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使用人工智能工具评估儿童慢性病进展的GDF15,EGF和Neopterin-A试点研究
Kinga Musiał1, Jakub Stojanowski2, Agnieszka Bargenda-Lange1
1Department of Pediatric Nephrology, Wrocław Medical University, Borowska 213, 50-556 Wrocław, Poland.
International journal of molecular sciences
|March 13, 2025
概括
人工智能可以准确预测儿童慢性病 (CKD) 的进展. 该模型使用表皮生长因子 (EGF),生长差异化因子15 (GDF15) 和新蛋白的血清水平来识别先进阶段.
科学领域:
- 儿科脏病学 儿科脏病学
- 生物标志物发现发现
- 人工智能在医学中的应用
背景情况:
- 儿童慢性病 (CKD) 的特点是细胞介导免疫和慢性炎症.
- 增长分化因子15 (GDF15) 表明炎症和压力,而表皮生长因子 (EGF) 则有助于管再生.
- 新素反映了细胞介导免疫,是激活的单细胞和巨细胞的产物.
研究的目的:
- 调查EGF,GDF15和neopterin在预测儿科患者中CKD进展中的作用.
- 开发和验证人工智能 (AI) 模型,以使用这些生物标志物评估CKD进展.
- 阐明炎症过程对儿科CKD中功能下降的贡献.
主要方法:
- 研究了一组151名患有CKD1-5期的儿童.
- 使用ELISA测量了EGF,GDF15和纽波特林的血清度.
- 一个人工神经网络 (ANN) 模型使用患者数据进行训练,包括人体测量,生化和生物标志物值.
主要成果:
- 最准确的AI模型包含了EGF,GDF15和质蛋白血清水平.
- 该模型在将患者分类为1-3或4-5的CKD阶段时,达到96.77%的高精度.
- 人工智能模型展示了对CKD进展的出色预测能力.
结论:
- 通过人工智能模型分析EGF,GDF15和新素的血清度,可以有效预测儿童的CKD进展情况.
- 这些发现强调了炎症在儿科CKD中功能下降中的关键作用.
- 这种人工智能驱动的方法为监测和管理儿科CKD提供了一个有希望的工具.
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