Predicting Reduction Mammaplasty Total Resection Weight With Machine Learning.

IF 1.4 4区 医学 Q3 SURGERY
Annals of Plastic Surgery Pub Date : 2024-08-01 Epub Date: 2024-06-04 DOI:10.1097/SAP.0000000000004016
Michelle Y Seu, Nikki Rezania, Carolyn E Murray, Mark T Qiao, Sydney Arnold, Charalampos Siotos, Jennifer Ferraro, Hossein E Jazayeri, Keith Hood, Deana Shenaq, George Kokosis
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引用次数: 0

Abstract

Background: Machine learning (ML) is a form of artificial intelligence that has been used to create better predictive models in medicine. Using ML algorithms, we sought to create a predictive model for breast resection weight based on anthropometric measurements.

Methods: We analyzed 237 patients (474 individual breasts) who underwent reduction mammoplasty at our institution. Anthropometric variables included body surface area (BSA), body mass index, sternal notch-to-nipple (SN-N), and nipple-to-inframammary fold values. Four different ML algorithms (linear regression, ridge regression, support vector regression, and random forest regression) either including or excluding the Schnur Scale prediction for the same data were trained and tested on their ability to recognize the relationship between the anthropometric variables and total resection weights. Resection weight prediction accuracy for each model and the Schnur scale alone were evaluated based on using mean absolute error (MAE).

Results: In our cohort, mean age was 40.36 years. Most patients (71.61%) were African American. Mean BSA was 2.0 m 2 , mean body mass index was 33.045 kg/m 2 , mean SN-N was 35.0 cm, and mean nipple-to-inframammary fold was 16.0 cm. Mean SN-N was found to have the greatest variable importance. All 4 models made resection weight predictions with MAE lower than that of the Schnur Scale alone in both the training and testing datasets. Overall, the random forest regression model without Schnur scale weight had the lowest MAE at 186.20.

Conclusion: Our ML resection weight prediction model represents an accurate and promising alternative to the Schnur Scale in the setting of reduction mammaplasty consultations.

利用机器学习预测乳房缩小成形术全切除重量
背景:机器学习(ML)是一种人工智能,已被用于创建更好的医学预测模型。利用 ML 算法,我们试图根据人体测量数据创建一个乳房切除重量预测模型:我们分析了在本院接受乳房缩小整形术的 237 名患者(474 个乳房)。人体测量变量包括体表面积(BSA)、体重指数、胸骨切迹对乳头(SN-N)和乳头对乳房褶皱值。针对相同的数据,对四种不同的 ML 算法(线性回归、脊回归、支持向量回归和随机森林回归)(包括或不包括 Schnur Scale 预测)进行了训练,并测试了它们识别人体测量变量与总切除重量之间关系的能力。根据平均绝对误差(MAE)评估了每个模型和单独使用 Schnur 量表预测切除体重的准确性:在我们的队列中,平均年龄为 40.36 岁。大多数患者(71.61%)为非洲裔美国人。平均 BSA 为 2.0 平方米,平均体重指数为 33.045 千克/平方米,平均 SN-N 为 35.0 厘米,平均乳头至乳房褶皱为 16.0 厘米。平均 SN-N 被认为是最重要的变量。在训练数据集和测试数据集中,所有 4 个模型预测的切除体重的 MAE 都低于单独使用施纳量表预测的 MAE。总体而言,不使用施纳量表权重的随机森林回归模型的 MAE 最低,为 186.20:我们的 ML 切除重量预测模型在乳房缩小成形术咨询中是一种准确而有前途的 Schnur 量表替代方法。
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来源期刊
CiteScore
2.70
自引率
13.30%
发文量
584
审稿时长
6 months
期刊介绍: The only independent journal devoted to general plastic and reconstructive surgery, Annals of Plastic Surgery serves as a forum for current scientific and clinical advances in the field and a sounding board for ideas and perspectives on its future. The journal publishes peer-reviewed original articles, brief communications, case reports, and notes in all areas of interest to the practicing plastic surgeon. There are also historical and current reviews, descriptions of surgical technique, and lively editorials and letters to the editor.
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