# Machine learning in the prediction of cardiac epicardial and mediastinal fat volumes

arXiv: https://arxiv.org/abs/2208.14374 · HTML: https://mlsanity.com/paper/2208.14374/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 30 references that are in the map.
- First posted on arXiv 30 Aug 2022.

## Builds on (most cited first, 1 of 1)

- [A novel approach for the automated segmentation and volume quantification of cardiac fats on computed tomography](https://mlsanity.com/paper/2112.11381/) (2021)

## Built on by (most cited first, 4 of 4)

- [Cardiac fat segmentation using computed tomography and an image-to-image conditional generative adversarial neural network](https://mlsanity.com/paper/2605.20064/) (2026)
- [Development of pericardial fat count images using a combination of three different deep-learning models](https://mlsanity.com/paper/2307.12316/) (2023)
- [X-Ray cardiac angiographic vessel segmentation based on pixel classification using machine learning and region growing](https://mlsanity.com/paper/2605.20073/) (2026)
- [Anatomy-Aware 3D Mesh Refinement of Pericardium Segmentations on Computed Tomography](https://mlsanity.com/paper/2607.19210/) (2026)

## Abstract (arXiv)

We propose a methodology to predict the cardiac epicardial and mediastinal fat volumes in computed tomography images using regression algorithms. The obtained results indicate that it is feasible to predict these fats with a high degree of correlation, thus alleviating the requirement for manual or automatic segmentation of both fat volumes. Instead, segmenting just one of them suffices, while the volume of the other may be predicted fairly precisely. The correlation coefficient obtained by the Rotation Forest algorithm using MLP Regressor for predicting the mediastinal fat based on the epicardial fat was 0.9876, with a relative absolute error of 14.4% and a root relative squared error of 15.7%. The best correlation coefficient obtained in the prediction of the epicardial fat based on the mediastinal was 0.9683 with a relative absolute error of 19.6% and a relative squared error of 24.9%. Moreover, we analysed the feasibility of using linear regressors, which provide an intuitive interpretation of the underlying approximations. In this case, the obtained correlation coefficient was 0.9534 for predicting the mediastinal fat based on the epicardial, with a relative absolute error of 31.6% and a root relative squared error of 30.1%. On the prediction of the epicardial fat based on the mediastinal fat, the correlation coefficient was 0.8531, with a relative absolute error of 50.43% and a root relative squared error of 52.06%. In summary, it is possible to speed up general medical analyses and some segmentation and quantification methods that are currently employed in the state-of-the-art by using this prediction approach, which consequently reduces costs and therefore enables preventive treatments that may lead to a reduction of health problems.