Evidence map›Paper›PMID 36216736›Full record

ReviewJournal of optometry2022

The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases.

Lorenzo Ferro Desideri, Carola Rutigliani, Paolo Corazza, Andrea Nastasi, Matilde Roda, Massimo Nicolo, Carlo Enrico Traverso, Aldo Vagge

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of optometry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Trial
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  8. Review on Advancement of AI in Cell Engineering and Molecular Biology.Methods in molecular biology (Clifton, N.J.) · 2025
    Review
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  11. Article
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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

8 authors.

Lorenzo Ferro DesideriUniversity Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy. Electronic address: lorenzoferrodes@gmail.com.
Carola RutiglianiVita-Salute San Raffaele University, Milan, Italy.
Paolo CorazzaUniversity Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy.
Andrea NastasiVita-Salute San Raffaele University, Milan, Italy.
Matilde RodaOphthalmology Unit, Department of Experimental, Diagnostic and Specialty Medicine (DIMES), Alma Mater Studiorum University of Bologna and S.Orsola-Malpighi Teaching Hospital, Bologna, Italy.
Massimo NicoloUniversity Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy.
Carlo Enrico TraversoUniversity Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy.
Aldo VaggeUniversity Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, the role of artificial intelligence (AI) and deep learning (DL) models is attracting increasing global interest in the field of ophthalmology. DL models are considered the current state-of-art among the AI technologies. In fact, DL systems have the capability to recognize, quantify and describe pathological clinical features. Their role is currently being investigated for the early diagnosis and management of several retinal diseases and glaucoma. The application of DL models to fundus photographs, visual fields and optical coherence tomography (OCT) imaging has provided promising results in the early detection of diabetic retinopathy (DR), wet age-related macular degeneration (w-AMD), retinopathy of prematurity (ROP) and glaucoma. In this review we analyze the current evidence of AI applied to these ocular diseases, as well as discuss the possible future developments and potential clinical implications, without neglecting the present limitations and challenges in order to adopt AI and DL models as powerful tools in the everyday routine clinical practice.

Indexed as

GlaucomaOphthalmologyArtificial IntelligenceDiagnostic Techniques, OphthalmologicalHumansInfant, NewbornRetinaArtificial intelligenceDeep learningGlaucomaMachine learningMaculaMedical retinaOptical coherence tomographyTelemedicine

Identifiers

PMID36216736
PMCPMC9732476

What OpenQuestion holds

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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.