授权急性损伤3D基于石墨烯的传感器使用极端学习机器
Netnapa Sittihakote1, Pobporn Danvirutai2, Sirirat Anutrakulchai3,4
1Faculty of Engineering, Biomedical Engineering, Khon Kaen University, Nai Mueang 40002, Khon Kaen, Thailand.
ACS omega
|May 20, 2024
概括
这项研究将极端学习机器 (ELM) 与石墨烯电极相结合,用于增强脏监测. 人工智能模型显著提高了检测中性粒细胞凝酶相关性脂卡林 (NGAL) 预测急性损伤 (AKI) 的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 纳米材料是一种纳米材料.
背景情况:
- 早期发现急性损伤 (AKI) 对患者的治疗结果至关重要.
- 目前用于监测功能生物标志物的方法,如中性粒细胞凝酶相关的卡林 (NGAL),可以提高速度和准确性.
- 基于石墨烯的电化学传感器为敏感的生物标志物检测提供了潜力.
研究的目的:
- 开发和评估一个AI增强的电化学传感器系统,用于近乎实时的NGAL检测.
- 调查极端学习机器 (ELM) 的应用,以提高使用3D石墨烯电极的NGAL检测精度.
- 评估预测AKI的综合系统的性能.
主要方法:
- 用于NGAL捕获的3D石墨烯电极与利波卡林-2抗体功能化的制造.
- 应用极端学习机器 (ELM) 算法来分析尿液数据并预测NGAL水平.
- 对ELM与其他机器学习算法 (SVM,MLP,随机森林) 的比较分析.
主要成果:
- ELM集成导致NGAL确定曲线下的面积 (AUC) 增加了15%.
- 对NGAL的检测极限显著减少,从14.8降至0.89 ng/mL.
- 准确性,精度,灵敏度,特异性和AKI预测F1得分的准确性显著改善 (例如精度增加30.69%).
结论:
- 基于ELM和石墨烯的传感器的组合为准确的NGAL检测和AKI预测提供了一个非常有效的方法.
- 开发的系统展示了小型化,AI增强生物传感器的潜力,用于实际的临床应用.
- 电化学传感器提供了性能和资源利用的最佳平衡,以提高电化学传感器的能力.
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