Obesity prediction from structural MRI using conformal deep learning with uncertainty quantification

Traditional obesity prediction studies often rely on magnetic resonance imaging (MRI) voxel-based morphometry to correlate BMI with obesity-related clinical measurements and brain structure, predominantly grey matter volume (GMV). However, these studies often show inconsistencies in regional GMV findings among obese patients, making it challenging to establish a clear relationship between obesity and brain structure. To address these limitations, we propose a computational model to predict obesity directly from individual T1-weighted structural MRI data. The proposed conformal deep learning model achieves a 5-fold cross validated average precision of 77.65% and an F1-score of 75.42%, effectively predicting the probabilistic outcome of obesity from structural MRI. Furthermore, our model provides probabilistic uncertainty quantification paired with gradient-based localization maps that discover key brain regions, such as lobes and white matter tracts for obesity prediction.