Diabetes detection from whole-body magnetic resonance imaging using deep learning.

Abstract

HypothesisObesity is one of the main drivers of type 2 diabetes (T2D), but not uniformly associated with the disease. The location of fat accumulation is critical for metabolic health. Specific patterns of body fat distribution such as visceral fat, are closely related to insulin resistance. There might be further, hitherto unknown features of body fat distribution which could additionally contribute to the disease.MethodsWe used machine learning with dense convolutional neural networks (DCNN) to detect diabetes related variables from 2,371 T1-weighted whole-body magnetic resonance imaging (MRI) datasets. MRI was performed in participants undergoing metabolic screening with oral glucose tolerance tests. Models were trained for sex, age, BMI, insulin sensitivity, HbA1c and prediabetes or incident diabetes. The results were compared to conventional models.ResultsThe Area Under the Receiver Operator Characteristic curve was 87% for the T2D discrimination and 68% for prediabetes, both superior to conventional models. Mean absolute regression errors were comparable to conventional models. Heatmaps showed that lower visceral abdominal regions were critical in diabetes classification. Subphenotyping revealed a group with high future diabetes and microalbuminuria risk.InterpretationOur results show that diabetes is detectable from whole-body MRI without additional data. Our technique of heatmap visualization unravels plausible anatomical regions and highlights the leading role of fat accumulation in the lower abdomen in diabetes pathogenesis.