Whole-Body MRI Combined with Deep Learning Can Detect Type 2 Diabetes

The study by researchers at the University of Tübingen (Tübingen, Germany) used deep learning methods and data from more than 2000 MRIs to identify patients with (pre-) diabetes. Being overweight and having a lot of body fat increase the risk of diabetes. However, not every overweight person also develops the disease. The decisive factor is where the fat is stored in the body. If fat is stored under the skin, it is less harmful than fat in deeper areas of the abdomen (known as visceral fat). How fat is distributed throughout the body can be easily visualized with whole-body MRI.

To detect such patterns of body fat distribution, the researchers used artificial intelligence (AI). They trained deep learning (machine learning) networks with whole-body MRI scans of 2,000 people who had also undergone screening with the oral glucose tolerance test (OGTT). The OGTT can screen for impaired glucose metabolism and diagnose diabetes. This is how the AI learned to detect diabetes. Further additional analysis also showed that a proportion of people with prediabetes, as well as people with a diabetes subtype that can lead to kidney disease, can also be identified via MRI scans. The researchers are now working to decipher the biological regulation of body fat distribution. One goal is to identify the causes of diabetes through new methods such as the use of AI in order to find better preventive and therapeutic options.

"We have now investigated whether type 2 diabetes could also be diagnosed on the basis of certain patterns of body fat distribution using MRI," said Prof. Robert Wagner, explaining the researchers' approach. "An analysis of the model results showed that fat accumulation in the lower abdomen plays a crucial role in diabetes detection."

University of Tübingen