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使用机器学习辅助对比增强超声波和临床特征的糖尿病病的可靠生物标志物

Lin Lin^1,2, Lin Yan¹, Nan Li¹

¹Department of Ultrasound, The First Medical Center, Chinese PLA General Hospital, 28 Fuxing Rd, Beijing, 100853, China.

Clinical and experimental medicine

|October 31, 2025

概括

机器学习有效地区分糖尿病病 (DN) 与非糖尿病病 (NDRD),使用对比增强超声波 (CEUS) 和临床数据. 这种人工智能辅助的方法提供了一个快速,可靠的工具,用于查糖尿病患者的损伤.

科学领域:

腎臟病學 (nephrology) 是一種醫學專業.
放射学放射学是一门学科.
人工智能的人工智能

背景情况:

糖尿病损伤诊断可能具有挑战性.
区分糖尿病病 (DN) 与非糖尿病病 (NDRD) 对于适当的管理至关重要.
早期检测需要非侵入性生物标志物.

研究的目的:

在糖尿病损伤患者中应用机器学习来区分DN和NDRD.
选对比增强超声波 (CEUS) 参数和临床特征的诊断实用性.
开发一个人工智能辅助的工具,用于DN的非侵入性查.

主要方法:

对120名患有慢性病的糖尿病患者 (240名脏) 的临床和CEUS数据的分析.
采用随机森林方法,用于模型开发的十倍交叉验证.
使用接收器操作特征 (ROC) 曲线来评估诊断性能.

主要成果:

综合随机森林模型在区分损伤类型方面实现了87.6%的平均分类准确度.
对ROC曲线的分析显示,曲线下的面积 (AUC) 值为0.918.8.
确定了CEUS参数和临床特征作为关键指标.

结论:

整合CEUS参数和临床特征的机器学习模型可以作为识别DN的非侵入性生物标志物.
人工智能辅助的CEUS与特定的临床特征相结合,为2型糖尿病患者的DN查提供了一种快速可靠的方法.
这种方法有助于区分糖尿病患者损伤类型.

关键词:

生物标志物生物标志物临床特征临床特征与对比度增强的超声波.糖尿病脏病糖尿病脏病

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