实现结膜性高血症的自动评估:一种半监督的人工智能方法
Damon Wong1,2,3, Yvonne Ng1, Leila Sara Eppenberger1
1Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Annals of the New York Academy of Sciences
|August 6, 2025
概括
这项研究引入了一种自动化方法,用于分级结膜性高血症,使用裂纹灯图像上的半监督学习. 该方法准确地估计了结节血管密度,与临床评估有很强的一致性.
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:
- 结膜性高血症是一种临床征兆,通常是使用主观尺度手动分级.
- 需要客观和自动化的方法来分类结膜性高血压,以提高一致性和效率.
研究的目的:
- 开发和验证一种自动化的方法,用于从裂纹灯图像中分类结膜炎高血症.
- 评估半监督学习在分割结膜血管和估计其密度方面的表现.
主要方法:
- 使用两处地点的裂灯图像进行了回顾性研究.
- 半监督学习用于结膜和血管细分,具有有限的标记数据.
- 结节血管密度的估计和与手动Efron分级的比较.
主要成果:
- 半监督模型显著改善了对完全监督模型的细分 (p < 0.001).
- 估计的结膜血管密度与地面真实性 (0.86) 和临床分级 (0.83和0.80) 有很高的相关性.
- 自动化方法与临床分级的一致性与评级者之间的可靠性相当.
结论:
- 半监督学习可以通过血管密度估计准确,自动分级结膜性高血症.
- 这种方法为客观和可重复的结膜高血症评估提供了潜在的机会.
- 该方法有望被整合到临床工作流程中,以评估眼睛状况.
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