Frozen section biopsy, introduced in the early 1900s, still remains the gold standard methodology for rapid histologic evaluations. Although a valuable tool, it is labor-, time-, and cost-intensive. Other challenges include visual and diagnostic variability, which may complicate interpretation and potentially compromise the quality of clinical decisions. Raman spectroscopy, with its high specificity and non-invasive nature, can be an effective tool for dependable and quick histopathology. The most promising modality in this context is stimulated Raman histology (SRH), a label-free, non-linear optical process which generates conventional H&E-like images in short time frames. SRH overcomes limitations of conventional Raman scattering by leveraging the qualities of stimulated Raman scattering (SRS), wherein the energy gets transferred from a high-power pump beam to a probe beam, resulting in high-energy, high-intensity scattering. SRH’s high resolution and non-requirement of preprocessing steps make it particularly suitable when it comes to intrasurgical histology. Combining SRH with artificial intelligence (AI) can lead to greater precision and less reliance on manual interpretation, potentially easing the burden of the overburdened global histopathology workforce. We review the recent applications and advances in SRH and how it is tapping into AI to evolve as a revolutionary tool for rapid histologic analysis.
冰冻切片活检技术自20世纪初问世以来，始终是快速组织学评估的金标准方法。尽管该技术具有重要价值，但其操作流程需要大量人力、时间及经济成本。此外，视觉判读与诊断差异等挑战可能影响结果解读，进而潜在地影响临床决策质量。拉曼光谱技术凭借其高特异性和无创特性，有望成为可靠且快速的组织病理学检测工具。其中最具前景的模态是受激拉曼组织成像技术（SRH），这种无标记非线性光学过程可在短时间内生成类常规H&E染色图像。SRH通过利用受激拉曼散射特性克服了传统拉曼散射的局限——在受激拉曼散射过程中，能量从高功率泵浦光束转移至探测光束，从而产生高能量、高强度散射。SRH的高分辨率特性及无需预处理步骤的优势，使其特别适用于术中组织学检测。将SRH与人工智能技术相结合，可显著提升诊断精度并减少对人工判读的依赖，有望缓解全球组织病理学工作者超负荷的工作压力。本文综述了SRH技术的最新应用进展，并探讨其如何与人工智能技术融合，发展成为快速组织学分析领域的革命性工具。
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