Evidence map›Paper›PMID 37421481›Full record

ArticleGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2024

Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review.

Aidan Pucchio, Saffire H Krance, Daiana R Pur, Jasmine Bhatti, Arshpreet Bassi, Karthik Manichavagan, Shaily Brahmbhatt, Ishita Aggarwal, Priyanka Singh, Aleena Virani and 3 more

Abstract readScoping Review
PubMed Publisher
In one paragraph

Article in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Aidan PucchioDepartment of Ophthalmology, Queen's University, Kingston, ON, Canada.ORCID http://orcid.org/0000-0001-8884-1338
Saffire H KranceSchulich School of Medicine and Dentistry, Western University, London, ON, Canada.ORCID http://orcid.org/0000-0001-6679-4067
Daiana R PurSchulich School of Medicine and Dentistry, Western University, London, ON, Canada.
Jasmine BhattiSchulich School of Medicine and Dentistry, Western University, London, ON, Canada.
Arshpreet BassiSchulich School of Medicine and Dentistry, Western University, London, ON, Canada.
Karthik ManichavaganQueens School of Medicine, Kingston, ON, Canada.
Shaily BrahmbhattSchulich School of Medicine and Dentistry, Western University, London, ON, Canada.
Ishita AggarwalQueens School of Medicine, Kingston, ON, Canada.
Priyanka SinghSchulich School of Medicine and Dentistry, Western University, London, ON, Canada.
Aleena ViraniSchulich School of Medicine and Dentistry, Western University, London, ON, Canada.
Meagan StanleyWestern University, London, ON, Canada.
Rafael N MirandaThe Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.ORCID http://orcid.org/0000-0003-2173-0021
Tina FelfeliThe Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada. tina.felfeli@mail.utoronto.ca.ORCID http://orcid.org/0000-0002-0927-3086

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis scoping review summarizes the applications of artificial intelligence (AI) and bioinformatics methodologies in analysis of ocular biofluid markers. The secondary objective was to explore supervised and unsupervised AI techniques and their predictive accuracies. We also evaluate the integration of bioinformatics with AI tools.

methodsThis scoping review was conducted across five electronic databases including EMBASE, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics were included.

resultsA total of 10,262 articles were retrieved from all databases and 177 studies met the inclusion criteria. The most commonly studied ocular diseases were diabetic eye diseases, with 50 papers (28%), while glaucoma was explored in 25 studies (14%), age-related macular degeneration in 20 (11%), dry eye disease in 10 (6%), and uveitis in 9 (5%). Supervised learning was used in 91 papers (51%), unsupervised AI in 83 (46%), and bioinformatics in 85 (48%). Ninety-eight papers (55%) used more than one class of AI (e.g. > 1 of supervised, unsupervised, bioinformatics, or statistical techniques), while 79 (45%) used only one. Supervised learning techniques were often used to predict disease status or prognosis, and demonstrated strong accuracy. Unsupervised AI algorithms were used to bolster the accuracy of other algorithms, identify molecularly distinct subgroups, or cluster cases into distinct subgroups that are useful for prediction of the disease course. Finally, bioinformatic tools were used to translate complex biomarker profiles or findings into interpretable data.

conclusionAI analysis of biofluid markers displayed diagnostic accuracy, provided insight into mechanisms of molecular etiologies, and had the ability to provide individualized targeted therapeutic treatment for patients. Given the progression of AI towards use in both research and the clinic, ophthalmologists should be broadly aware of the commonly used algorithms and their applications. Future research may be aimed at validating algorithms and integrating them in clinical practice.

Indexed as

Artificial IntelligenceBiomarkersComputational BiologyEye DiseasesHumansBiomarkersAqueous humorArtificial intelligenceBioinformaticsBiomarkersMachine learningOphthalmologyVitreous humor

Identifiers

PMID37421481

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.