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通过机器学习和人工智能彻底改变唾液生物标志物

Jiahui Sun¹, Yuqing Jia², Xiaoxuan Wang³

¹Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, Wuhan, China.

Clinica chimica acta; international journal of clinical chemistry

|December 27, 2025

概括

使用人工智能 (AI) 和机器学习 (ML) 的唾液分析显示出早期疾病检测的前景. 这种方法整合了多个学科的数据来识别生物标志物,提高了广泛健康查的诊断能力.

科学领域:

生物标志物的发现和诊断.
计算生物学和生物信息学
翻译医学是一种翻译医学.

背景情况:

慢性疾病对全球健康构成重大挑战,需要早期检测策略.
唾液提供了一个非侵入性的,可访问的生物流体替代传统诊断方法.
多omics技术已经确定了各种疾病的许多唾液生物标志物.

研究的目的:

从2010年到2025年,审查人工智能支持的唾液诊断方面的进展.
总结了主要的唾液生物标记物类别和使用的机器学习算法.
评估人工智能辅助的唾液分析对早期疾病查的潜力.

主要方法:

对人工智能和唾液诊断研究的系统审查.
分析多omics数据 (蛋白质组学,基因组学,代谢组学等). ) 的情况.
机器学习算法用于生物标志物模式提取和分类的评估.

主要成果:

人工智能和机器学习增强了复杂,高维的唾液多组数据的分析.
已确定的主要唾液生物标志物包括蛋白质,核酸,代谢物,外体和微生物特征.
机器学习显著提高了使用唾液生物标志物检测疾病的分类性能.

结论:

人工智能支持的唾液诊断显示了提高早期疾病检测的巨大潜力.
与ML集成的唾液分析可以作为大规模预防性医疗保健的可访问工具.
这种方法对资源有限或高风险人群特别有价值.

关键词:

人工智能的人工智能是人工智能.生物标志物生物标志物机器学习机器学习这是唾液的唾液.

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