Artificial Intelligence to Improve Cardiometabolic Risk Evaluation Using CT Scans

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

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: 1. 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 2. 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.