应用基于多层Y0L0v8神经网络的深度学习算法来识别真菌角膜炎
A V Sitnova1, E R Valitov2, S N Svetozarskiy3
1Clinical Resident, Department of Eye Diseases; The S. Fyodorov Eye Microsurgery Federal State Institution, 59a Beskudnikovsky Blvd., Moscow, 127486, Russia.
Sovremennye tekhnologii v meditsine
|January 30, 2025
概括
深度学习算法在从眼睛图像中诊断真菌角膜炎方面表现有前途,在准确性方面超过眼科医生. 这种计算机视觉方法可以帮助临床决策和远程医疗检测真菌角膜炎.
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:
- 性角膜炎是一种严重的眼部感染,需要准确及时诊断.
- 当前的诊断方法可能耗时,可能需要专门的专业知识.
- 深度学习为诊断眼部疾病的自动图像分析提供了潜在的解决方案.
研究的目的:
- 开发和评估一种基于深度学习的方法,使用前段眼睛照片来诊断真菌角膜炎.
- 将深度学习模型的诊断性能 (灵敏度和特异性) 与执业眼科医生进行比较.
主要方法:
- 处理了274张前段图像 (130张真菌角膜炎,144张对照) 的数据集.
- 在预处理和注释图像上训练了YOLOv8卷积神经网络.
- 模型的性能在一个单独的测试组中进行了评估,并与眼科医生诊断的结果进行了比较.
主要成果:
- 深度学习模型实现了56.0%的灵敏度,96.1%的特异性和76.5%的整体准确性.
- 执业眼科医生实现了57.7%的灵敏度,41.7%的特异性和50.0%的准确性.
- 在这项研究中,深度学习模型与专家判断相比,显示出更高的准确性.
结论:
- 深度学习算法在真菌角膜炎诊断方面具有很高的潜力,在没有元数据的情况下超过了人类专家的准确性.
- 计算机视觉技术可以在复杂的病例和远程医疗环境中作为一种有价值的补充工具.
- 需要进一步的研究来完善模型,扩大数据集,并与替代诊断方法进行比较.
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