用不同成像方法对地理缩自动细分的有效性进行比较
Maria Cristina Savastano1,2, Emanuele Crincoli3,4, Alfonso Savastano1,2
1Ophthalmology Unit, "Fondazione Policlinico Universitario A. Gemelli IRCCS", Rome, Italy.
Eye (London, England)
|April 12, 2025
概括
人工智能 (AI) 在使用基底自光 (FAF),近红外 (N-IR) 和光学连贯断层扫描血管学 (OCTA) 图像对地理缩 (GA) 的细分表现出了强的表现. 在基于AI的GA区域评估中,不建议使用逆模 (RM) 进行视网膜成像.
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:
- 地理缩 (GA) 是导致与年龄相关的黄斑变性视力丧失的主要原因.
- 精确测量GA对于监测疾病进展和评估治疗疗效至关重要.
- 目前的GA细分方法依赖于手动解释,这可能耗时且容易变化.
研究的目的:
- 为了比较通过底部自光 (FAF),近红外 (N-IR) 成像,逆转模 (RM) 成像和光学连贯性断层扫描血管造影 (OCTA) 测量到的地理缩小 (GA) 的大小.
- 为了评估基于人工智能 (AI) 的GA在这些四种成像模式上的自动细分的准确性.
主要方法:
- 回顾性收集高质量的FAF,N-IR,RM和OCTA图像,来自GA患者.
- 从70%的图像中使用手动细分的GA和虚假区域训练一个AI模型 (可训练的Weka细分器).
- 在30%的测试组上比较自动和手动GA测量,以确定细分精度.
主要成果:
- 基于AI的自动细分显示了FAF,N-IR和OCTA成像的非常好的性能.
- 手动和自动测量之间的课内相关系数 (ICC) 为FAF的0.82,N-IR的0.81,RM的0.67,OCTA的0.77.
- 在FAF和N-IR成像自动测量时,注意到GA面积略高估计.
结论:
- 基于AI的自动细分对于使用FAF,N-IR和OCTA进行GA评估非常有效.
- 在使用FAF和N-IR时,应考虑AI对GA的高估.
- 由于其他面对面成像技术的优越性,RM成像不适合基于AI的自动GA区域评估.
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