基于深度学习的检测视网膜脱落与眼睛超声波图像中的玻璃体出血
Naoki Toyama1, Takako Hidaka1, Hiroki Tamura2
1Department of Ophthalmology, Faculty of Medicine, University of Miyazaki, 5200 Kihara, Kiyotake, Miyazaki, 889-1692, Japan.
Scientific reports
|February 11, 2026
概括
一个新的深度学习算法在眼睛超声波图像上准确检测视网膜脱落 (RD) 和玻璃体出血 (VH). 这种人工智能工具有助于诊断RD,即使VH的视图模糊,也可能保留视力.
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:
- 视网膜脱落 (RD) 是一种严重的眼睛疾病,导致视力丧失.
- 诊断RD是 fundus-obscuring玻璃体出血 (VH) 的挑战,即使是超声波.
- 眼部超声波对于可视化后部眼部疾病至关重要.
研究的目的:
- 开发和验证用于检测RD和VH的深度学习模型.
- 为了提高RD检测在VH复杂的情况下的准确性.
- 为眼科医生使用B扫描超声波图像创建支持性诊断工具.
主要方法:
- 使用基于YOLOv5架构的卷积神经网络 (CNN).
- 该模型在2,188张图像上进行了训练,并在1,042张图像上进行了验证.
- 图像增强技术,包括不清晰的掩饰 (UM),用于提高性能.
主要成果:
- 深度学习模型实现了高检测准确度:96.6%的RD,99.2%的VH,和98.0%的RD与VH.
- 五次交叉验证提高了模型的稳定性和准确性.
- 该算法在B扫描超声波上识别RD和VH方面表现出显著的有效性.
结论:
- 开发的深度学习算法准确地检测眼睛超声波图像上的RD和VH.
- 这种人工智能工具有望成为R&D的支持性诊断辅助工具,特别是在存在VH时.
- 这项技术可以改善严重眼部疾病的早期检测和管理,并可能预防视力丧失.
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