人工智能预测基于in vivo共聚焦激光内微镜成像的非增强性脑瘤恶性
Jiuxu Chen1,2, Yuan Xu2, Irakliy Abramov2
1School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States.
Frontiers in surgery
|January 21, 2026
概括
一个新的AI模型分析了共聚焦激光内微镜 (CLE) 图像序列,以准确地分类脑瘤等级. 这种基于序列的方法优于基于框架的模型,并与专家神经病理学家的表现相匹配.
科学领域:
- 神经外科 神经外科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 非增强性脑瘤可能是恶性,这对传统的MRI构成挑战.
- 同焦激光内分显微镜 (CLE) 在手术期间提供细胞分辨率成像,但解释是主观的.
- 目前用于CLE的AI模型忽视了时间上下文,与人类专家不同.
研究的目的:
- 开发和评估一种新的基于序列的深度学习模型,用于使用CLE图像对脑瘤等级进行分类.
- 在AI解释CLE数据时模仿专家神经病理学家的时间推理过程.
- 提高无增强性脑瘤的手术内评估的准确性和降低主观性.
主要方法:
- 基于序列的深度学习模型是使用来自16名患有无增强脑瘤的患者的CLE图像序列创建的.
- 使用预训练的骨干提取视觉特征,通过变压器编码器和时间卷积进行时间建模.
- 模型的性能与跨多个列车测试分割的基于框架的分类进行了比较.
主要成果:
- 基于序列的模型实现了高准确度,视觉变压器骨干达到93%,显著优于基于框架的模型.
- 诊断性能与专家神经病理学家解释 (87%) 相当.
- 该模型显示了对文物的稳定性,并通过结合时间进展来增强可解释性.
结论:
- 整合视觉和时间CLE数据的AI模型可以准确地分类脑瘤等级,与专家的性能竞争.
- 与基于框架的模型相比,这种基于序列的方法提供了更好的准确性和更少的主观性.
- 开发的人工智能系统显示了作为一个非增强脑瘤切除的手术内决策支持工具的前景.
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