性别特定的机器学习分类模型改善了腹腔大动脉动脉瘤的结果预测.
Katherine E Kerr1, Indrani Sen2, Pete H Gueldner1
1Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
Biology of sex differences
|November 11, 2025
概括
与一般模型相比,特定于性别的机器学习模型改善了腹腔大动脉瘤 (AAA) 结果预测. 均等的患者数据表示可以提高女性患者的准确性,而不会影响男性患者的结果.
科学领域:
- 心血管医学 心血管医学
- 医学成像分析 医学成像分析
- 医疗保健中的机器学习
背景情况:
- 腹腔大动脉瘤 (AAA) 是一种危及生命的疾病,破裂死亡率高.
- 女性患者比男性更频繁地经历AAA破裂,尽管患病率较低.
- 目前基于直径的手术干预标准存在局限性,因为一些动脉瘤在值以下破裂.
研究的目的:
- 研究特定性别机器学习 (ML) 模型对预测腹腔大动脉动脉瘤 (AAA) 患者预后的影响.
- 为了比较性别特定的ML模型与一般性,性别不可知模型的性能.
- 评估ML模型中样本大小均等对预测准确性的影响.
主要方法:
- 获得了来自537名患者的CT图像数据 (159名女性,378名男性).
- 利用临床,生物机械和形态特征进行ML模型训练.
- 经过培训和测试的一般,男性特异性和女性特异性ML模型,使用20%的持久设置.
主要成果:
- 女性特异性和男性特异性ML模型都表现出优异的预测性能,与各自性别的一般模型相比.
- 在男性和女性患者之间的样本大小均等,改善了女性的预测结果.
- 男患者的表现没有受到取样大小等同的负面影响.
结论:
- 性别特定的ML模型显示了改善AAA患者结果预测的显著前景.
- 通过性别特异模型和数据平衡,解决AAA数据集中女性患者的不足问题至关重要.
- 考虑数据集中的人口代表性对于减少医学ML应用中的偏见至关重要.
关键词:
人工智能可解释性腹腔大动脉动脉瘤 腹腔大动脉动脉瘤生物力学 生物力学机器学习 机器学习形态学 形态学 形态学结果预测结果预测.基于性别的差异.形状分析 形状分析压力分析 压力分析这是血管外科手术.更多相关视频
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