{"title":"ROPRNet: Deep learning-assisted recurrence prediction for retinopathy of prematurity","authors":"","doi":"10.1016/j.bspc.2024.107135","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Signal Processing and Control","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1746809424011935","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
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.
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.