ROPRNet: Deep learning-assisted recurrence prediction for retinopathy of prematurity

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL
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Abstract

Retinopathy of Prematurity (ROP) recurrence is significant for the prognosis of ROP treatment. In this paper, corrected gestational age at treatment is involved as an important risk factor for the assessment of ROP recurrence. To reveal the complementary information from fundus images and risk factors, a dual-modal deep learning framework with two feature extraction streams, termed as ROPRNet, is designed to assist recurrence prediction of ROP after anti-vascular endothelial growth factor (Anti-VEGF) treatment, involving a stacked autoencoder (SAE) stream for risk factors and a cascaded deep network (CDN) stream for fundus images. Here, the specifically-designed CDN stream involves several novel modules to effectively capture subtle structural changes of retina in the fundus images, involving enhancement head (EH), enhanced ConvNeXt (EnConvNeXt) and multi-dimensional multi-scale feature fusion (MMFF). Specifically, EH is designed to suppress the variations of color and contrast in fundus images, which can highlight the informative features in the images. To comprehensively reveal the inherent medical hints submerged in the fundus images, an adaptive triple-branch attention (ATBA) and a special ConvNeXt with a rare-class sample generator (RSG) were designed to compose the EnConvNeXt for effectively extracting features from fundus images. The MMFF is designed for feature aggregation to mitigate redundant features from several fundus images from different shooting angles, involving a designed multi-dimensional and multi-sale attention (MD-MSA). The designed ROPRNet is validated on a real clinical dataset, which indicate that it is superior to several existing ROP diagnostic models, in terms of 0.894 AUC, 0.818 accuracy, 0.828 sensitivity and 0.800 specificity.
ROPRNet:深度学习辅助早产儿视网膜病变复发预测
早产儿视网膜病变(ROP)复发对早产儿视网膜病变治疗的预后意义重大。本文将治疗时的矫正胎龄作为评估早产儿视网膜病变复发的一个重要风险因素。为了揭示眼底图像和风险因素的互补信息,我们设计了一种具有两个特征提取流的双模式深度学习框架(称为 ROPRNet),以帮助预测抗血管内皮生长因子(Anti-VEGF)治疗后 ROP 的复发,其中包括一个用于风险因素的堆叠自动编码器(SAE)流和一个用于眼底图像的级联深度网络(CDN)流。在这里,专门设计的 CDN 流涉及多个新型模块,以有效捕捉眼底图像中视网膜的细微结构变化,包括增强头(EH)、增强 ConvNeXt(EnConvNeXt)和多维多尺度特征融合(MMFF)。具体来说,EH 的设计目的是抑制眼底图像中颜色和对比度的变化,从而突出图像中的信息特征。为了全面揭示潜藏在眼底图像中的内在医学信息,设计了自适应三分支注意(ATBA)和带有稀有类样本生成器(RSG)的特殊 ConvNeXt 来组成 EnConvNeXt,以有效提取眼底图像中的特征。MMFF 设计用于特征聚合,以减少来自不同拍摄角度的多个眼底图像的冗余特征,其中涉及设计的多维和多销售关注(MD-MSA)。设计的 ROPRNet 在真实的临床数据集上进行了验证,结果表明它在 AUC 值 0.894、准确率 0.818、灵敏度 0.828 和特异性 0.800 方面优于现有的几个 ROP 诊断模型。
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来源期刊
Biomedical Signal Processing and Control
Biomedical Signal Processing and Control 工程技术-工程:生物医学
CiteScore
9.80
自引率
13.70%
发文量
822
审稿时长
4 months
期刊介绍: Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.
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