of the FatHealth algorithm to identify individuals with pre-diabetes and with type 2 diabetes mellitus

Conditions

Interventions

DIAGNOSTIC_TEST

Oral Glucose Tolerance Test

* Obtain blood sample for glucose assessment (time "0" sample). This may be obtained via venepuncture or after cannula insertion. * Test a small sample using a near patient glucose testing meter. If the result on the glucose meter is greater than or equal to 11mmol/L, send the blood sample urgently to lab. If it is confirmed by biochemistry to be above 11mmol/L, there is no need to continue test. * If the result is less than 11mmol/L on meter, give the patient the glucose solution to drink. * Collect a further blood sample at 120 minutes. * Send samples all together to laboratory for glucose measurement.

Sponsors & Collaborators

  • University of Oxford

    collaborator OTHER

  • University of Leeds

    collaborator OTHER

  • Milton Keynes University Hospital NHS Foundation Trust

    collaborator OTHER_GOV

  • Caristo Diagnostics Limited

    lead INDUSTRY

Study Design

Allocation
NON_RANDOMIZED
Purpose
DIAGNOSTIC
Masking
SINGLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-02-15
Primary Completion
2025-02-28
Completion
2025-02-28

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05058690 on ClinicalTrials.gov