Artificial intelligence and diabetes: A detailed analysis of mortality risks

The medical sector is undergoing a radical transformation thanks to rapid advancements in information technology. Artificial intelligence is no longer just abstract algorithms or theoretical concepts, but has become a practical clinical tool that enhances doctors' ability to make critical decisions. In this context, scientists at the Institute of Aging Biology at Lobachevsky University in Russia have achieved a remarkable medical breakthrough by developing an advanced, interpretable AI-based model designed to predict mortality risks in diabetic patients with unprecedented accuracy.

Extensive study and accurate results

This scientific innovation was based on an in-depth research study that included meticulous monitoring of the vital signs of more than 550 diabetic patients over a long period of up to 17 years. Mikhail Ivanchenko, director of the Institute of Aging Biology Research, explained that the true value of this research lies not only in its predictive power but also in its "interpretable nature." The new tool allows physicians to understand the underlying reasons for the predictions by analyzing the complex interrelationships between dozens of health parameters specific to each patient.

Personal risk maps that go beyond traditional analysis

The results showed that factors such as patient age, disease duration, and the number of accumulated complications are the strongest risk indicators. What distinguishes this technological approach is its ability to create a "personalized risk map" for each individual patient. For example, the system can demonstrate that a high risk of death (e.g., 68%) in a specific patient is primarily due to a combination of elevated creatinine, advanced age, and the presence of four specific diabetic complications, thus shifting treatment from general to highly personalized.

Hidden biological markers revealed by artificial intelligence

The research highlighted the importance of biological markers that were not previously given sufficient focus in routine assessment, most notably:

• Sodium brain peptide hormone (NT-proBNP): which acts as a mirror reflecting the hidden stress that the heart muscle may be exposed to.
• Creatinine levels: which are a vital indicator of kidney function.
• The structure of N-glycans in blood serum: which has been identified as an important biomarker for immune regulation processes and biological aging.

Global context and the importance of innovation

This development comes at a time when the world is facing increasing health challenges, with rising rates of diabetes globally placing a heavy burden on healthcare systems. The significance of this Russian model lies in its ability to shift medicine from a reactive, symptomatic approach to a proactive and preventative one. By detecting hidden risks early, physicians can intervene before the condition worsens, potentially reducing mortality rates and improving the quality of life for millions of patients worldwide.