ArticleJCI insight2021
Detection of diabetes from whole-body MRI using deep learning.
Article in JCI insight, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Prediction models for progression from prediabetes to diabetes: a systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Explainable AI-based analysis of human pancreas sections identifies traits of type 2 diabetes.Nature communications · 2026Article
- Elevated Cholinesterase Activity and the Metabolic Syndrome-Dissecting Fatty Liver, Insulin Resistance and Dysglycaemia.Liver international : official journal of the International Association for the Study of the Liver · 2025Article
- Accuracy of anthropometric parameters in predicting prediabetes among adolescents in Eastern Sudan: a community-based cross-sectional study.BMC endocrine disorders · 2025Article
- Risk of new-onset diabetes and efficacy of pharmacological weight loss therapy.Diabetes, obesity & metabolism · 2024Article
- Deep learning for the prediction of type 2 diabetes mellitus from neck-to-knee Dixon MRI in the UK biobank.Heliyon · 2023Article
- Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs.Nature communications · 2023Article
- Machine Learning as a Support for the Diagnosis of Type 2 Diabetes.International journal of molecular sciences · 2023Article
- Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review.Diabetology & metabolic syndrome · 2022Review
Corrections and comments
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Authors and funding
16 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Obesity is one of the main drivers of type 2 diabetes, but it is 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 that could additionally contribute to the disease. We used machine learning with dense convolutional neural networks to detect diabetes-related variables from 2371 T1-weighted whole-body MRI data sets. 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 with those of conventional models. The area under the receiver operating characteristic curve was 87% for the type 2 diabetes discrimination and 68% for prediabetes, both superior to conventional models. Mean absolute regression errors were comparable to those of 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.Our results show that diabetes is detectable from whole-body MRI without additional data. Our technique of heatmap visualization identifies plausible anatomical regions and highlights the leading role of fat accumulation in the lower abdomen in diabetes pathogenesis.
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