PuSH - Publication Server of Helmholtz Zentrum München: Detection of diabetes from whole-body MRI using deep learning.

Information

Clues to quality of journals

Open Access Policy of Helmholtz Association 2016

CC Licencences

Publication Metrics

Navigation

Home

Deutsch

EVA: Electronic Publishing

New EVA Application

Research

Advanced Search

Browse by ...

... HMGU-Authors/Consortia

... Organizational Structure

... Journal

... Publication Type

... Research Data

... Arbeitsgruppen

... Publication Year

Publication overview

Statistics

Statistics (last 5 years)

OA Publications

Publish

Submit Publication

Import publication from...

...EVA

Report missing publication

Highlights

Support & Contact

Contact persons

Help

Data protection

Helmholtz Open Science

JANE Journal Estimator

SHERPA/RoMEO

DOAJ

Export:

Text

Endnote (RIS) BIB

BibTeX

Dietz, B.* ; Machann, J. ; Agrawal, V.* ; Heni, M. ; Schwab, P.* ; Dienes, J.K.* ; Reichert, S.* ; Birkenfeld, A.L. ; Häring, H.-U. ; Schick, F. ; Stefan, N. ; Fritsche, A. ; Preissl, H. ; Schölkopf, B.* ; Bauer, S.* ; Wagner, R.

Detection of diabetes from whole-body MRI using deep learning.

JCI insight 6:e146999 (2021)

Publ. Version/Full Text

DOI

PMC

	Open Access Gold

Abstract
Metrics
Extra information

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.

Impact Factor

Scopus SNIP

Scopus
Cited By

Altmetric

8.315

1.308