Body fat predictions

                 Introduction

                    Machine learning is now used to create new measurements that help corelate 
body fat with cardiometabolic diseases.
                    The dataset used for the development of the convolutional neural networks (CNNs)
consists of MRI imaging data collected from 40,032 participants from UK.
                    MRI or magnetic resonance imaging is a noninvasive way to examine organs, tissues
and the skeletal system of a person. In short terms the procedure produces high-resolution
images of the inside of the body and this allows doctors to have a clearer view of
what is happening.
                    In order to train the CNNs, the specialists have split the data in two separated parts.
The first part which was used for training consisted in 9,041 samples and the
second part of the dataset (30,991) was used for testing.

                    The samples used for training were already quantified in different measurements, for
example:
            - visceral adipose tissue (VAT)
            - abdominal subcutaneous adipose tissue (ASAT)
            - gluteofemoral adipose tissue (GFAT)
                    After training, the CNNs were used to quantify the remaining data from the other participants.By doing this the scientists were able to derive new metrics which were fully independent
of BMI (Body Mass Index).
                    The new metrics are called:
            - VAT adjusted for BMI (VATadjBMI)
            - ASAT adjusted for BMI (ASATadjBMI)
            - GFAT adjusted for BMI (GFATadjBMI)

                    The results of the CNNs showed a near-perfect estimation for VAT, ASAT and GFAT.
                    By taking the presence of type 2 diabetes and associating it with the new metrics, 
the following results were obtained:
            - VATadjBMI showed a significantly increased risk with a OR/SD (odds ratio per standard deviation increase) of 1.49 and 95% CI (confidance interval)
            - ASATadjBMI was largely neutral with 1.08 OR/SD and 95% CI
            - GFATadjBMI conferred protection with 0.75 OR/SD and 95% CI

                    Bibliography

https://www.medrxiv.org/content/10.1101/2021.05.07.21256854v2