{"title":"人工智能在利用光学相干断层成像诊断不常见囊样黄斑水肿中的应用:系统综述。","authors":"","doi":"10.1016/j.survophthal.2024.06.005","DOIUrl":null,"url":null,"abstract":"<div><p>Cystoid macular edema (CME) is a sight-threatening condition often associated with inflammatory and diabetic diseases. Early detection is crucial to prevent irreversible vision loss. Artificial intelligence (AI) has shown promise in automating CME diagnosis through optical coherence tomography (OCT) imaging, but its utility needs critical evaluation. This systematic review assesses the application of AI to diagnosis CME, specifically focusing on disorders like postoperative CME (Irvine Gass syndrome) and retinitis pigmentosa without obvious vasculopathy, using OCT imaging. A comprehensive search was conducted across 6 databases (PubMed, Scopus, Web of Science, Wiley, ScienceDirect, and IEEE) from 2018 to November, 2023. Twenty-three articles met the inclusion criteria and were selected for in-depth analysis. We evaluate AI's role in CME diagnosis and its performance in “detection”, “classification”, and “segmentation” of OCT retinal images. We found that convolutional neural network (CNN)-based methods consistently outperformed other machine learning techniques, achieving an average accuracy of over 96 % in detecting and identifying CME from OCT images. Despite certain limitations such as dataset size and ethical concerns, the synergy between AI and OCT, particularly through CNNs, holds promise for significantly advancing CME diagnostics.</p></div>","PeriodicalId":22102,"journal":{"name":"Survey of ophthalmology","volume":"69 6","pages":"Pages 937-944"},"PeriodicalIF":5.1000,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0039625724000730/pdfft?md5=6ea55528589d75249db0494f68fee5e8&pid=1-s2.0-S0039625724000730-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Applications of artificial intelligence in diagnosis of uncommon cystoid macular edema using optical coherence tomography imaging: A systematic review\",\"authors\":\"\",\"doi\":\"10.1016/j.survophthal.2024.06.005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Cystoid macular edema (CME) is a sight-threatening condition often associated with inflammatory and diabetic diseases. Early detection is crucial to prevent irreversible vision loss. Artificial intelligence (AI) has shown promise in automating CME diagnosis through optical coherence tomography (OCT) imaging, but its utility needs critical evaluation. This systematic review assesses the application of AI to diagnosis CME, specifically focusing on disorders like postoperative CME (Irvine Gass syndrome) and retinitis pigmentosa without obvious vasculopathy, using OCT imaging. A comprehensive search was conducted across 6 databases (PubMed, Scopus, Web of Science, Wiley, ScienceDirect, and IEEE) from 2018 to November, 2023. Twenty-three articles met the inclusion criteria and were selected for in-depth analysis. We evaluate AI's role in CME diagnosis and its performance in “detection”, “classification”, and “segmentation” of OCT retinal images. We found that convolutional neural network (CNN)-based methods consistently outperformed other machine learning techniques, achieving an average accuracy of over 96 % in detecting and identifying CME from OCT images. Despite certain limitations such as dataset size and ethical concerns, the synergy between AI and OCT, particularly through CNNs, holds promise for significantly advancing CME diagnostics.</p></div>\",\"PeriodicalId\":22102,\"journal\":{\"name\":\"Survey of ophthalmology\",\"volume\":\"69 6\",\"pages\":\"Pages 937-944\"},\"PeriodicalIF\":5.1000,\"publicationDate\":\"2024-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0039625724000730/pdfft?md5=6ea55528589d75249db0494f68fee5e8&pid=1-s2.0-S0039625724000730-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Survey of ophthalmology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0039625724000730\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OPHTHALMOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Survey of ophthalmology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0039625724000730","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
囊样黄斑水肿(CME)是一种威胁视力的疾病,通常与炎症和糖尿病相关。早期发现对于防止不可逆转的视力丧失至关重要。人工智能(AI)在通过光学相干断层扫描(OCT)成像自动诊断 CME 方面已显示出前景,但其实用性还需要严格评估。本系统性综述评估了人工智能在CME诊断中的应用,特别关注术后CME(欧文-加斯综合征)和无明显血管病变的视网膜色素变性等疾病的OCT成像。从 2018 年到 2023 年 11 月,我们在 6 个数据库(PubMed、Scopus、Web of Science、Wiley、ScienceDirect 和 IEEE)中进行了全面检索。有 23 篇文章符合纳入标准,并被选中进行深入分析。我们评估了人工智能在 CME 诊断中的作用及其在 OCT 视网膜图像的 "检测"、"分类 "和 "分割 "中的性能。我们发现,基于卷积神经网络(CNN)的方法始终优于其他机器学习技术,在从 OCT 图像检测和识别 CME 方面的平均准确率超过 96%。尽管存在数据集规模和伦理问题等某些限制,但人工智能与 OCT 之间的协同作用,特别是通过 CNN,有望显著推进 CME 诊断。
Applications of artificial intelligence in diagnosis of uncommon cystoid macular edema using optical coherence tomography imaging: A systematic review
Cystoid macular edema (CME) is a sight-threatening condition often associated with inflammatory and diabetic diseases. Early detection is crucial to prevent irreversible vision loss. Artificial intelligence (AI) has shown promise in automating CME diagnosis through optical coherence tomography (OCT) imaging, but its utility needs critical evaluation. This systematic review assesses the application of AI to diagnosis CME, specifically focusing on disorders like postoperative CME (Irvine Gass syndrome) and retinitis pigmentosa without obvious vasculopathy, using OCT imaging. A comprehensive search was conducted across 6 databases (PubMed, Scopus, Web of Science, Wiley, ScienceDirect, and IEEE) from 2018 to November, 2023. Twenty-three articles met the inclusion criteria and were selected for in-depth analysis. We evaluate AI's role in CME diagnosis and its performance in “detection”, “classification”, and “segmentation” of OCT retinal images. We found that convolutional neural network (CNN)-based methods consistently outperformed other machine learning techniques, achieving an average accuracy of over 96 % in detecting and identifying CME from OCT images. Despite certain limitations such as dataset size and ethical concerns, the synergy between AI and OCT, particularly through CNNs, holds promise for significantly advancing CME diagnostics.
期刊介绍:
Survey of Ophthalmology is a clinically oriented review journal designed to keep ophthalmologists up to date. Comprehensive major review articles, written by experts and stringently refereed, integrate the literature on subjects selected for their clinical importance. Survey also includes feature articles, section reviews, book reviews, and abstracts.