Muntean George Adrian, Marginean Anca, Groza Adrian, Damian Ioana, Roman Sara Alexia, Hapca Mădălina Claudia, Muntean Maximilian Vlad, Nicoară Simona Delia
Department of Ophthalmology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Emergency County Hospital, 400347 Cluj-Napoca, Romania.
Department of Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.
Diagnostics (Basel). 2023 Jul 24;13(14):2464. doi: 10.3390/diagnostics13142464.
The era of artificial intelligence (AI) has revolutionized our daily lives and AI has become a powerful force that is gradually transforming the field of medicine. Ophthalmology sits at the forefront of this transformation thanks to the effortless acquisition of an abundance of imaging modalities. There has been tremendous work in the field of AI for retinal diseases, with age-related macular degeneration being at the top of the most studied conditions. The purpose of the current systematic review was to identify and evaluate, in terms of strengths and limitations, the articles that apply AI to optical coherence tomography (OCT) images in order to predict the future evolution of age-related macular degeneration (AMD) during its natural history and after treatment in terms of OCT morphological structure and visual function. After a thorough search through seven databases up to 1 January 2022 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 1800 records were identified. After screening, 48 articles were selected for full-text retrieval and 19 articles were finally included. From these 19 articles, 4 articles concentrated on predicting the anti-VEGF requirement in neovascular AMD (nAMD), 4 articles focused on predicting anti-VEGF efficacy in nAMD patients, 3 articles predicted the conversion from early or intermediate AMD (iAMD) to nAMD, 1 article predicted the conversion from iAMD to geographic atrophy (GA), 1 article predicted the conversion from iAMD to both nAMD and GA, 3 articles predicted the future growth of GA and 3 articles predicted the future outcome for visual acuity (VA) after anti-VEGF treatment in nAMD patients. Since using AI methods to predict future changes in AMD is only in its initial phase, a systematic review provides the opportunity of setting the context of previous work in this area and can present a starting point for future research.
人工智能（AI）时代彻底改变了我们的日常生活，人工智能已成为一股强大力量，正逐步改变医学领域。由于能轻松获取大量成像模态，眼科处于这一变革的前沿。在人工智能用于视网膜疾病的领域已经开展了大量工作，年龄相关性黄斑变性是研究最多的病症之首。本系统评价的目的是识别并评估那些将人工智能应用于光学相干断层扫描（OCT）图像，以便从OCT形态结构和视觉功能方面预测年龄相关性黄斑变性（AMD）在其自然病程及治疗后的未来演变的文章，同时评估其优缺点。按照系统评价与Meta分析的首选报告项目（PRISMA）指南对截至2022年1月1日的七个数据库进行全面检索后，共识别出1800条记录。筛选后，选择48篇文章进行全文检索，最终纳入19篇文章。在这19篇文章中，4篇专注于预测新生血管性AMD（nAMD）的抗血管内皮生长因子（VEGF）需求，4篇聚焦于预测nAMD患者的抗VEGF疗效，3篇预测早期或中期AMD（iAMD）向nAMD的转变，1篇预测iAMD向地理性萎缩（GA）的转变，1篇预测iAMD向nAMD和GA两者的转变，3篇预测GA的未来发展，3篇预测nAMD患者接受抗VEGF治疗后视力（VA）的未来结果。由于使用人工智能方法预测AMD的未来变化尚处于初始阶段，系统评价提供了梳理该领域以往工作背景的机会，并可为未来研究提供一个起点。
