通过知识丰富的视觉语言模型,提高罕见和常见 fundus 疾病的诊断准确性
Meng Wang1,2, Tian Lin3, Aidi Lin3
1Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Nature communications
|July 1, 2025
概括
新的视觉语言模型RetiZero在超过34万张 fundus图像和400种疾病上受过训练. 它在诊断眼睛疾病方面表现出卓越的表现,即使是罕见的眼睛疾病,在某些测试中表现优于人类专家.
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:
- 现有的 fundus 图像模型对疾病的了解有限.
- 需要先进的人工智能来诊断各种眼睛疾病.
研究的目的:
- 介绍RetiZero,这是一个新的视觉语言模型用于 fundus 图像分析.
- 结合了对400多种 fundus 疾病的广泛知识.
- 评估RetiZero在各种诊断和检索任务上的表现.
主要方法:
- 预先训练的RetiZero在341,896个 fundus图像上使用文本描述.
- 利用跨多个种族和国家的多样化数据集.
- 在零射击疾病识别,图像检索和临床诊断协助方面的评估表现.
主要成果:
- 在零射击识别中实现了0.843 (15种疾病) 和0.756 (52种疾病) 的Top-5精度.
- 在图像对图像检索任务中得分为0.950和0.886.
- 超过眼科医生的平均准确度在Top-3零射击性能中,特别是在罕见疾病中.
结论:
- RetiZero在底部图像分析方面表现出了卓越的能力.
- 该模型有助于诊断常见和罕见的 fundus 疾病.
- RetiZero有可能被整合到临床环境中,以改善眼病诊断.
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