机器学习模型用于体积和体重估计,用于计划乳腺重建
Sheng-Pu Teo1, Mee-Hoong See2,3, Lee-Lee Lai4
1Faculty of Computing and Informatics, Multimedia University, Selangor, Malaysia.
Health information science and systems
|January 27, 2026
概括
机器学习使用患者数据准确估计乳房体积和体重,为重建规划提供了具有成本效益的替代方案. 这种方法简化了手术前评估,提高了临床实践中的可访问性和效率.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习应用 机器学习应用
- 医学成像和数据分析.
背景情况:
- 准确的乳房体积和体重估计对于乳房切除术后的重建至关重要.
- 目前的方法往往涉及高成本或复杂的程序.
- 为了解决这些局限性,开发了一个新的机器学习框架.
研究的目的:
- 开发和验证用于准确估计乳房体积和体重的机器学习框架.
- 为了利用人口统计和人体统计数据进行手术前乳腺评估.
- 为现有方法提供具有成本效益和可访问性的替代方案.
主要方法:
- 从199名患者 (2021-2023) 收集和预处理了数据.
- 功能选择利用领域专业知识,斯皮尔曼的等级相关性和Boruta算法.
- 线性回归,随机森林回归和支持矢量回归模型被训练并使用R2和Pearson的相关系数进行评估.
主要成果:
- 发现乳房体积/体重和患者特征 (如BMI,杯子大小和瘤严重程度) 之间存在显著的相关性.
- 最佳线性回归模型实现了乳房体积的R2为81.8%,乳房体重为72%.
- 该模型整合了领域专家和统计学选择的特征.
结论:
- 机器学习整合了人口和人体数据,提供了一个准确,可解释和可访问的手术前乳腺评估方法.
- 这种方法消除了成像成本和对专门设备的依赖,利用常规收集的临床数据.
- 拟议的模型为临床实践提供了一个实用,高效和具有成本效益的解决方案,克服了传统方法的局限性.
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