ReviewClinical ophthalmology (Auckland, N.Z.)2022
Artificial Intelligence Analysis of Biofluid Markers in Age-Related Macular Degeneration: A Systematic Review.
Review in Clinical ophthalmology (Auckland, N.Z.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 13 citations in OpenAlex.
- Systematic Analysis of Plasma-Derived Extracellular Vesicle-Enriched Samples in Age-Related Macular Degeneration Reveals Oxidative Stress Linked to Altered Lipid and Protein Profiles.Journal of extracellular biology · 2026Article
- Molecular mechanisms and intervention strategies for age-related macular degeneration.International journal of ophthalmology · 2026Review
- Ocular involvement in newly diagnosed pediatric leukemia: A systematic review and meta-analysis.Pediatric investigation · 2025Review
- Artificial Intelligence Applications in Decision-Making for Disease Management:Sultan Qaboos University medical journal · 2025Article
- Role of Oxidative Stress and Inflammation in Age Related Macular Degeneration: Insights into the Retinal Pigment Epithelium (RPE).International journal of molecular sciences · 2025Review
- Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Article
- Evaluating the long-term biological stability of cytokine biomarkers in ocular fluid samples.BMJ open ophthalmology · 2023Article
- Systematic Review of Neovascular Age-Related Macular Degeneration Disease Activity Criteria Use to Shorten, Maintain or Extend Treatment Intervals with Anti-VEGF in Clinical Trials: Implications for Clinical Practice.Ophthalmology and therapy · 2023Review
- Recent Advances in Two-Dimensional MXene-Based Electrochemical Biosensors for Sweat Analysis.Molecules (Basel, Switzerland) · 2023Review
- Variability of Replicates of Intraocular Inflammatory Biomarkers in Ocular Fluid Samples Analyzed with Multiplex Assays.Clinical ophthalmology (Auckland, N.Z.) · 2023Article
- Short exposure to photo-oxidative damage triggers molecular signals indicative of early retinal degeneration.Frontiers in immunology · 2023Article
- Decreased Circulating Very Small Low-Density Lipoprotein is Likely Causal for Age-Related Macular Degeneration.Ophthalmology scienceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.