外部等离子传感的进步：设备集成策略和AI辅助诊断
Advances in exosome plasmonic sensing: Device integration strategies and AI-aided diagnosis
影响因子:10.50000
分区:化学1区 Top / 生物物理1区 生物工程与应用微生物1区 分析化学1区 电化学2区
发表日期:2024 Dec 15
作者:
Xiangyujie Lin, Jiaheng Zhu, Jiaqing Shen, Youyu Zhang, Jinfeng Zhu
摘要
作为下一代生物标志物，外泌体在跟踪癌症进展方面具有巨大的潜力。他们在癌症诊断中面临许多检测限制。血浆生物传感器由于无标签，实时和高敏感性而引起了外部检测的最前沿的关注。它们在最小液体样品的多重免疫测定中的优势在各种诊断研究中确立了领先地位。这篇综述描述了等离激元传感技术的应用原理，强调了基于外泌体的光谱和图像信号在疾病诊断中的重要性。它还引入了小型化外泌体等离激元生物传感平台的进步，这可以促进未来医疗保健的护理点测试。如今，受到科学技术的人工智能激增（AI）的启发，正在采用越来越多的AI算法来处理等离子检测的外部谱和图像数据。使用机器学习的代表性算法已成为外泌体液体活检的等离子生物传感研究的主流趋势。通常，这些算法有效地处理复杂的外泌体数据集并为精确诊断建立强大的预测模型。这篇综述进一步讨论了基于外部的诊断中AI算法选择的关键策略。特别是，我们将AI算法分为外泌体等离子体检测应用的可解释和不可解释的组。可解释的AI通过阐明决策过程提高了诊断的透明度和可靠性，而无法解释的AI则通过“ Black-Box”工作模式进行了强大的数据处理，提供了高诊断精度。我们认为，在不久的将来，AI将继续促进外部等离子体检测和移动医疗保健的重大进展。
Abstract
Exosomes, as next-generation biomarkers, has great potential in tracking cancer progression. They face many detection limitations in cancer diagnosis. Plasmonic biosensors have attracted considerable attention at the forefront of exosome detection, due to their label-free, real-time, and high-sensitivity features. Their advantages in multiplex immunoassays of minimal liquid samples establish the leading position in various diagnostic studies. This review delineates the application principles of plasmonic sensing technologies, highlighting the importance of exosomes-based spectrum and image signals in disease diagnostics. It also introduces advancements in miniaturizing plasmonic biosensing platforms of exosomes, which can facilitate point-of-care testing for future healthcare. Nowadays, inspired by the surge of artificial intelligence (AI) for science and technology, more and more AI algorithms are being adopted to process the exosome spectrum and image data from plasmonic detection. Using representative algorithms of machine learning has become a mainstream trend in plasmonic biosensing research for exosome liquid biopsy. Typically, these algorithms process complex exosome datasets efficiently and establish powerful predictive models for precise diagnosis. This review further discusses critical strategies of AI algorithm selection in exosome-based diagnosis. Particularly, we categorize the AI algorithms into the interpretable and uninterpretable groups for exosome plasmonic detection applications. The interpretable AI enhances the transparency and reliability of diagnosis by elucidating the decision-making process, while the uninterpretable AI provides high diagnostic accuracy with robust data processing by a "black-box" working mode. We believe that AI will continue to promote significant progress of exosome plasmonic detection and mobile healthcare in the near future.