在口腔细胞学中使用无标签的深紫外线显微镜:迈向无污点诊断的一步
Sumsum P Sunny1,2,3,4, Jiabin Chen1, Yihan Wang1
1James C. Wyant College of Optical Sciences, University of Arizona, Tucson AZ, USA.
Biomedical optics express
|August 14, 2025
概括
这项研究介绍了一种低成本,人工智能驱动的紫外线显微镜系统,用于早期检测口腔癌. 该技术分析未染色的细胞,为传统活检提供了更快,非侵入性的替代方案.
科学领域:
- 生物医学成像技术 生物医学成像技术
- 人工智能在医学中的应用
- 癌症的诊断 癌症的诊断
背景情况:
- 口腔癌的诊断依赖于侵入性活检和染色,限制了早期检测.
- 目前的方法耗时,可能不适合初级医疗保健机构.
- 对口腔病变需要快速,非侵入性和客观的诊断工具.
研究的目的:
- 开发和验证与人工智能 (AI) 集成的无标签深紫外 (UV) 显微镜系统.
- 允许对未染色的口腔细胞学标本进行自动化,客观的分析,以早期检测口腔癌.
- 评估系统在区分良性,潜在恶性和恶性口腔病变方面的有效性.
主要方法:
- 低成本,无标签的深紫外线显微镜系统被用来分析未染色的口腔细胞学样本.
- 一个深度学习的U-Net架构被用于核细分.
- 通过使用人工智能进行分类,提取和分析了关键的核形态特征.
主要成果:
- 紫外线显微镜系统在区分诊断组中实现了高灵敏度 (>80%) 和特异性 (>79%).
- 核特征的无监督聚类有效地分类了患者队列,表明了诊断潜力.
- 由人工智能驱动的分析提供了对细胞和核形态的客观见解.
结论:
- 集成AI的紫外线显微镜系统为早期口腔癌检测提供了一个有希望的,非侵入性的方法.
- 这项技术消除了染色的需要,缩短了加工时间,对环境的影响最小.
- 该系统适用于初级医疗保健机构,并有可能改善患者的治疗结果,等待进一步验证.
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