机器学习让皮肤组织光谱分辨更准，助力疾病早期诊断。
Machine Learning Applications to Diffuse Reflectance Spectroscopy in Optical Diagnosis; A Systematic Review
- 用机器学习处理漫反射光谱数据，自动识别组织类型
- 77项研究验证，模型诊断准确率高但需更多临床验证
- 适合医学影像、生物传感领域研究人员参考
漫反射光谱在区分生物组织方面表现出强大能力，但其宽频且平滑的信号特征难以靠人眼辨别，需依赖算法处理。近年来，机器学习模型在此任务中展现出高诊断准确率，催生了多种针对不同疾病和状况的应用方法。本系统综述遵循PRISMA指南，共检索并深入分析77项研究，总结当前技术进展，指出研究空白，并提出未来方向。结论认为，漫反射光谱与机器学习结合在临床组织分类中潜力巨大，但亟需更严谨的样本分层、在体验证及可解释性算法的发展。
原文摘要 · Abstract (English)
Diffuse Reflectance Spectroscopy has demonstrated a strong aptitude for identifying and differentiating biological tissues. However, the broadband and smooth nature of these signals require algorithmic processing, as they are often difficult for the human eye to distinguish. The implementation of machine learning models for this task has demonstrated high levels of diagnostic accuracies and led to a wide range of proposed methodologies for applications in various illnesses and conditions. In this systematic review, we summarise the state of the art of these applications, highlight current gaps in research and identify future directions. This review was conducted in accordance with the PRISMA guidelines. 77 studies were retrieved and in-depth analysis was conducted. It is concluded that diffuse reflectance spectroscopy and machine learning have strong potential for tissue differentiation in clinical applications, but more rigorous sample stratification in tandem with in-vivo validation and explainable algorithm development is required going forward.
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