使用人工智能在体内识别角异构及其与与眼相关的遗传标记的关系
Viney Gupta1, Shweta Birla2, Toshit Varshney1
1Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences (AIIMS), New Delhi, India.
Indian journal of ophthalmology
|December 26, 2023
概括
深度学习可以准确地检测AS-OCT图像上的角度异构 (ADoA),将其与眼瘤基因突变相关联. 这种AI方法可以客观地识别青光眼患者的ADoA.
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:
- 视角失调 (ADoA) 是各种青光眼类型的关键因素.
- 准确地识别ADoA对于青光眼的诊断和管理至关重要.
- 前段光学连贯断层扫描 (AS-OCT) 提供了眼睛前段的详细成像.
研究的目的:
- 开发和验证一个深度学习 (DL) 模型,用于预测AS-OCT图像上的ADoA.
- 评估DL识别的ADoA与已知眼相关基因的突变之间的相关性.
主要方法:
- 利用800张高清AS-OCT图像进行模型开发和验证.
- 训练了一个机器学习模型在340图像从青光眼患者 (初级先天性,青少年发病的开放角度,成人发病的初级开放角度) 和对照.
- 基于特定的AS-OCT特征定义的ADoA:缺席的Schlemm通道,超反射性在椎网状网络上,或超反射膜.
主要成果:
- 在检测ADoA时,DL模型实现了高精度 (>95%),特异性 (>97%) 和灵敏性 (>96%).
- 大多数已知患有青光眼基因突变 (MYOC,CYP1B1,FOXC1,LTBP2) 的患者的图像中都存在ADoA.
- 相比之下,ADoA仅在5%的健康对照中存在.
结论:
- 深度学习模型可以客观而准确地从AS-OCT图像中识别角度异位.
- 这些发现支持DL在眼研究中的实用性,并可能在临床实践中用于识别ADoA.
- 这项研究强调了ADoA与特定格洛科马基因突变之间的强烈关联.
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