在ADPKD患者的功能下降的自动预后使用深度学习
Anish Raj1, Fabian Tollens2, Anna Caroli3
1Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim 68167, Baden Württemberg, Germany; Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim 68167, Baden Württemberg, Germany.
Zeitschrift fur medizinische Physik
|August 23, 2023
概括
通过使用深度学习来预测功能下降,提高了自体主导多囊病 (ADPKD) 的准确预后. 这种AI方法增强了对ADPKD患者的监测和早期干预.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 在自身主导多囊性病 (ADPKD) 中,对功能下降的准确预后对于及时干预至关重要.
- 目前的预后生物标志物,如高度调整的体积 (HtTKV) 和估计的膜过率 (eGFR),具有局限性,包括手动测量挑战.
- 将自动成像功能与传统生物标志物的整合可以提高预后准确性.
研究的目的:
- 开发和验证用于自动体积细分和预测ADPKD功能下降的深度学习算法.
- 通过结合自动化图像分析来增强超越当前方法的预后能力.
主要方法:
- 开发了两种深度学习模型:一种自动体积细分CNN和一种使用细分体积,预测HtTKV,年龄和基线eGFR的预测模型.
- 该预测模型采用了卷积神经网络 (CNN) 和多层感知器 (MLP) 的组合.
- 该研究包括135名受试者,对预测CKD阶段 (>=3A,>=3B) 和8年内eGFR下降的验证.
主要成果:
- 实现了高曲线下面面积 (AUC) 评分:0.96对于CKD>=3A,0.96对于CKD>=3B,0.95对于30%的eGFR下降.
- 在预测和测量的eGFR下降之间发现了0.81的强皮尔森相关系数.
- 一个扩展模型在8年后预测了不同的CKD阶段,AUC为0.97.
结论:
- 建议的深度学习方法准确地预测ADPKD患者的功能下降.
- 自动体积细分和从MRI图像中提取特征显著提高了预后准确性.
- 这种人工智能驱动的方法提供了改善监测和预后的潜力,即使是在ADPKD早期阶段.
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