A Machine Learning Method for Nipple-Areola Complex Localization for Chest Masculinization Surgery

Abstract

Appropriately positioning the Nipple-Areola Complex (NAC) during chest masculinization surgery is a principle determinant of the aesthetic success of the procedure. Nonetheless, today, this positioning process relies on the subjective judgement of the surgeon. Therefore, this paper proposes a novel machine learning solution that leverages Artificial Neural Networks (ANNs) for estimating the NAC location on the chest wall. A dataset composed of 173 pictures of male subjects of various ages and body types was used. The ANN was fed a set of features inputs based on distance ratios between features of the upper body that are common between both biological sexes (e.g. umbilicus, anterior axillary fold, suprasternal notch). Using the proposed ANN regressive model, we achieved a Root Mean Square Error (RMSE) of 0.0617 for the ratio of distances from the suprasternal notch to the center between the NACs, and from the latter point to the umbilicus. Furthermore, an RMSE of 0.0560 for the ratio of the distances between the NACs and from the anterior axillary fold to the umbilicus was obtained. Our results demonstrate that machine learning can be used to support the surgeon in the localization of the NAC for chest masculinization surgery.