医学
人口统计学的
植入
乳房再造术
乳房植入物
乳房外科
机器学习
患者满意度
样本量测定
学习曲线
外科
隆胸
人工智能
医学物理学
乳腺癌
统计
计算机科学
内科学
人口学
数学
癌症
社会学
操作系统
作者
Filipe V. Basile,Thaís da Silva Oliveira
标识
DOI:10.1097/prs.0000000000011146
摘要
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.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.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.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.
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