The role of artificial intelligence in analysis of biofluid markers for diagnosis and management of glaucoma: A systematic review

Abstract

Purpose This review focuses on utility of artificial intelligence (AI) in analysis of biofluid markers in glaucoma. We detail the accuracy and validity of AI in the exploration of biomarkers to provide insight into glaucoma pathogenesis. Methods A comprehensive search was conducted across five electronic databases including Embase, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science. Studies pertaining to biofluid marker analysis using AI or bioinformatics in glaucoma were included. Identified studies were critically appraised and assessed for risk of bias using the Joanna Briggs Institute Critical Appraisal tools. Results A total of 10,258 studies were screened and 39 studies met the inclusion criteria, including 23 cross-sectional studies (59%), nine prospective cohort studies (23%), six retrospective cohort studies (15%), and one case-control study (3%). Primary open angle glaucoma (POAG) was the most commonly studied subtype (55% of included studies). Twenty-four studies examined disease characteristics, 10 explored treatment decisions, and 5 provided diagnostic clarification. While studies examined at entire metabolomic or proteomic profiles to determine changes in POAG, there was heterogeneity in the data with over 175 unique, differentially expressed biomarkers reported. Discriminant analysis and artificial neural network predictive models displayed strong differentiating ability between glaucoma patients and controls, although these tools were untested in a clinical context. Conclusion The use of AI models could inform glaucoma diagnosis with high sensitivity and specificity. While insight into differentially expressed biomarkers is valuable in pathogenic exploration, no clear pathogenic mechanism in glaucoma has emerged.

Variability of Replicates of Intraocular Inflammatory Biomarkers in Ocular Fluid Samples Analyzed with Multiplex Assays

“…The use of biomarker analysis with artificial intelligence has been demonstrated in many ophthalmic diseases, such as glaucoma, uveitis, uveal melanoma, age-related macular degeneration, corneal and ocular surface diseases, and retinal occlusive diseases. [8][9][10][11][12] Herein, we evaluated the variability of intraocular inflammatory biomarkers in multiplex assays of intraocular biofluid samples.…”

Evaluating the long-term biological stability of cytokine biomarkers in ocular fluid samples

“…Cytokines are low-molecular-weight glycoprotein molecules produced by both immune and non-immune cells that serve as indicators of normal biological processes, pathogenic processes or pharmacological responses to a therapeutic intervention 1. Recently, there has been a growing interest in using cytokines in aqueous humour and vitreous collected via anterior chamber (AC) paracentesis and vitreous sampling, respectively, as surrogate markers to understand disease pathogenesis, monitor disease progression and assess treatment response 2–12. Given the emerging research and clinical practices based on cytokine analysis, it is imperative to ensure that methodologies to measure cytokines are robust.…”

“…1 Recently, there has been a growing interest in using cytokines in aqueous humour and vitreous collected via anterior chamber (AC) paracentesis and vitreous sampling, respectively, as surrogate markers to understand disease pathogenesis, monitor disease progression and assess treatment response. [2][3][4][5][6][7][8][9][10][11][12] Given the emerging research and clinical practices based on cytokine analysis, it is imperative to ensure that methodologies to measure cytokines are robust. Any conclusions drawn from poor handling of the ocular samples and unreliable method of cytokine quantification could potentially result in misled clinical decisions with negative outcomes.…”