晶状体自发荧光和不透明对视网膜成像的影响

IF 2 Q2 OPHTHALMOLOGY
Leon von der Emde, Geena C Rennen, Marc Vaisband, Jan Hasenauer, Raffael Liegl, Monika Fleckenstein, Maximilian Pfau, Frank G Holz, Thomas Ach
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引用次数: 0

摘要

背景视网膜成像,包括眼底自发荧光(FAF),在很大程度上取决于光学介质的清晰度。晶状体状态至关重要,因为老化的晶状体具有阻光和自发荧光(AF)特性,会扭曲图像分析。在此,我们报告了晶状体不透光和自发荧光指标及其对自动图像质量评估的影响。方法 227 名受试者(年龄范围:19-89 岁)接受了晶状体定量 AF(LQAF)、Scheimpflug、前房光学相干断层扫描以及蓝/绿 FAF(BAF/GAF)和红外线(IR)成像。提取 LQAF 值、Pentacam 晶核分期评分和晶状体相对反射率来估计晶状体混浊。医学读者对 FAF 和红外成像质量进行了平均意见评分。使用卷积神经网络(CNN)建立了预测图像质量的回归模型。进行了相关性分析,以评估透镜评分与人类或 CNN 注释得出的视网膜图像质量之间的关联。结果 在所有成像模式中,视网膜图像质量普遍较高(IR (8.25±1.99) >GAF >BAF (6.6±3.13))。CNN 图像质量预测效果极佳(平均绝对误差 (MAE) 0.9)。预测结果与人类分级相当。总体而言,在所有成像模式中,LQAF 与图像质量分级标准的相关性最高(例如,BAF/LQAF 的 Pearson 相关性±CI 为 -0.35 (-0.50 to 0.18))。BAF 图像质量最容易受到光栅度量增加的影响,而 IR(-0.19 (-0.38 to 0.01))则表现出最高的恢复能力。结论 基于 CNN 的视网膜图像质量评估取得了出色的结果。研究强调了 BAF 易受光栅重塑的影响。这些结果有助于制定临床研究的临界值,确保为监测视网膜疾病收集可靠的数据。如有合理要求,可提供数据。如有合理要求,可向通讯作者索取数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impact of lens autofluorescence and opacification on retinal imaging
Background Retinal imaging, including fundus autofluorescence (FAF), strongly depends on the clearness of the optical media. Lens status is crucial since the ageing lens has both light-blocking and autofluorescence (AF) properties that distort image analysis. Here, we report both lens opacification and AF metrics and the effect on automated image quality assessment. Methods 227 subjects (range: 19–89 years old) received quantitative AF of the lens (LQAF), Scheimpflug, anterior chamber optical coherence tomography as well as blue/green FAF (BAF/GAF), and infrared (IR) imaging. LQAF values, the Pentacam Nucleus Staging score and the relative lens reflectivity were extracted to estimate lens opacification. Mean opinion scores of FAF and IR image quality were compiled by medical readers. A regression model for predicting image quality was developed using a convolutional neural network (CNN). Correlation analysis was conducted to assess the association of lens scores, with retinal image quality derived from human or CNN annotations. Results Retinal image quality was generally high across all imaging modalities (IR (8.25±1.99) >GAF >BAF (6.6±3.13)). CNN image quality prediction was excellent (average mean absolute error (MAE) 0.9). Predictions were comparable to human grading. Overall, LQAF showed the highest correlation with image quality grading criteria for all imaging modalities (eg, Pearson correlation±CI −0.35 (−0.50 to 0.18) for BAF/LQAF). BAF image quality was most vulnerable to an increase in lenticular metrics, while IR (−0.19 (−0.38 to 0.01)) demonstrated the highest resilience. Conclusion The use of CNN-based retinal image quality assessment achieved excellent results. The study highlights the vulnerability of BAF to lenticular remodelling. These results can aid in the development of cut-off values for clinical studies, ensuring reliable data collection for the monitoring of retinal diseases. Data are available upon reasonable request. Data are available from the corresponding author upon reasonable request.
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来源期刊
BMJ Open Ophthalmology
BMJ Open Ophthalmology OPHTHALMOLOGY-
CiteScore
3.40
自引率
4.20%
发文量
104
审稿时长
20 weeks
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