ReviewClinical ophthalmology (Auckland, N.Z.)2022
The Application of Artificial Intelligence in the Analysis of Biomarkers for Diagnosis and Management of Uveitis and Uveal Melanoma: 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 7 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
7 citing papers in PubMed.
- Tubercular Uveitis: Multimodal Imaging, Immunologic Stratification, and a Practical Framework for Diagnosis and Management.Clinical ophthalmology (Auckland, N.Z.) · 2026Review
- PICO-based assessment and categorization of evidence for digital health interventions: an inductive framework development.Frontiers in digital health · 2026Review
- GLUT-1-negative Choroidal Malignant Melanoma with Liver Metastasis Recurrence 12 Years after Surgery without FDG Accumulation in a Recurrent Lesion on FDG-PET/CT.Internal medicine (Tokyo, Japan) · 2025Article
- 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
- Predicting Malignant Transformation of Choroidal Nevi Using Machine Learning.Research square · 2023Article
- Evaluating the long-term biological stability of cytokine biomarkers in ocular fluid samples.BMJ open ophthalmology · 2023Article
- Variability of Replicates of Intraocular Inflammatory Biomarkers in Ocular Fluid Samples Analyzed with Multiplex Assays.Clinical ophthalmology (Auckland, N.Z.) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This study aims to identify the available literature describing the utilization of artificial intelligence (AI) as a clinical tool in uveal diseases. Methods: A comprehensive literature search was conducted in 5 electronic databases, finding studies relating to AI and uveal diseases. Results: After screening 10,258 studies,18 studies met the inclusion criteria. Uveal melanoma (44%) and uveitis (56%) were the two uveal diseases examined. Ten studies (56%) used complex AI, while 13 studies (72%) used regression methods. Lactate dehydrogenase (LDH), found in 50% of studies concerning uveal melanoma, was the only biomarker that overlapped in multiple studies. However, 94% of studies highlighted that the biomarkers of interest were significant. Conclusion: This study highlights the value of using complex and simple AI tools as a clinical tool in uveal diseases. Particularly, complex AI methods can be used to weigh the merit of significant biomarkers, such as LDH, in order to create staging tools and predict treatment outcomes.
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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.