Artificial Intelligence Analysis of Biofluid Markers in Age-Related Macular Degeneration: A Systematic Review

Aidan Pucchio; Saffire H Krance; Daiana R Pur; Rafael N Miranda; Tina Felfeli

doi:10.2147/OPTH.S377262

Artificial Intelligence Analysis of Biofluid Markers in Age-Related Macular Degeneration: A Systematic Review

Clin Ophthalmol. 2022 Aug 7:16:2463-2476. doi: 10.2147/OPTH.S377262. eCollection 2022.

Authors

Aidan Pucchio¹, Saffire H Krance², Daiana R Pur², Rafael N Miranda^{3

4}, Tina Felfeli^{3

4

5}

Affiliations

¹ School of Medicine, Queen's University, Kingston, ON, Canada.
² Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.
³ Toronto Health Economics and Technology Assessment Collaborative, Toronto, ON, Canada.
⁴ Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.
⁵ Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, ON, Canada.

Abstract

This systematic review explores the use of artificial intelligence (AI) in the analysis of biofluid markers in age-related macular degeneration (AMD). We detail the accuracy and validity of AI in diagnostic and prognostic models and biofluid markers that provide insight into AMD pathogenesis and progression. This review was conducted in accordance with the Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines. A comprehensive search was conducted across 5 electronic databases including Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, EMBASE, Medline, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics in AMD were included. Identified studies were assessed for risk of bias and critically appraised using the Joanna Briggs Institute Critical Appraisal tools. A total of 10,264 articles were retrieved from all databases and 37 studies met the inclusion criteria, including 15 cross-sectional studies, 15 prospective cohort studies, five retrospective cohort studies, one randomized controlled trial, and one case-control study. The majority of studies had a general focus on AMD (58%), while neovascular AMD (nAMD) was the focus in 11 studies (30%), and geographic atrophy (GA) was highlighted by three studies. Fifteen studies examined disease characteristics, 15 studied risk factors, and seven guided treatment decisions. Altered lipid metabolism (HDL-cholesterol, total serum triglycerides), inflammation (c-reactive protein), oxidative stress, and protein digestion were implicated in AMD development and progression. AI tools were able to both accurately differentiate controls and AMD patients with accuracies as high as 87% and predict responsiveness to anti-VEGF therapy in nAMD patients. Use of AI models such as discriminant analysis could inform prognostic and diagnostic decision-making in a clinical setting. The identified pathways provide opportunity for future studies of AMD development and could be valuable in the advancement of novel treatments.

Keywords: age-related macular degeneration; artificial intelligence; biofluid; diagnosis; pathogenesis.

Publication types

Review

Grants and funding

This research was in-part funded by Fighting Blindness Canada. This project did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.