摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-人工智能的新研究是一篇报道的主题。根据NewsRx编辑在土耳其伊斯坦布尔的新闻报道，研究表明：“光学相干断层扫描术(OCT)已经进入眼科领域的一个变革时代，为眼科疾病的检测提供了高分辨率的非侵入性成像。OCT通常用于诊断基础眼科疾病，如青光眼和年龄相关性黄斑变性(AMD)，"在宽屏广告技术的应用中起着重要作用."我们的新闻记者引用了伊斯坦布尔大学Cerrahpasa的研究，“除了青光眼和AMD之外，我们还将调查相关的PA病理，如视网膜前膜(ERM)、黄斑裂孔(MH)、黄斑营养不良HY(MD)、玻璃体黄斑牵引(VMT)、糖尿病黄斑病变(DMP)、囊样黄斑水肿(CME)、中心性浆液性脉络膜视网膜血管病变(CSC)、糖尿病黄斑水肿(DME)、糖尿病黄斑近视黄斑变性(MMD)和脉络膜新生血管形成(CNV)疾病。本综述考察了OCT图像在检测、表征和监测眼疾病中的作用。使用2020 PRISMA指南对使用机器学习(ML)或深度学习(DL)技术的各种眼疾病的研究进行了系统综述。在IEEE、PubMed、Web of Science和Scopus数据库中进行的彻底搜索获得了1787篇出版物。其中1136篇在删除重复后仍然存在。随后排除了会议论文、综述论文和非开放获取的文章，使选择的文章减少到511篇。进一步的审查导致由于索引质量较低或相关性较差而排除了435篇文章，导致76篇期刊文章供审查。我们发现基于ML的决策支持的一个主要挑战是特征的丰富性和重要性的确定，相比之下，基于DL的决策支持具有即插即用的特点，而不是依赖于一种反复试验或错误的方法。此外，我们观察到预先训练的网络是实用的，特别是在处理复杂图像时，例如OCT。医学决策支持主要是为了减少眼科和视网膜专家在日常工作中的工作量。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting out of Istanbul, Turke y, by NewsRx editors, research stated, "Optical coherence tomography (OCT) has u shered in a transformative era in the domain of ophthalmology, offering non-inva sive imaging with high resolution for ocular disease detection. OCT, which is fr equently used in diagnosing fundamental ocular pathologies, such as glaucoma and age-related macular degeneration (AMD), plays an important role in the widespre ad adoption of this technology." Our news journalists obtained a quote from the research from Istanbul University -Cerrahpasa, "Apart from glaucoma and AMD, we will also investigate pertinent pa thologies, such as epiretinal membrane (ERM), macular hole (MH), macular dystrop hy (MD), vitreomacular traction (VMT), diabetic maculopathy (DMP), cystoid macul ar edema (CME), central serous chorioretinopathy (CSC), diabetic macular edema ( DME), diabetic retinopathy (DR), drusen, glaucomatous optic neuropathy (GON), ne ovascular AMD (nAMD), myopia macular degeneration (MMD) and choroidal neovascula rization (CNV) diseases. This comprehensive review examines the role that OCT-de rived images play in detecting, characterizing, and monitoring eye diseases. The 2020 PRISMA guideline was used to structure a systematic review of research on various eye conditions using machine learning (ML) or deep learning (DL) techniq ues. A thorough search across IEEE, PubMed, Web of Science, and Scopus databases yielded 1787 publications, of which 1136 remained after removing duplicates. Su bsequent exclusion of conference papers, review papers, and non-open-access arti cles reduced the selection to 511 articles. Further scrutiny led to the exclusio n of 435 more articles due to lower-quality indexing or irrelevance, resulting i n 76 journal articles for the review. During our investigation, we found that a major challenge for ML-based decision support is the abundance of features and t he determination of their significance. In contrast, DL-based decision support i s characterized by a plug-and-play nature rather than relying on a trial-and-err or approach. Furthermore, we observed that pre-trained networks are practical an d especially useful when working on complex images such as OCT. Consequently, pr e-trained deep networks were frequently utilized for classification tasks. Curre ntly, medical decision support aims to reduce the workload of ophthalmologists a nd retina specialists during routine tasks."
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