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使用卷积神经网络进行客观和可重现的角膜共聚焦显微镜图像选择的自动化程序

Qincheng Qiao^1,2, Wen Xue^1,3, Jinzhe Li³

¹Department of Endocrinology and Metabolism, Qilu Hospital, Shandong University, Jinan, China.

|March 19, 2025

概括

一个用于角膜共聚焦显微镜 (CCM) 图像的自动深度学习算法显著改善了糖尿病外围神经病变 (DPN) 的诊断. 这种人工智能工具提高了识别DPN的准确性和效率,降低了诸如脚等风险.

科学领域:

眼科医生眼科眼科
神经学神经学
医疗成像医学成像

背景情况:

糖尿病周围神经病变 (DPN) 是一种常见的糖尿病并发症,增加了脚和截肢的风险.
角膜共聚焦显微镜 (CCM) 通过分析角膜神经形态来非侵入性地评估DPN.
选择高质量,代表性的CCM图像用于DPN诊断是一个重大挑战.

研究的目的:

开发和验证一个完全自动化的算法来选择代表性的CCM图像用于DPN诊断.
利用深度学习和无监督聚类来实现客观和可重现的图像选择.
使用CCM提高DPN评估的诊断效率和准确性.

主要方法:

开发了一个使用ResNet-18进行特征提取的深度学习算法.
使用无监督集群来识别具有代表性的CCM图像,平衡非冗余性和代表性.
该算法的性能被不同专业知识的研究人员对手动图像选择进行了验证.

主要成果:

与手动选择相比,自动化算法在区分DPN方面表现出卓越的性能.
它显著减少了观察者之间的变化,提高了诊断效率,在不到1秒的时间内分析图像.
对于角膜神经纤维密度,长度和分支密度等关键形态参数,可以实现更高的诊断准确度.

结论:

拟议的自动化CCM图像选择算法为DPN诊断提供了强大的和高效的工具.
开源算法与标准的CCM工作流程兼容,有利于研究人员和临床医生.
建议进行多中心研究,以验证在不同人群中算法的性能.

关键词:

深度学习是一种深度学习.同焦点显微镜的共焦显微镜.糖尿病神经病变糖尿病神经病变

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