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一般目的视觉语言模型和眼科基础模型在青光眼的检测和功能预测中的性能

Jalil Jalili^1,2, Justin Huynh^1,3, Evan Walker^1,2

¹Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.

Translational vision science & technology

|November 19, 2025

概括

视觉语言模型 (VLMs) 在检测眼和预测视野平均偏差时使用光学连贯断层扫描 (OCT) 扫描显示出高准确性. 这些先进的AI模型为可扩展的青光眼护理决策支持提供了潜在的潜力.

科学领域:

眼科和人工智能的人工智能
医学成像分析医学成像分析
计算机视觉在医疗保健中的应用

背景情况:

玻璃眼是全球不可逆转的失明的主要原因.
准确检测和监测青光眼的进展对于有效的管理至关重要.
光学连贯断层扫描 (OCT) 提供视神经头部和视网膜神经纤维层的高分辨率横截面图像.

研究的目的:

评估视觉语言模型 (VLMs) 对于青光眼检测的性能.
用OCT图像评估VLMs在预测视野 (VF) 平均偏差 (MD) 的能力.
为了比较微调的VLM与专门的基础模型和传统方法的有效性.

主要方法:

利用了来自DIGS和ADAGES研究的1025名参与者的27,610张SpectraLIS OCT图像.
精心调整的LLaVA,PaliGemma,RETFound和ResNet-50模型用于青光眼的分类和VF MD预测.
训练模型在OCT圆扫描中集中在视神经头部,使用AUC和MAE指标评估性能.

主要成果:

精心调整的LLaVA模型获得了最高的性能:青光眼分类的AUC为0.92和VF MD预测的MAE为1.79dB.
RETFound和PaliGemma也表现出强的表现,AUC分别为0.91和0.90.
冷视觉编码器导致精度降低;分层分析表明老年人和中度至高级玻璃眼病例的分类表现更好.

结论:

精心调整的VLM在青光眼检测和VF MD预测方面表现出很高的性能,与专业模型相比或超过.
这些通用AI模型可以有效地适应临床玻璃眼治疗.
VLM显示了利用OCT成像进行可扩展的决策支持系统的巨大潜力.

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