光学连贯断层扫描中的深度学习:缺口在哪里?
Dawei Li1, An Ran Ran2, Carol Y Cheung2
1College of Future Technology, Peking University, Beijing, China.
Clinical & experimental ophthalmology
|May 28, 2023
概括
深度学习 (DL) 显示出在眼科中自动化光连贯断层扫描 (OCT) 图像分析的潜力,用于细分和疾病分类等任务. 然而,对于临床使用,必须解决数据稀缺性和缺乏透明度等挑战.
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:
- 光学连贯断层扫描 (OCT) 通过成像斑点和视神经头部来诊断眼睛疾病至关重要.
- 由于潜在的文物和影响定量测量的混条件,解读OCT图像需要专门的专业知识.
- 深度学习 (DL) 方法正在越来越多地被用于自动化OCT图像分析.
研究的目的:
- 审查眼科中基于DL的OCT图像分析的当前趋势.
- 确定现有领域的差距和挑战.
- 建议未来的临床应用研究方向.
主要方法:
- 审查使用DL分析OCT图像的研究.
- 确定DL表现出表现的关键任务.
- 分析阻碍临床采用的挑战.
主要成果:
- 在海外图像细分,量化,疾病分类,进展预测和分类方面,DL显示出有前途的表现.
- 确定的挑战包括公共数据的稀缺性,现实世界的绩效差异,模型透明度问题,监管障碍和有限的OCT可访问性.
结论:
- 在眼科中,DL为增强OCT图像分析提供了显著的潜力.
- 解决数据可用性,模型解释性和监管标准对于成功的临床整合至关重要.
- 需要进一步的研究来克服目前的局限性,以便在临床广泛采用之前进行进一步的研究.
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