自动结石识别:基于尿液和血液常规分析的自适应性特征加权LSTM模型
Quanjing Zhu1, Patrick Cheong-Iao Pang2, Canhui Chen3
1Department of Laboratory Medicine, West China Hospital, Sichuan University, Guoxue Lane, Wuhou District, Chengdu, 610041, China.
Urolithiasis
|October 14, 2024
概括
一个新的深度学习模型,KS-LSTM,使用常规的血液和尿液测试准确地识别结石. 这种工具有助于临床医生早期检测石,改善患者的治疗结果.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 结石是普遍存在的尿道系统疾病,需要早期检测.
- 当前的诊断方法可能并不总是能够及时识别.
- 深度学习为增强诊断能力提供了潜力.
研究的目的:
- 开发和验证深度学习 (DL) 模型,用于早期结石识别.
- 用例行尿液和血液测试指数用于模型训练.
- 将DL模型的性能与其他诊断方法进行比较.
主要方法:
- 对1130名结石患者和1230名健康对照者的回顾性分析.
- 基于长期短期记忆 (LSTM) 的自适应特征加权模型 (KS-LSTM) 的开发.
- 模型评估使用准确度和接收器操作特征曲线 (AUC) 下的面积.
主要成果:
- 该KS-LSTM模型实现了89.5%的准确性和0.95.9的AUC.
- 发现的关键预测因素包括尿液白细胞,隐性血液,蛋白质和微细胞百分比.
- 与其他车型相比,KS-LSTM车型表现出了卓越的性能.
结论:
- 当通过KS-LSTM模型分析时,常规的尿液和血液检测可以准确地检测结石.
- 该KS-LSTM模型为早期结石诊断提供了宝贵的帮助.
- 这种人工智能驱动的方法可以支持科的临床决策.
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