一个基础人工智能模型的新型多式实现,使用光神经头部底部图像和OCT成像来检测青光眼
Benton Chuter1,2, Vedant Joshi1,2, Shahin Hallaj1,2
1Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, California.
Ophthalmology science
|February 3, 2026
概括
与CFP模型相比,多式RETFound人工智能模型使用彩色底部照片和OCT图像显示了更好的青光眼的检测. 然而,单模 OCT AI 模型可能足以用于临床应用.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 青光眼的检测依赖于眼睛成像的准确解释.
- 像RETFound这样的自我监督学习模型为自动疾病检测提供了潜在的潜力.
- 评估多式联络方法可以提高诊断性能.
研究的目的:
- 使用彩色底部照片 (CFPs) 和OCT图像,比较单模式和多模式的RETFound模型来检测青光眼.
- 评估这些模型在不同种族,年龄组和疾病严重程度的普遍性.
- 确定用于临床青光眼诊断的最佳AI实施方法.
主要方法:
- 开发了一个多式联网RETFound模型,使用配对的CFP和OCT图像.
- 将其性能与单模CFP-only和OCT-onlyRETFound模型进行了比较.
- 按种族,年龄和青光眼严重程度进行分层性能分析.
主要成果:
- 多式模式的AUC为0.94,超过了CFP单式模式 (AUC0.86) 的AUC,但不超过了OCT单式模式 (AUC0.93).
- 多模式和OCT单模式模型显示出比CFP单模式模型更高的精度和回忆.
- 所有的模型在检测中度至重度的玻璃眼方面都比轻度的玻璃眼表现更好.
结论:
- 多式联网RETFound模型比CFP单式联网模型提供了增强的诊断能力.
- 单模式的OCT人工智能模型表现出与多模式方法可比的性能.
- 单模式的OCTAI可能足够,并且更容易实现,用于临床青光眼的检测.
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