Using Machine Learning to Select Breast Implant Volume

Abstract

Background: In breast augmentation surgery, the selection of the appropriate breast implant size is a crucial step that can greatly impact patient satisfaction and the outcome of the procedure. However, this decision is often based on the subjective judgment of the surgeon and the patient, which can lead to suboptimal results. In this study, we aimed to develop a machine-learning approach that can accurately predict the size of breast implants selected for breast augmentation surgery. Methods: We collected data on patient demographics, medical history, and surgeon preferences from a sample of 1000 consecutive patients who underwent breast augmentation. This information was used to train and test a supervised machine learning model to predict the size of breast implants. Results: Our study demonstrated the effectiveness of the algorithm in predicting breast implant size, achieving a Pearson’s correlation coefficient of 0.9335 (p <0.001). The model generated accurate predictions in 86% of the instances, with a Mean Absolute Error (MAE) of 27.10 ml. Its effectiveness was confirmed in the reoperation group, in which 36 of 57 (63%) patients would have received a more suitable implant size if the model's suggestion were followed, potentially avoiding reoperation. Conclusions: Our findings show that machine learning can accurately predict the chosen size of breast implants in augmentation surgery. By integrating our AI model into a decision support system for breast augmentation surgery, essential guidance can be provided to both the surgeons and patients. This not only streamlines the implant selection process but also facilitates enhanced communication and decision-making, ultimately leading to more reliable outcomes and improved patient satisfaction.