Deep learning imaging phenotype predicts cardiometabolic disorders #AIinMedicine

The content provided includes a list of references related to various topics such as diabetes mellitus, cardiovascular disease, metabolic syndrome, and neuroimaging biomarkers. These references cover studies on the pathogenesis and pathophysiology of diabetes, global estimates of diabetes prevalence, the role of risk factors in reducing the global burden of cardiovascular disease, and the relationship between metabolic syndrome and cardiovascular disease. Additionally, there are references on lifestyle modifications for metabolic syndrome, the association of metabolic syndrome with type 2 diabetes, and the clinical perspective of obesity, metabolic syndrome, and cardiovascular disease. Other references focus on neuroimaging biomarkers for Alzheimer’s disease, cardiovascular measures from abdominal MRI, and the prediction of liver cancer prognosis using deep learning models. The references also touch on topics like colorectal cancer surveillance, fatty liver disease, and the association of visceral obesity with metabolic changes and the risk of metabolic syndrome. These studies highlight the importance of understanding these conditions and using advanced technologies like deep learning for diagnosis, risk prediction, and treatment monitoring.

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Source link: https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-024-05163-1