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

  • Original Article
  • Breast Surgery
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Aesthetic Plastic Surgery Aims and scope Submit manuscript

Abstract

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.

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Funding

Dr. Montemurro is a plastic surgeon in private practice at Akademikliniken, Stockholm, Sweden The authors declared no potential conflicts of interest with respect to the research, authorship, and publication of this article. The authors received no financial support for the research, authorship, and publication of this article.

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Authors and Affiliations

  1. Akademikliniken, Storängsvägen 10, 11541, Stockholm, Sweden

    Paolo Montemurro & Christoph Wallner

  2. Department of Plastic Surgery, BG University Hospital Bergmannsheil, Ruhr University Bochum, Bürkle-de-la-Camp Platz 1, 44789, Bochum, Germany

    Marcus Lehnhardt, Björn Behr & Christoph Wallner

Authors
  1. Paolo Montemurro
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  2. Marcus Lehnhardt
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  3. Björn Behr
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  4. Christoph Wallner
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Corresponding author

Correspondence to Paolo Montemurro.

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The authors declare that they have no conflicts of interest to disclose.

Human and Animals Rights

This article does not contain any studies with human participants or animals performed by any of the authors. For this type of study informed consent is not required.

Informed Consent

All patients were counselled in accordance with the Declaration of Helsinki guidelines and written informed consent was obtained preoperatively.

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Montemurro, P., Lehnhardt, M., Behr, B. et al. A Machine Learning Approach to Identify Previously Unconsidered Causes for Complications in Aesthetic Breast Augmentation. Aesth Plast Surg 46, 2669–2676 (2022). https://doi.org/10.1007/s00266-022-02997-2

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  • DOI: https://doi.org/10.1007/s00266-022-02997-2

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