基于人工智能方法的SD-OCT 2rt激光治疗AMD适应症自动确定技术

IF 0.9 Q4 OPTICS Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI:10.3103/S1060992X24700565
A. Yu. Ionov, N. Yu. Ilyasova, N. S. Demin, E. A. Zamytskiy, E. Yu. Zubkova
{"title":"基于人工智能方法的SD-OCT 2rt激光治疗AMD适应症自动确定技术","authors":"A. Yu. Ionov,&nbsp;N. Yu. Ilyasova,&nbsp;N. S. Demin,&nbsp;E. A. Zamytskiy,&nbsp;E. Yu. Zubkova","doi":"10.3103/S1060992X24700565","DOIUrl":null,"url":null,"abstract":"<p>The aim of this work is to develop and study the technology of automatic determination of indications for 2RT-laser treatment of AMD by SD-OCT images based on artificial intelligence methods. This is necessary to improve the accuracy and efficiency of AMD diagnosis, as well as to provide faster and more accurate treatment assignment to each patient. The U-Net architecture was chosen as the neural network architecture to extract the area of interest in the retinal OCT image. The VGG16 architecture was used as the neural network architecture for classification. These architectures are well established. As a result of training, the model showed a fairly high accuracy of 90% for segmentation and 98% for classification. Automatic localization and classification based on SD-OST images will allow the most accurate determination of indications for 2RT laser treatment. This will significantly reduce the burden on physicians and make diagnostics more accessible.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S277 - S284"},"PeriodicalIF":0.9000,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Technology of Automatic Determination of Indications for 2RT-Laser Treatment of AMD from SD-OCT Images Based on Artificial Intelligence Methods\",\"authors\":\"A. Yu. Ionov,&nbsp;N. Yu. Ilyasova,&nbsp;N. S. Demin,&nbsp;E. A. Zamytskiy,&nbsp;E. Yu. Zubkova\",\"doi\":\"10.3103/S1060992X24700565\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The aim of this work is to develop and study the technology of automatic determination of indications for 2RT-laser treatment of AMD by SD-OCT images based on artificial intelligence methods. This is necessary to improve the accuracy and efficiency of AMD diagnosis, as well as to provide faster and more accurate treatment assignment to each patient. The U-Net architecture was chosen as the neural network architecture to extract the area of interest in the retinal OCT image. The VGG16 architecture was used as the neural network architecture for classification. These architectures are well established. As a result of training, the model showed a fairly high accuracy of 90% for segmentation and 98% for classification. Automatic localization and classification based on SD-OST images will allow the most accurate determination of indications for 2RT laser treatment. This will significantly reduce the burden on physicians and make diagnostics more accessible.</p>\",\"PeriodicalId\":721,\"journal\":{\"name\":\"Optical Memory and Neural Networks\",\"volume\":\"33 2 supplement\",\"pages\":\"S277 - S284\"},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2024-12-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Optical Memory and Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.3103/S1060992X24700565\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"OPTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optical Memory and Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.3103/S1060992X24700565","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0

摘要

本工作旨在开发和研究基于人工智能方法的SD-OCT图像自动确定2rt激光治疗AMD适应症的技术。这对于提高AMD诊断的准确性和效率,以及为每位患者提供更快、更准确的治疗分配是必要的。选择U-Net结构作为提取视网膜OCT图像感兴趣区域的神经网络结构。采用VGG16体系结构作为神经网络体系结构进行分类。这些体系结构已经很好地建立起来。经过训练,该模型的分割准确率达到90%,分类准确率达到98%。基于SD-OST图像的自动定位和分类将允许最准确地确定2RT激光治疗的适应症。这将大大减轻医生的负担,使诊断更容易获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Technology of Automatic Determination of Indications for 2RT-Laser Treatment of AMD from SD-OCT Images Based on Artificial Intelligence Methods

The aim of this work is to develop and study the technology of automatic determination of indications for 2RT-laser treatment of AMD by SD-OCT images based on artificial intelligence methods. This is necessary to improve the accuracy and efficiency of AMD diagnosis, as well as to provide faster and more accurate treatment assignment to each patient. The U-Net architecture was chosen as the neural network architecture to extract the area of interest in the retinal OCT image. The VGG16 architecture was used as the neural network architecture for classification. These architectures are well established. As a result of training, the model showed a fairly high accuracy of 90% for segmentation and 98% for classification. Automatic localization and classification based on SD-OST images will allow the most accurate determination of indications for 2RT laser treatment. This will significantly reduce the burden on physicians and make diagnostics more accessible.

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
CiteScore
1.50
自引率
11.10%
发文量
25
期刊介绍: The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.
期刊最新文献
Comparison of Neural Network Architectures in the Thermal Explosion Approximation Problem Review on Crop Yield Prediction by Using Different Machine Learning and Deep Learning Model Erratum to: Human Action Recognition from Motion Capture Data Using Machine Learning Techniques Optimized ResNet50-based Model for Solar Panel Fault Detection Using a Modified Light Spectrum Optimizer Multimodal Sentiment and Emotion Detection Using Hybrid Fusion of Text, Audio, and Image with Explainable AI Integration
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1