Ocular blood flow as a clinical observation: Value, limitations and data analysis.
Ocular blood flow as a clinical observation: Value, limitations and data analysis.
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DOI：
10.1016/j.preteyeres.2020.100841
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发表时间：
2020-01-24
影响因子：
17.8
通讯作者：
Arciero J
中科院分区：
文献类型：
--
作者：
Harris A;Guidoboni G;Siesky B;Mathew S;Verticchio Vercellin AC;Rowe L;Arciero J
眼部血流的改变已被认为是许多眼部疾病发生和发展的重要危险因素。特别是，几项基于人群和纵向的研究已经提供了令人信服的证据，证明血液动力学生物标记物是几个不同地理区域的眼病的独立危险因素。尽管有这些证据，但血流与其他危险因素和共病(如年龄、性别、种族、糖尿病和高血压)协同作用对眼生理和病理的相对贡献仍不确定。目前还没有评估眼内所有相关血管床的金标准，来自多种眼部成像技术的异质血管生物标志物不可互换，难以作为一个整体进行解释。由于这些疾病的复杂性和成像的局限性，标准的统计方法在不同的研究中往往产生不一致的结果，并且无法量化或解释患者患眼病的总体风险。将数学建模与人工智能相结合，有望推动眼科数据分析，并使来自不同的、多输入的临床和人口统计学生物标志物的个性化风险评估成为可能。机制驱动的数学建模使虚拟实验室可用于研究致病机制、提高诊断能力和改进疾病管理。人工智能提供了一种新的方法，可以利用来自各种患者类型的海量数据来诊断和监测眼部疾病。本文综述了眼部血管解剖生理学、眼部成像技术、青光眼和其他眼部疾病的临床表现、机制建模预测等方面的研究现状和尚未解决的主要问题，为临床观察与数学模型和人工智能的结合奠定了基础。提出了综合数据分析的可行替代方案，旨在克服标准统计方法的局限性，并在眼科领域实现个性化定制的精确医学。
Alterations in ocular blood flow have been identified as important risk factors for the onset and progression of numerous diseases of the eye. In particular, several population-based and longitudinal-based studies have provided compelling evidence of hemodynamic biomarkers as independent risk factors for ocular disease throughout several different geographic regions. Despite this evidence, the relative contribution of blood flow to ocular physiology and pathology in synergy with other risk factors and comorbidities (e.g., age, gender, race, diabetes and hypertension) remains uncertain. There is currently no gold standard for assessing all relevant vascular beds in the eye, and the heterogeneous vascular biomarkers derived from multiple ocular imaging technologies are non-interchangeable and difficult to interpret as a whole. As a result of these disease complexities and imaging limitations, standard statistical methods often yield inconsistent results across studies and are unable to quantify or explain a patient’s overall risk for ocular disease. Combining mathematical modeling with artificial intelligence holds great promise for advancing data analysis in ophthalmology and enabling individualized risk assessment from diverse, multi-input clinical and demographic biomarkers. Mechanism-driven mathematical modeling makes virtual laboratories available to investigate pathogenic mechanisms, advance diagnostic ability and improve disease management. Artificial intelligence provides a novel method for utilizing a vast amount of data from a wide range of patient types to diagnose and monitor ocular disease. This article reviews the state of the art and major unanswered questions related to ocular vascular anatomy and physiology, ocular imaging techniques, clinical findings in glaucoma and other eye diseases, and mechanistic modeling predictions, while laying a path for integrating clinical observations with mathematical models and artificial intelligence. Viable alternatives for integrated data analysis are proposed that aim to overcome the limitations of standard statistical approaches and enable individually tailored precision medicine in ophthalmology.
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影响因子：
--
作者：
Bandello F;Sacconi R;Querques L;Corbelli E;Cicinelli MV;Querques G
通讯作者：
Querques G
影响因子：
4.4
作者：
Berdahl JP;Fautsch MP;Stinnett SS;Allingham RR
通讯作者：
Allingham RR
影响因子：
4.4
作者：
Arciero, Julia;Harris, Alon;Guidoboni, Giovanna
通讯作者：
Guidoboni, Giovanna
DOI：
10.2310/7070.2005.4102
发表时间：
2006-02-01
期刊：
JOURNAL OF OTOLARYNGOLOGY
影响因子：
--
作者：
Arikan, OK;Akarsu, C;Koç, C
通讯作者：
Koç, C
影响因子：
0.8
作者：
Banks, H. T.;Bekele-Maxwell, K.;Guidoboni, G.
通讯作者：
Guidoboni, G.