预测5年内2型糖尿病 (T2DM) 出现的风险,使用乳房图像:放射学和深度学习算法之间的比较研究
Nishta Letchumanan1, Shouhei Hanaoka2, Tomomi Takenaga2
1The University of Tokyo, Department of Radiology, Graduate School of Medicine, Tokyo, Japan.
Journal of medical imaging (Bellingham, Wash.)
|January 8, 2025
概括
对乳房影像的放射学分析显示,有可能预测2型糖尿病 (T2DM) 风险. 这种方法在预测T2DM在五年内发展方面超过了深度学习 (CNN).
科学领域:
- 医疗成像医学成像
- 医疗保健中的机器学习
- 糖尿病研究研究 糖尿病研究
背景情况:
- 全球范围内,2型糖尿病 (T2DM) 的患病率正在上升.
- 早期预测T2DM对于及时干预和管理至关重要.
- 通常用于乳腺癌查的乳房显微镜正在探索新的应用.
研究的目的:
- 用乳房图像预测5年内2型糖尿病 (T2DM) 的发生情况.
- 将基于放射学的机器学习方法与深度学习卷积神经网络 (CNN) 的性能进行比较.
主要方法:
- 一种放射学方法从乳房图像中提取了668个特征,并使用机器学习算法进行分析.
- 一个卷积神经网络 (CNN) 与修改的ResNet架构被应用于原始的乳房图像.
- 这两种方法都采用嵌套,分层的五倍交叉验证和超参数调整来评估性能.
主要成果:
- 使用光梯度增强模型的放射学方法,实现了68.9%的精度,30.7%的灵敏度,89.5%的特异性和0.63 AUROC.
- 在CNN方法取得的AUROC为0.58.
- 与CNN (0.58) 相比,放射学显示出更高的AUROC (0.63).
结论:
- 与CNN方法相比,基于放射学的方法在预测T2DM发生方面表现优越.
- 放射学特征的解释性和临床相关性可能有助于其更好的性能.
- 乳房镜像对包括T2DM在内的非传统疾病风险预测有潜力.
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