拉曼差异光谱和U-Net卷积神经网络用于皮肤神经纤维瘤的分子分析
Levi Matthies1,2, Hendrik Amir-Kabirian1, Medhanie T Gebrekidan3
1Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
PloS one
|April 11, 2024
概括
这项研究引入了拉曼光谱学和人工智能,用于对神经纤维瘤1型神经纤维瘤 (NF1) 的皮肤神经纤维瘤的客观实时诊断. 这种非侵入性光学技术有助于精确的手术切除,改善患者的治疗结果.
科学领域:
- 生物医学光学 生物医学光学
- 人工智能在医学中的应用
- 在瘤学瘤学.
背景情况:
- 在神经纤维素瘤类型1 (NF1) 中的皮肤神经纤维瘤会导致显著的美学,疼痛和功能问题.
- 目前的手术切除依赖于主观边缘评估,导致不完全切除和复发.
- 需要客观的实时诊断工具来指导外科切除和改善患者护理.
研究的目的:
- 引入转移刺激拉曼光谱 (SERS) 与深度神经网络 (DNN) 结合,以客观,实时诊断皮肤神经纤维瘤.
- 评估这种技术在分辨神经纤维瘤和相邻的健康组织中的有效性.
- 在NF1患者中为指导性外科切除提供基础.
主要方法:
- 34个外科病理样本的活体分析 (9个皮肤神经纤维瘤,25个生理组织).
- 移动刺激拉曼光谱 (SERS) 的应用用于光谱数据采集.
- 深度神经网络 (U-Net) 用于光谱处理,结合主要组件和线性差异分析进行分类.
主要成果:
- 在区分皮肤神经纤维瘤与生理组织方面具有高的诊断性能.
- 获得了100%的灵敏度,97.3%的特异性和97.6%的整体分类准确度.
- 成功地将光谱峰与它们各自的分子起源分配,验证了该技术.
结论:
- 带有人工智能的转移刺激拉曼光谱 (SERS) 为诊断皮肤神经纤维瘤提供了一种有前途的非侵入性方法.
- 这种光学技术有可能显著改善NF1的诊断和手术治疗.
- 客观的实时反可以指导外科医生,最大限度地减少残留瘤和复发率.
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