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两级深度神经网络用于通过体内共聚焦显微镜图像诊断真菌角膜炎

Chun-Peng Li^1,2, Weiwei Dai³, Yun-Peng Xiao¹

¹Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.

Scientific reports

|August 8, 2024

概括

一个新的深度学习框架有助于使用体内共聚焦显微镜 (IVCM) 图像来诊断真菌角膜炎 (FK). 这种人工智能工具实现了高精度,协助眼科医生预防视力丧失.

科学领域:

眼科医生眼科眼科
医疗成像医学成像
人工智能的人工智能

背景情况:

性角膜炎 (FK) 诊断对于预防视力损失至关重要.
在体内共聚焦显微镜 (IVCM) 是FK的关键诊断工具.
当前的诊断方法可能耗时,需要专家解释.

研究的目的:

开发和评估用于使用IVCM图像自动化FK诊断的深度学习框架.
提高FK诊断的准确性和效率.
为眼科医生提供决策支持.

主要方法:

设计了一个两阶段的深度学习架构,集成图像级和序列级信息.
该框架在最大的公开数据集上进行了培训和验证,其中包括96,632张专家标记的IVCM图像.
性能在一个独立的测试组上进行了评估.

主要成果:

深度学习框架为FK实现了高诊断准确度.
在看不见的试验组中,特异性达到96.65%,灵敏度达到97.57%.
人工智能的表现与经验丰富的眼科医生的表现相当或超过.

结论:

开发的深度学习框架显示了帮助眼科医生诊断真菌角膜炎的重大前景.
人工智能提供多层次的诊断建议 (图像,序列,患者),增强临床决策.
这项技术有可能通过及时和准确的FK诊断来改善患者的治疗结果.

关键词:

菌的角质炎是一种真菌性角质炎.图像的分类图像的分类.神经网络的神经网络的神经网络变压器变压器变压器

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