通过使用卷积神经网络,从原始Casia2体积数据预测ectasia查指数
Maziar Mirsalehi1, Benjamin Fassbind1, Andreas Streich2
1Department of Experimental Ophthalmology, Saarland University, Homburg, Germany.
PloS one
|September 2, 2025
概括
卷积神经网络使用原始光学连贯性断层扫描数据准确预测. EfficientNetB0模型取得了最高的准确性,超过了以前用于查的方法.
科学领域:
- 眼科 眼科
- 人工智能
- 医学成像
背景情况:
- 角膜是一种渐进的眼睛疾病,其特征是角膜变薄.
- 早期发现角质对于及时干预和管理至关重要.
- 来自Casia2仪器的ectasia查指数用于识别角,但基于原始数据的预测需要先进的方法.
研究的目的:
- 使用卷积神经网络 (CNN) 从原始光学连贯性断层扫描 (OCT) 数据中预测脱离性查指数.
- 评估不同的CNN架构 (ResNet18,DenseNet121,EfficientNetB0) 在预测角质方面的性能.
- 为了比较使用OCT原始数据与处理过的数据进行查的有效性.
主要方法:
- 三种CNN模型 (ResNet18,DenseNet121,EfficientNetB0) 被调整为从原始Casia2 OCT数据中预测查指数.
- 原始的3D OCT数据 (3dv格式) 被转换为每次眼睛检查中的16张图像.
- 模型的性能使用指数预测和准确性,灵敏度,特异性,正预测值和F1分数进行了评估.
主要成果:
- EfficientNetB0在预测ectasia查指数时获得了最低的平均绝对误差 (5. 86).
- 这三种CNN模型都表现出高分类性能:EfficientNetB0获得了95.83%的准确性和94.72%的F1分数.
- 这些模型成功地将扫描分类为Keratoconus或Not Keratoconus,具有高灵敏度和特异性.
结论:
- 通过使用CNN的原始CTD数据预测角质是有效的,并且优于使用处理数据的先前方法.
- 与其他模型和最先进的方法相比,经过调整的EfficientNetB0模型显示出更高的性能.
- 这种方法为自动化和准确的查提供了一个有前途的途径.
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