首页|Istanbul University-Cerrahpasa Reports Findings in Artificial Intelligence [Artificial intelligence in retinal screening using OCT images: A review of the l ast decade (2013-2023)]
Istanbul University-Cerrahpasa Reports Findings in Artificial Intelligence [Artificial intelligence in retinal screening using OCT images: A review of the l ast decade (2013-2023)]
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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."
IstanbulTurkeyEurasiaArtificial In telligenceDiagnostics and ScreeningEmerging TechnologiesHealth and Medicin eMachine LearningOptical Coherence Tomography
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