The study notes that even a single CT image from the lower back can reveal critical details about these markers, potentially predicting events like cardiovascular disease and overall mortality. The study examined how well these CT-derived markers could predict Type 2 diabetes and other related health issues. Researchers analyzed data from more than 32,000 South Korean adults who had previously undergone health screenings, including positron emission tomography (PET)/CT scans.
At the start of the study, 6 percent of participants had diabetes, and 9 percent developed it during the follow-up period. The automated CT markers were effective at predicting new diabetes cases, with accuracy scores of 0.68 for men and 0.
82 for women (where 1.0 indicates perfect accuracy). The amount of visceral fat around the abdominal organs was the best predictor of diabetes.
When combined with measures of muscle size, liver fat levels, and calcium buildup in the arteries, the predictions became even more accurate. Developed by a multi-institutional team, the AI model flagged elevated diabetes risk years before diagnosis by analyzing more than 270,000 x-rays and electronic health records, focusing on fatty tissue location. Validated on nearly 10,000 additional patients, the authors explained that this approach offers a cost-effective method for early detection.
One significant obstacle is securing reimbursement that reflects the actual value of these screenings. Without proper compensation, integrat.