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Ocular blood flow as a clinical observation: Value, limitations and data analysis.
Prog Retin Eye Res. 2020 Jan 24 [Online ahead of print]PR

Abstract

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.

Authors+Show Affiliations

Department of Ophthalmology, Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA. Electronic address: alon.harris@mssm.edu.University of Missouri, Columbia, MO, USA.Department of Ophthalmology, Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA.Indiana University School of Medicine, Indianapolis, IN, USA.Department of Ophthalmology, Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA; University of Pavia, Pavia, Italy; IRCCS - Fondazione Bietti, Rome, Italy.Indiana University School of Medicine, Indianapolis, IN, USA.Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31987983

Citation

Harris, Alon, et al. "Ocular Blood Flow as a Clinical Observation: Value, Limitations and Data Analysis." Progress in Retinal and Eye Research, 2020, p. 100841.
Harris A, Guidoboni G, Siesky B, et al. Ocular blood flow as a clinical observation: Value, limitations and data analysis. Prog Retin Eye Res. 2020.
Harris, A., Guidoboni, G., Siesky, B., Mathew, S., Verticchio Vercellin, A. C., Rowe, L., & Arciero, J. (2020). Ocular blood flow as a clinical observation: Value, limitations and data analysis. Progress in Retinal and Eye Research, 100841. https://doi.org/10.1016/j.preteyeres.2020.100841
Harris A, et al. Ocular Blood Flow as a Clinical Observation: Value, Limitations and Data Analysis. Prog Retin Eye Res. 2020 Jan 24;100841. PubMed PMID: 31987983.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Ocular blood flow as a clinical observation: Value, limitations and data analysis. AU - Harris,Alon, AU - Guidoboni,Giovanna, AU - Siesky,Brent, AU - Mathew,Sunu, AU - Verticchio Vercellin,Alice C, AU - Rowe,Lucas, AU - Arciero,Julia, Y1 - 2020/01/24/ PY - 2019/10/02/received PY - 2020/01/14/revised PY - 2020/01/16/accepted PY - 2020/1/29/pubmed PY - 2020/1/29/medline PY - 2020/1/29/entrez KW - Artificial intelligence KW - Glaucoma KW - Mathematical models KW - Ocular blood flow KW - Vascular risk factors SP - 100841 EP - 100841 JF - Progress in retinal and eye research JO - Prog Retin Eye Res N2 - 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. SN - 1873-1635 UR - https://www.unboundmedicine.com/medline/citation/31987983/Ocular_blood_flow_as_a_clinical_observation:_Value,_limitations_and_data_analysis L2 - https://linkinghub.elsevier.com/retrieve/pii/S1350-9462(20)30013-6 DB - PRIME DP - Unbound Medicine ER -
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