预测HFA 30-2 视觉场与深度学习从多模式的OCT-Fundus特征融合和结构功能不一致性分析的深度学习
İlknur Tuncer Fırat1, Murat Fırat2, Haci Erbali1
1Faculty of Medicine, Inonu University, Ophthalmology, Malatya, Türkiye.
Journal of imaging informatics in medicine
|January 20, 2026
概括
这项研究表明,人工智能可以从眼部扫描中预测视觉现场测试结果,从而改善青光眼的诊断. 当结构和功能措施对齐时,预测更准确,突出了结构功能一致的重要性.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
背景情况:
- 玻璃眼是不可逆转的视力丧失的主要原因.
- 视野 (VF) 测试评估功能损失,而光学连贯性断层扫描 (OCT) 和 fundus成像提供结构数据.
- VF测试可以是主观的,并表现出可变性,有时显示结构功能不一致 (SFD).
研究的目的:
- 为了估计汉普里30-2视野测量 (平均偏差 (MD),模式标准偏差 (PSD) 和点wise值灵敏度 (TS)) 在青光眼/眼睛高血压 (OHT) 患者.
- 使用基于视觉变压器 (ViT-B/32) 的功能融合方法,将OCT和 fundus 图像结合起来.
- 分析SFD对预测准确性的影响.
主要方法:
- 使用ViT-B/32模型从光盘照片, fundus图像和视网膜神经纤维层 (RNFL) 地图中提取了视觉特征.
- 开发了一种多式人工智能模型,将视觉特征与人口和临床数据相结合.
- 采用全球VF指数 (MD,PSD) 的概率回归和位置感知网络进行点智的TS预测.
主要成果:
- 人工智能模型的平均绝对误差 (MAE) 为MD的2.26dB,PSD的1.42dB,平均TS的2.96dB.
- 除了患有SFD的眼睛,MAE的改善为1.82dB (MD),1.30dB (PSD) 和2.12dB (平均TS),比例增加至±2dB.
- 在临床一致的病例中,模型的性能明显更好.
结论:
- 基于ViT-B/32的深度特征融合准确地从多式联络结构图像中预测VF指标.
- 结构功能不一致会对预测可靠性产生负面影响.
- 在OCT-VF一致的情况下,AI预测更可靠,在解释结果时应考虑SFD.
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