使用 fundus 摄影预测功能:混因子的作用
Hyun-Woong Park1, Hae Ri Kim2,3, Ki Yup Nam4
1Department of Cardiology, Chungnam National University Sejong Hospital, Sejong, Korea.
The Korean journal of internal medicine
|March 19, 2025
概括
视网膜底部照片可以使用人工智能来查功能. CKD-EPI公式显示了比MDRD更好的准确性,尽管性别可能是预测中的混因素.
科学领域:
- 眼科医生 眼科 眼科
- 腎臟病學 (nephrology) 是一種醫學.
- 人工智能的人工智能
背景情况:
- 脏和视网膜有共同的病理; 脏疾病 (脏病) 通常与视网膜病变有关.
- 之前的研究表明, fundus 照片可以预测估计的淋巴细胞过率 (eGFR).
研究的目的:
- 用 fundus 照片评估 MDRD 和 CKD-EPI 公式用于 eGFR 预测的性能.
- 评估深度神经网络在非侵入性功能查中的实用性.
主要方法:
- 使用了45108名患者的数据集,并配对了 fundus 照片和肌素测量.
- 采用EfficientNet B3模型来预测来自 fundus图像的年龄,性别和肌水平.
- 使用MDRD和CKD-EPI两种公式计算eGFR.
主要成果:
- 深度神经网络准确地预测患者的年龄和性别从 fundus 图像.
- 性别被确定为影响肌素预测的混变量,MDRD更容易受到影响.
- 在eGFR预测中,CKD-EPI公式的表现优于MDRD公式 (AUC为0.864对比0.802).
结论:
- 通过深度神经网络分析的 fundus 摄影,为功能查提供了一种有价值的非侵入性方法.
- 建议使用CKD-EPI公式,而不是MDRD,以获得更可靠的eGFR估计,因为性相关混的敏感性较低.
- 承认性别的影响是人工智能驱动的诊断模型中的混因素.
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