PDF(Pubmed)
Abstract：
UNASSIGNED: Retinopathy of prematurity (ROP) poses a significant global threat to childhood vision, necessitating effective screening strategies. This study addresses the impact of color channels in fundus imaging on ROP diagnosis, emphasizing the efficacy and safety of utilizing longer wavelengths, such as red or green for enhanced depth information and improved diagnostic capabilities.
UNASSIGNED: This study aims to assess the spectral effectiveness in color fundus photography for the deep learning classification of ROP.
UNASSIGNED: A convolutional neural network end-to-end classifier was utilized for deep learning classification of normal, stage 1, stage 2, and stage 3 ROP fundus images. The classification performances with individual-color-channel inputs, i.e., red, green, and blue, and multi-color-channel fusion architectures, including early-fusion, intermediate-fusion, and late-fusion, were quantitatively compared.
UNASSIGNED: For individual-color-channel inputs, similar performance was observed for green channel (88.00% accuracy, 76.00% sensitivity, and 92.00% specificity) and red channel (87.25% accuracy, 74.50% sensitivity, and 91.50% specificity), which is substantially outperforming the blue channel (78.25% accuracy, 56.50% sensitivity, and 85.50% specificity). For multi-color-channel fusion options, the early-fusion and intermediate-fusion architecture showed almost the same performance when compared to the green/red channel input, and they outperformed the late-fusion architecture.
UNASSIGNED: This study reveals that the classification of ROP stages can be effectively achieved using either the green or red image alone. This finding enables the exclusion of blue images, acknowledged for their increased susceptibility to light toxicity.
UNASSIGNED: This study aims to assess the spectral effectiveness in color fundus photography for the deep learning classification of ROP.
UNASSIGNED: A convolutional neural network end-to-end classifier was utilized for deep learning classification of normal, stage 1, stage 2, and stage 3 ROP fundus images. The classification performances with individual-color-channel inputs, i.e., red, green, and blue, and multi-color-channel fusion architectures, including early-fusion, intermediate-fusion, and late-fusion, were quantitatively compared.
UNASSIGNED: For individual-color-channel inputs, similar performance was observed for green channel (88.00% accuracy, 76.00% sensitivity, and 92.00% specificity) and red channel (87.25% accuracy, 74.50% sensitivity, and 91.50% specificity), which is substantially outperforming the blue channel (78.25% accuracy, 56.50% sensitivity, and 85.50% specificity). For multi-color-channel fusion options, the early-fusion and intermediate-fusion architecture showed almost the same performance when compared to the green/red channel input, and they outperformed the late-fusion architecture.
UNASSIGNED: This study reveals that the classification of ROP stages can be effectively achieved using either the green or red image alone. This finding enables the exclusion of blue images, acknowledged for their increased susceptibility to light toxicity.
摘要：
■早产儿视网膜病变（ROP）对儿童视力构成了重大的全球威胁，需要有效的筛查策略。本研究探讨了眼底成像中彩色通道对ROP诊断的影响，强调使用较长波长的有效性和安全性，例如用于增强深度信息和改进诊断能力的红色或绿色。
■本研究旨在评估彩色眼底摄影中ROP深度学习分类的光谱有效性。
■卷积神经网络端到端分类器用于正常的深度学习分类，阶段1、阶段2和阶段3ROP眼底图像。具有单个颜色通道输入的分类性能，即，红色,绿色,蓝色,和多色通道融合架构，包括早期融合，中间融合,和后期融合，进行了定量比较。
■对于单个颜色通道输入，绿色通道观察到类似的性能(88.00%的准确率，灵敏度76.00%，和92.00%的特异性)和红色通道(87.25%的准确性，74.50%灵敏度,和91.50%的特异性)，这大大优于蓝色通道(78.25%的准确度，56.50%灵敏度,和85.50%的特异性)。对于多色通道融合选项，与绿色/红色通道输入相比，早期融合和中间融合架构显示出几乎相同的性能，它们的性能优于后期融合架构。
■这项研究表明，单独使用绿色或红色图像可以有效地实现ROP阶段的分类。这一发现可以排除蓝色图像，承认它们对光毒性的敏感性增加。
■本研究旨在评估彩色眼底摄影中ROP深度学习分类的光谱有效性。
■卷积神经网络端到端分类器用于正常的深度学习分类，阶段1、阶段2和阶段3ROP眼底图像。具有单个颜色通道输入的分类性能，即，红色,绿色,蓝色,和多色通道融合架构，包括早期融合，中间融合,和后期融合，进行了定量比较。
■对于单个颜色通道输入，绿色通道观察到类似的性能(88.00%的准确率，灵敏度76.00%，和92.00%的特异性)和红色通道(87.25%的准确性，74.50%灵敏度,和91.50%的特异性)，这大大优于蓝色通道(78.25%的准确度，56.50%灵敏度,和85.50%的特异性)。对于多色通道融合选项，与绿色/红色通道输入相比，早期融合和中间融合架构显示出几乎相同的性能，它们的性能优于后期融合架构。
■这项研究表明，单独使用绿色或红色图像可以有效地实现ROP阶段的分类。这一发现可以排除蓝色图像，承认它们对光毒性的敏感性增加。
