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Updated: Sep 15, 2025

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深度学习用于检测具有恶性潜力的结肠多:使用功能增强的光学连贯断层扫描 (OCT) 图像进行ex vivo分类

Christos Photiou¹, Andrew Thrapp², Guillermo Tearney²

¹KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus.

Biomedical optics express

|July 18, 2025

概括

这项研究通过使用光学连贯断层扫描 (OCT) 来检测癌前多来加强结直肠癌 (CRC) 查. 新的框架提高了诊断准确度,有助于早期发现癌症.

科学领域:

生物医学光学生物医学光学
医学成像医学成像
癌症研究癌症研究

背景情况:

结肠直肠癌 (CRC) 是全球癌症死亡的主要原因.
通过结肠镜早期检测对于预防CRC至关重要.
光学连贯性断层扫描 (OCT) 显示了改善结肠镜查效率和成本的前景.

研究的目的:

开发一个新的框架来从OCT图像中提取先进的生物标志物,用于结肠多.
提高检测结肠癌前变化的效果.
提高OCT在CRC查中的诊断准确度.

主要方法:

从外体图像中提取新特征 (强度,分数统计,光谱特征,散射器大小).
从提取的生物标志物生成功能增强图像.
将功能增强和强度图像集成到使用决策级融合的深度学习分类模型中.

主要成果:

深度学习模型实现了88.3%的准确性,93.5%的灵敏度和77.9%的特异性.
在区分良性和恶性潜在的息肉时,获得了0.857的曲线下面面积 (AUC).
这种方法成功地将正常/高可塑性息肉与腺瘤/状状腺瘤区分开来.

结论:

新的框架显著提高了OCT检测癌前结肠病变的能力.
这种方法有可能改善OCT在结直肠癌查结果中的作用.
集成先进的生物标志物和深度学习为早期CRC检测提供了一个有希望的工具.

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