SEARCHSYSTEMATIC_REVIEWglaucoma, ophthalmology

systematic_review·glaucoma, ophthalmology, systematic review, biomedical research, clinical trial·PMC10469503

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

European Journal of Ophthalmology · 6 authors, 5 centres

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TL;DR

This systematic review analyzed 39 studies on using artificial intelligence to interpret biofluid biomarkers for glaucoma. It found that AI models can distinguish glaucoma patients from controls with high sensitivity and specificity, but the specific biomarkers identified were highly variable across studies.

Full summary

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This systematic review included 39 studies examining the use of artificial intelligence (AI) or bioinformatics to analyze biofluid markers in glaucoma. The studies were predominantly cross-sectional (59%) and prospective cohort (23%), with primary open-angle glaucoma as the most common subtype. However, significant heterogeneity was observed in the reported differentially expressed biomarkers, with over 175 unique markers noted across studies and minimal replication. The review highlights that while AI shows promise for diagnostic applications, these tools have not been tested in clinical contexts.

STUDY TYPE

systematic_review

SPECIALTY

glaucoma

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AI pipeline

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100% · A

ORIGINAL PAPER

PMC10469503

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

European Journal of Ophthalmology

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ABSTRACT

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. Keywords: artificial intelligence, biofluid, glaucoma, diagnosis, pathogenesis

CITATION EXPORT

Aidan Pucchio, Saffire Krance, Daiana R Pur et al.. The role of artificial intelligence in analysis of biofluid markers for diagnosis and management of glaucoma: A systematic review. European Journal of Ophthalmology. (2022). https://doi.org/10.1177/11206721221140948

AUTHORS

6 · 5 CENTRES

Aidan Pucchio, Saffire Krance, Daiana R Pur, Arshpreet Bassi, Rafael Miranda, Tina Felfeli

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