Artificial Intelligence to Improve Cardiometabolic Risk Evaluation Using CT Scans

This multicentre prospective study will evaluate the ability of the FatHealth technology to correctly identify individuals with pre-diabetes and diabetes, validating the technology against the current gold-standard diagnostic method, oral glucose tolerance testing.

Participants will be individuals who have undergone a CT scan of the chest (coronary CT angiogram [CCTA] or CT chest) as part of observational cohort studies.

Participants will be invited for an oral glucose tolerance test (OGTT), which is the current gold-standard method for detecting pre-diabetes and diabetes mellitus. All patients must have an evaluable OGTT. The study population will include:

• Approximately 90 individuals who had a CCTA as part of their clinical care (45 identified as having an abnormal FatHealth algorithm calculation and 45 with a normal FatHealth algorithm calculation) will undergo OGTT which is evaluable; and
• Approximately 90 individuals who had a chest CT as part of their clinical care (45 identified as having an abnormal FatHealth algorithm calculation and 45 with a normal FatHealth algorithm calculation) will undergo OGTT which is evaluable.

Inclusion criteria

• Participant is willing and able to give informed consent for participation in the study. Male or Female, aged 18 to 80 years.
• Body mass index (BMI) ≥ 25kg/m2
• FatHealth status assessed as the following:

• Elevated FatHealth status (50% of participants)
• Non-elevated FatHealth status (50% of participants)

Exclusion criteria

• Participant is unable or unwilling to give informed consent
• Participant is unable to understand English language
• Confirmed diagnosis of diabetes mellitus treated with oral medication or Insulin