https://orcid.org/0000-0001-9225-3256
Abstract
Breast reduction is a common procedure with growing rates in the United States of America, aimed at alleviating the physical and psychological burdens of macromastia. Despite high success rates, it carries a risk of complications, with incidence rates ranging from 6.2% to 43%.
The authors developed a machine learning model using gradient-boosting decision trees to predict severe breast reduction complications up to 30 days following surgery requiring inpatient care.
This retrospective study included 322 cases of breast reduction surgery performed at the Tel Aviv Medical Center from 2017 to 2024. Model performance was evaluated using 5-fold cross-validation, and key metrics such as area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were reported. An interpretability tool was also created to visualize complication risks based on clinical features.
Severe complications occurred in 7.4% of cases. Key predictive factors included specimen weight, SN-N distance, and liposuction volume. The model achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.83, with an accuracy of 0.93 and a negative predictive value (NPV) of 0.95. The interpretability tool clearly visualized complication risks, aiding preoperative counseling.
This is the first study to use artificial intelligence (AI) to predict severe complications in breast reduction surgery. In this study, the AI model, with an AUC-ROC of 0.83 and NPV of 0.95, offers a reliable tool for surgical planning and patient education. Further validation across diverse populations is recommended to confirm its clinical utility.
