Abstract：
OBJECTIVE: This scoping review summarizes the applications of artificial intelligence (AI) and bioinformatics methodologies in analysis of ocular biofluid markers. The secondary objective was to explore supervised and unsupervised AI techniques and their predictive accuracies. We also evaluate the integration of bioinformatics with AI tools.
METHODS: This scoping review was conducted across five electronic databases including EMBASE, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics were included.
RESULTS: A total of 10,262 articles were retrieved from all databases and 177 studies met the inclusion criteria. The most commonly studied ocular diseases were diabetic eye diseases, with 50 papers (28%), while glaucoma was explored in 25 studies (14%), age-related macular degeneration in 20 (11%), dry eye disease in 10 (6%), and uveitis in 9 (5%). Supervised learning was used in 91 papers (51%), unsupervised AI in 83 (46%), and bioinformatics in 85 (48%). Ninety-eight papers (55%) used more than one class of AI (e.g. > 1 of supervised, unsupervised, bioinformatics, or statistical techniques), while 79 (45%) used only one. Supervised learning techniques were often used to predict disease status or prognosis, and demonstrated strong accuracy. Unsupervised AI algorithms were used to bolster the accuracy of other algorithms, identify molecularly distinct subgroups, or cluster cases into distinct subgroups that are useful for prediction of the disease course. Finally, bioinformatic tools were used to translate complex biomarker profiles or findings into interpretable data.
CONCLUSIONS: AI analysis of biofluid markers displayed diagnostic accuracy, provided insight into mechanisms of molecular etiologies, and had the ability to provide individualized targeted therapeutic treatment for patients. Given the progression of AI towards use in both research and the clinic, ophthalmologists should be broadly aware of the commonly used algorithms and their applications. Future research may be aimed at validating algorithms and integrating them in clinical practice.
METHODS: This scoping review was conducted across five electronic databases including EMBASE, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics were included.
RESULTS: A total of 10,262 articles were retrieved from all databases and 177 studies met the inclusion criteria. The most commonly studied ocular diseases were diabetic eye diseases, with 50 papers (28%), while glaucoma was explored in 25 studies (14%), age-related macular degeneration in 20 (11%), dry eye disease in 10 (6%), and uveitis in 9 (5%). Supervised learning was used in 91 papers (51%), unsupervised AI in 83 (46%), and bioinformatics in 85 (48%). Ninety-eight papers (55%) used more than one class of AI (e.g. > 1 of supervised, unsupervised, bioinformatics, or statistical techniques), while 79 (45%) used only one. Supervised learning techniques were often used to predict disease status or prognosis, and demonstrated strong accuracy. Unsupervised AI algorithms were used to bolster the accuracy of other algorithms, identify molecularly distinct subgroups, or cluster cases into distinct subgroups that are useful for prediction of the disease course. Finally, bioinformatic tools were used to translate complex biomarker profiles or findings into interpretable data.
CONCLUSIONS: AI analysis of biofluid markers displayed diagnostic accuracy, provided insight into mechanisms of molecular etiologies, and had the ability to provide individualized targeted therapeutic treatment for patients. Given the progression of AI towards use in both research and the clinic, ophthalmologists should be broadly aware of the commonly used algorithms and their applications. Future research may be aimed at validating algorithms and integrating them in clinical practice.
摘要：
目的：本文综述了人工智能（AI）和生物信息学方法在眼生物流体标志物分析中的应用。次要目标是探索有监督和无监督的AI技术及其预测准确性。我们还评估了生物信息学与人工智能工具的整合。
方法：这项范围审查是在五个电子数据库中进行的，包括EMBASE，Medline,Cochrane中央控制试验登记册,Cochrane系统评价数据库，和WebofScience从成立到2021年7月14日。包括使用AI或生物信息学进行生物流体标记分析的研究。
结果：从所有数据库中检索到10,262篇文献，177项研究符合纳入标准。最常研究的眼病是糖尿病性眼病，有50篇论文(28%)，虽然在25项研究(14%)中探索了青光眼，年龄相关性黄斑变性20例（11%），干眼症10例（6%），9例(5%)葡萄膜炎。监督学习在91篇论文中使用(51%)，83例无监督AI（46%），和生物信息学在85（48％）。98篇论文(55%)使用了一类以上的人工智能(例如>1的监督，无人监督,生物信息学，或统计技术)，而79（45%）只使用了一个。监督学习技术通常用于预测疾病状态或预后。并表现出很强的准确性。无监督AI算法用于增强其他算法的准确性，识别分子上不同的亚组，或将病例聚类为不同的亚组，这些亚组对预测病程很有用。最后,我们使用生物信息学工具将复杂的生物标志物谱或研究结果转化为可解释的数据.
结论：生物流体标志物的AI分析显示出诊断准确性，提供了对分子病因机制的见解，并有能力为患者提供个体化的针对性治疗。鉴于人工智能在研究和临床中的应用进展，眼科医生应该广泛了解常用的算法及其应用。未来的研究可能旨在验证算法并将其整合到临床实践中。
方法：这项范围审查是在五个电子数据库中进行的，包括EMBASE，Medline,Cochrane中央控制试验登记册,Cochrane系统评价数据库，和WebofScience从成立到2021年7月14日。包括使用AI或生物信息学进行生物流体标记分析的研究。
结果：从所有数据库中检索到10,262篇文献，177项研究符合纳入标准。最常研究的眼病是糖尿病性眼病，有50篇论文(28%)，虽然在25项研究(14%)中探索了青光眼，年龄相关性黄斑变性20例（11%），干眼症10例（6%），9例(5%)葡萄膜炎。监督学习在91篇论文中使用(51%)，83例无监督AI（46%），和生物信息学在85（48％）。98篇论文(55%)使用了一类以上的人工智能(例如>1的监督，无人监督,生物信息学，或统计技术)，而79（45%）只使用了一个。监督学习技术通常用于预测疾病状态或预后。并表现出很强的准确性。无监督AI算法用于增强其他算法的准确性，识别分子上不同的亚组，或将病例聚类为不同的亚组，这些亚组对预测病程很有用。最后,我们使用生物信息学工具将复杂的生物标志物谱或研究结果转化为可解释的数据.
结论：生物流体标志物的AI分析显示出诊断准确性，提供了对分子病因机制的见解，并有能力为患者提供个体化的针对性治疗。鉴于人工智能在研究和临床中的应用进展，眼科医生应该广泛了解常用的算法及其应用。未来的研究可能旨在验证算法并将其整合到临床实践中。
