OCTA鉴别动静脉分析预测糖尿病黄斑水肿抗vegf治疗结果。

IF 2.9 2区 医学 Q2 BIOCHEMICAL RESEARCH METHODS
Mansour Abtahi, Albert K Dadzie, Behrouz Ebrahimi, Boda Huang, Yi-Ting Hsieh, Xincheng Yao
{"title":"OCTA鉴别动静脉分析预测糖尿病黄斑水肿抗vegf治疗结果。","authors":"Mansour Abtahi, Albert K Dadzie, Behrouz Ebrahimi, Boda Huang, Yi-Ting Hsieh, Xincheng Yao","doi":"10.1364/BOE.557748","DOIUrl":null,"url":null,"abstract":"<p><p>This study evaluates the role of differential artery-vein (AV) analysis in optical coherence tomography angiography (OCTA) for treatment outcome prediction of diabetic macular edema (DME). Deep learning AV segmentation in OCTA enabled the robust extraction of quantitative AV features, including perfusion intensity density (PID), blood vessel density (BVD), vessel skeleton density (VSD), vessel area flux (VAF), blood vessel caliber (BVC), blood vessel tortuosity (BVT), and vessel perimeter index (VPI). Support vector machine (SVM) classifiers were employed to predict changes in best-corrected visual acuity (BCVA) and central retinal thickness (CRT). Comparative analysis revealed that differential AV analysis significantly enhanced prediction performance, with BCVA accuracy improved from 70.45% to 86.36% and CRT accuracy enhanced from 68.18% to 79.55% compared to traditional OCTA analysis. These findings underscore the potential of AV analysis as a transformative tool for advancing personalized therapeutic strategies and improving clinical decision-making in managing DME.</p>","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":"16 4","pages":"1732-1741"},"PeriodicalIF":2.9000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12047724/pdf/","citationCount":"0","resultStr":"{\"title\":\"Differential artery-vein analysis in OCTA for predicting the anti-VEGF treatment outcome of diabetic macular edema.\",\"authors\":\"Mansour Abtahi, Albert K Dadzie, Behrouz Ebrahimi, Boda Huang, Yi-Ting Hsieh, Xincheng Yao\",\"doi\":\"10.1364/BOE.557748\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This study evaluates the role of differential artery-vein (AV) analysis in optical coherence tomography angiography (OCTA) for treatment outcome prediction of diabetic macular edema (DME). Deep learning AV segmentation in OCTA enabled the robust extraction of quantitative AV features, including perfusion intensity density (PID), blood vessel density (BVD), vessel skeleton density (VSD), vessel area flux (VAF), blood vessel caliber (BVC), blood vessel tortuosity (BVT), and vessel perimeter index (VPI). Support vector machine (SVM) classifiers were employed to predict changes in best-corrected visual acuity (BCVA) and central retinal thickness (CRT). Comparative analysis revealed that differential AV analysis significantly enhanced prediction performance, with BCVA accuracy improved from 70.45% to 86.36% and CRT accuracy enhanced from 68.18% to 79.55% compared to traditional OCTA analysis. These findings underscore the potential of AV analysis as a transformative tool for advancing personalized therapeutic strategies and improving clinical decision-making in managing DME.</p>\",\"PeriodicalId\":8969,\"journal\":{\"name\":\"Biomedical optics express\",\"volume\":\"16 4\",\"pages\":\"1732-1741\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12047724/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical optics express\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1364/BOE.557748\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical optics express","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1364/BOE.557748","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

摘要

本研究评估了鉴别动静脉(AV)分析在光学相干断层扫描血管造影(OCTA)中对糖尿病黄斑水肿(DME)治疗结果预测的作用。基于OCTA的深度学习AV分割能够鲁棒提取定量AV特征,包括灌注强度密度(PID)、血管密度(BVD)、血管骨架密度(VSD)、血管面积通量(VAF)、血管直径(BVC)、血管弯曲度(BVT)和血管周长指数(VPI)。采用支持向量机(SVM)分类器预测最佳矫正视力(BCVA)和视网膜中央厚度(CRT)的变化。对比分析显示,差异AV分析显著提高了预测效果,与传统OCTA分析相比,BCVA准确率从70.45%提高到86.36%,CRT准确率从68.18%提高到79.55%。这些发现强调了AV分析作为推进个性化治疗策略和改善管理DME临床决策的变革性工具的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Differential artery-vein analysis in OCTA for predicting the anti-VEGF treatment outcome of diabetic macular edema.

This study evaluates the role of differential artery-vein (AV) analysis in optical coherence tomography angiography (OCTA) for treatment outcome prediction of diabetic macular edema (DME). Deep learning AV segmentation in OCTA enabled the robust extraction of quantitative AV features, including perfusion intensity density (PID), blood vessel density (BVD), vessel skeleton density (VSD), vessel area flux (VAF), blood vessel caliber (BVC), blood vessel tortuosity (BVT), and vessel perimeter index (VPI). Support vector machine (SVM) classifiers were employed to predict changes in best-corrected visual acuity (BCVA) and central retinal thickness (CRT). Comparative analysis revealed that differential AV analysis significantly enhanced prediction performance, with BCVA accuracy improved from 70.45% to 86.36% and CRT accuracy enhanced from 68.18% to 79.55% compared to traditional OCTA analysis. These findings underscore the potential of AV analysis as a transformative tool for advancing personalized therapeutic strategies and improving clinical decision-making in managing DME.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Biomedical optics express
Biomedical optics express BIOCHEMICAL RESEARCH METHODS-OPTICS
CiteScore
6.80
自引率
11.80%
发文量
633
审稿时长
1 months
期刊介绍: The journal''s scope encompasses fundamental research, technology development, biomedical studies and clinical applications. BOEx focuses on the leading edge topics in the field, including: Tissue optics and spectroscopy Novel microscopies Optical coherence tomography Diffuse and fluorescence tomography Photoacoustic and multimodal imaging Molecular imaging and therapies Nanophotonic biosensing Optical biophysics/photobiology Microfluidic optical devices Vision research.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信