A Machine Learning Approach to Identify Previously Unconsidered Causes for Complications in Aesthetic Breast Augmentation.

Abstract

Primary breast augmentation is one of the most commonly requested aesthetic procedures. Considering the large number of procedures performed in connection with a high demand, it is crucial to prevent complications. For this reason, finding and avoiding possible sources of complications is decisive. Between January 2010 and December 2021, 1625 female patients underwent an aesthetic breast augmentation performed by a single surgeon. The data collected were analyzed through a machine learning technique for binary recursive partitioning. This made it possible to detect unknown sources of a complication and determine a vertex for the various features. When analyzing the data, for most features a high importance score with low entropy was achieved, concluding a high significance. In addition, reproducibility was demonstrated through detailed testing and training accuracies in the algorithm. With this procedure, in addition to known risks such as a high BMI and round implant shape, a larger than A preoperative bra-cup size (OR: 2.7) and a taller body could also be identified as most significant influencing factors for complications. Preoperative breast size plays an exceptionally important role in the occurrence of complications and should be a factor held in a surgeon's considerations. In addition, this study shows ways to transfer artificial intelligence into plastic surgery to increase medical quality. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .