使用自发拉曼和连贯抗斯托克斯拉曼光谱与人工智能的宫癌诊断模型
Chenyang Liu1, Caifeng Xiu2, Yongfang Zou3
1The Department of Gynecology, Obstetrics and Gynecology Center, The First Hospital of Jilin University, Changchun 130000, China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|October 31, 2024
概括
一致的抗斯托克斯拉曼光谱 (CARS) 为检测子宫癌提供了一种快速,客观的方法. 汽车成像与机器学习相结合,在分类癌细胞组织方面实现了100%的准确性,提供了一种新的诊断方法.
科学领域:
- 生物医学光学 生物医学光学
- 频谱学是一种光谱学.
- 癌症的诊断 癌症的诊断
背景情况:
- 宫癌是全球重要的健康问题.
- 目前的组织病理学诊断是耗时且主观的.
- 需要快速,客观,非破坏性的诊断方法.
研究的目的:
- 为了研究高波数自发拉曼光谱和连贯抗斯托克斯拉曼光谱 (CARS) 用于宫癌检测.
- 为了确定CARS成像和宫病变之间的关系.
- 开发一种机器学习模型来分类宫组织类型.
主要方法:
- 高波数自发拉曼光谱法用于分析宫组织中的脂质,脂肪酸和蛋白质.
- 使用一致的抗斯托克斯拉曼光谱法 (CARS) 来增强信号强度和灵敏度.
- 一个ConvNeXt网络 (机器学习模型) 使用CARS图像进行组织分类的训练.
主要成果:
- 与正常组织相比,宫癌组织的脂质,脂肪酸和蛋白质含量较高.
- 汽车成像显示,癌细胞组织 (质化和非质化) 的强度明显高于正常细胞.
- ConvNeXt网络在从CARS图像中分类组织类型时实现了100%的验证准确性.
结论:
- 汽车成像提供了一种快速,客观和非破坏性的宫癌诊断方法.
- 汽车图像可以被视觉解释,而质珍珠可以帮助识别癌症的亚型.
- 开发的基于CARS的机器学习模型提供了高效和准确的宫癌诊断.
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