使用深度学习在眼科中拒绝光学连贯性断层图像:系统性审查
Hanya Ahmed1, Qianni Zhang1, Robert Donnan2
1Department of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK.
Journal of imaging
|April 26, 2024
概括
深度学习有效地否定光学连贯断层扫描 (OCT) 视网膜图像,改善疾病诊断. 然而,为了更广泛的临床应用,研究质量和报告需要改进.
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) 对于检测视网膜疾病和边界定位至关重要.
- 在OCT图像中的斑点噪声会降低诊断质量.
- 深度学习 (DL) 提供了一个有前途的端到端解决方案,用于拒绝OCT图像.
研究的目的:
- 审查使用深度学习来消除视网膜OCT图像的研究.
- 评估基于DL的无雾化方法的质量和性能.
- 确定该领域的趋势和挑战.
主要方法:
- 对多个数据库 (PubMed,Google Scholar,Scopus,Embase) 和ArXiv进行系统的选,以查看2010年以后的出版物.
- 使用图像质量指标 (PSNR,CNR,SSIM) 进行选择研究的质量评估.
- 在对95个已识别的出版物进行彻底评估后,包括了41项研究.
主要成果:
- 深度学习模型在消除视网膜OCT图像方面表现有希望,据报道,图像质量指标的增加.
- 研究使用了各种OCT图像类型,包括公开的视网膜数据集和视神经头部 (ONH) 图像.
- 研究方法和性能测量的显著异质性阻止了元分析.
- 少数研究 (n=8) 显示偏差风险较低.
结论:
- 深度学习越来越多地应用于消除视网膜OCT图像,显示出提高诊断清晰度的潜力.
- 报告和研究质量的变化目前限制了综合分析和临床整合.
- 需要进一步标准化方法和报告,以充分利用DL用于OCT图像染.
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