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Deep Neural Networks for Image-Based Dietary Assessment

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Predicting Body Mass Index From Structural MRI Brain Images Using a Deep Convolutional Neural Network.

Pál Vakli¹, Regina J Deák-Meszlényi¹, Tibor Auer²

¹Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, Hungary.

Frontiers in Neuroinformatics

|April 9, 2020

Summary

Deep learning accurately predicts body mass index (BMI) using structural brain scans. This neuroimaging approach reveals brain regions linked to BMI, offering new insights into body weight regulation.

Keywords:

amygdala body mass index caudate nucleus convolutional neural networks deep learning magnetic resonance imaging

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Area of Science:

Neuroimaging
Deep Learning
Human Physiology

Background:

Deep learning (DL) is increasingly utilized in cognitive and clinical neuroimaging.
DL models efficiently classify brain disorders and predict neurodegenerative disease risk.

Purpose of the Study:

To investigate the prediction of body mass index (BMI) using structural brain imaging and DL.
To explore the relationship between brain structure and individual BMI variations.

Main Methods:

A deep convolutional neural network (CNN) was employed to predict BMI.
The CNN utilized structural magnetic resonance imaging (MRI) brain scans, age, and sex as input data.
Localization maps were generated to identify brain regions contributing to BMI prediction.

Main Results:

Individual BMI was accurately predicted from single structural MRI scans using DL.
Key brain structures, including the caudate nucleus and amygdala, were identified as significant predictors of BMI.
CNN-based visualization provided complementary insights compared to standard brain segmentation methods.

Conclusions:

Predicting BMI from structural brain scans using DL is a promising approach.
This method can help investigate the link between brain morphology and body weight.
It opens new avenues for exploring the clinical utility of brain-predicted BMI.