Pur Daiana Roxana, Krance Saffire, Pucchio Aidan, Bassi Arshpreet, Miranda Rafael N, Felfeli Tina
Schulich School of Medicine and Dentistry, Ontario, London, Canada.
School of Medicine, Queen's University, Ontario, Kingston, Canada.
Graefes Arch Clin Exp Ophthalmol. 2023 Feb;261(2):317-336. doi: 10.1007/s00417-022-05769-5. Epub 2022 Aug 4.
To review the literature on the application of bioinformatics and artificial intelligence (AI) for analysis of biofluid biomarkers in retinal vein occlusion (RVO) and their potential utility in clinical decision-making.
We systematically searched MEDLINE, Embase, Cochrane, and Web of Science databases for articles reporting on AI or bioinformatics in RVO involving biofluids from inception to August 2021. Simple AI was categorized as logistics regressions of any type. Risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Tools.
Among 10,264 studies screened, 14 eligible articles, encompassing 578 RVO patients, met the inclusion criteria. The use and reporting of AI and bioinformatics was heterogenous. Four articles performed proteomic analyses, two of which integrated AI tools such as discriminant analysis, probabilistic clustering, and string pathway analysis. A metabolomic study used AI tools for clustering, classification, and predictive modeling such as orthogonal partial least squares discriminant analysis. However, most studies used simple AI (n = 9). Vitreous humor sample levels of interleukin-6 (IL-6), vascular endothelial growth factor (VEGF), and aqueous humor levels of intercellular adhesion molecule-1 and IL-8 were implicated in the pathogenesis of branch RVO with macular edema. IL-6 and VEGF may predict visual acuity after intravitreal injections or vitrectomy, respectively. Metabolomics and Kyoto Encyclopedia of Genes and Genomes enrichment analysis identified the metabolic signature of central RVO to be related to lower aqueous humor concentration of carbohydrates and amino acids. Risk of bias was low or moderate for included studies.
Bioinformatics has applications for analysis of proteomics and metabolomics present in biofluids in RVO with AI for clinical decision-making and advancing the future of RVO precision medicine. However, multiple limitations such as simple AI use, small sample volume, inconsistent feasibility of office-based sampling, lack of longitudinal follow-up, lack of sampling before and after RVO, and lack of healthy controls must be addressed in future studies.
回顾关于生物信息学和人工智能（AI）在视网膜静脉阻塞（RVO）生物流体生物标志物分析中的应用及其在临床决策中的潜在效用的文献。
我们系统地检索了MEDLINE、Embase、Cochrane和Web of Science数据库，以查找从创刊到2021年8月报道RVO中涉及生物流体的人工智能或生物信息学的文章。简单人工智能被归类为任何类型的逻辑回归。使用乔安娜·布里格斯研究所批判性评价工具评估偏倚风险。
在筛选的10264项研究中，14篇符合纳入标准的合格文章，涵盖578例RVO患者。人工智能和生物信息学的使用和报告存在异质性。四篇文章进行了蛋白质组学分析，其中两篇整合了判别分析、概率聚类和STRING通路分析等人工智能工具。一项代谢组学研究使用人工智能工具进行聚类、分类和预测建模，如正交偏最小二乘判别分析。然而，大多数研究使用简单人工智能（n = 9）。玻璃体液中白细胞介素-6（IL-6）、血管内皮生长因子（VEGF）的样本水平，以及房水中细胞间黏附分子-1和IL-8的水平与黄斑水肿型分支RVO的发病机制有关。IL-6和VEGF可能分别预测玻璃体内注射或玻璃体切除术后的视力。代谢组学和京都基因与基因组百科全书富集分析确定，中央RVO的代谢特征与房水中碳水化合物和氨基酸浓度较低有关。纳入研究的偏倚风险为低或中度。
生物信息学可用于分析RVO生物流体中的蛋白质组学和代谢组学，并结合人工智能用于临床决策，推动RVO精准医学的未来发展。然而，未来的研究必须解决多个局限性，如简单人工智能的使用、样本量小、基于办公室采样的可行性不一致、缺乏纵向随访、缺乏RVO前后的采样以及缺乏健康对照。
