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

Introduction: 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.

Methods: 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.

Results: 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.

Discussion: 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.

Level Of Evidence Iv: 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 .