唾液和机器学习的ATR-FTIR光谱作为Sjögren病的查测试
Jhonatan Contreras1,2, Bhavik Vyas3, Oleg Ryabchykov1,2
1Photonic Data Science Department, Leibniz Institute of Photonic Technology (IPHT), Albert-Einstein-Straße 9, Jena 07745, Germany.
Analytical chemistry
|November 18, 2025
概括
这项研究引入了一种人工智能驱动的光谱方法来诊断Sjögren.
科学领域:
- 免疫学 免疫学 免疫学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 斯约格伦病是一种自身免疫性疾病,会影响外分泌腺,导致眼睛和口腔干燥.
- 目前的诊断方法面临的挑战是由于非特异性症状,缺乏生物标志物和侵入性.
- 早期和准确的诊断对于管理Sjögren病和预防并发症至关重要.
研究的目的:
- 评估减弱总反射率里埃变换红外光谱 (ATR-FTIR) 与AI结合用于Sjögren病诊断的疗效.
- 开发一种不确定性意识的机器学习模型,以可靠和自信地识别疾病.
- 探索Sjögren病的非侵入性诊断方法.
主要方法:
- 使用ATR-FTIR光谱分析生物样本.
- 开发了一个神经网络模型,用于不确定性估计,使用蒙特卡洛脱落.
- 雇佣了分层的交叉验证和针对女性参与者的专注培训,以确保可靠和公正的结果.
主要成果:
- 不确定性意识模型在频谱层面实现了75%的准确性和73%的灵敏性.
- 在患者层面,该模型显示了79%的准确性和76%的灵敏度.
- 不确定性估计有助于改进培训数据并提高模型性能.
结论:
- ATR-FTIR光谱学与一种不确定性感知机器学习模型相结合,对Sjögren病的诊断有很大的希望.
- 这种非侵入性方法为早期检测提供了可靠的工具,有可能改善患者的治疗结果.
- 自信意识的解释增强了基于AI的诊断工具的临床实用性.
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