Evidence map›Paper›PMID 40478592›Full record

SynthesisTranslational vision science & technology2025

Current Applications of Artificial Intelligence for Fuchs Endothelial Corneal Dystrophy: A Systematic Review.

Siyin Liu, Lynn Kandakji, Aleksander Stupnicki, Dayyanah Sumodhee, Marcello T Leucci, Scott Hau, Shafi Balal, Arthur Okonkwo, Ismail Moghul, Sandor P Kanda and 7 more

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Translational vision science & technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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

17 authors.

Siyin LiuUniversity College London Institute of Ophthalmology, London, UK.
Lynn KandakjiUniversity College London Institute of Ophthalmology, London, UK.
Aleksander StupnickiUniversity College London Medical School, London, UK.
Dayyanah SumodheeUniversity College London Institute of Ophthalmology, London, UK.
Marcello T LeucciMoorfields Eye Hospital NHS Foundation Trust, London, UK.
Scott HauMoorfields Eye Hospital NHS Foundation Trust, London, UK.
Shafi BalalUniversity College London Institute of Ophthalmology, London, UK.
Arthur OkonkwoMoorfields Eye Hospital NHS Foundation Trust, London, UK.
Ismail MoghulUniversity College London Institute of Ophthalmology, London, UK.
Sandor P KandaMoorfields Eye Hospital NHS Foundation Trust, London, UK.
Bruce D AllanUniversity College London Institute of Ophthalmology, London, UK.
Dan M GoreMoorfields Eye Hospital NHS Foundation Trust, London, UK.
Kirithika MuthusamyUniversity College London Institute of Ophthalmology, London, UK.
Alison J HardcastleUniversity College London Institute of Ophthalmology, London, UK.
Alice E DavidsonUniversity College London Institute of Ophthalmology, London, UK.
Petra LiskovaDepartment of Paediatrics and Inherited Metabolic Disorders, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic.
Nikolas PontikosUniversity College London Institute of Ophthalmology, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Fuchs endothelial corneal dystrophy (FECD) is a common, age-related cause of visual impairment. This systematic review synthesizes evidence from the literature on artificial intelligence (AI) models developed for the diagnosis and management of FECD. Methods: We conducted a systematic literature search in MEDLINE, PubMed, Web of Science, and Scopus from January 1, 2000, to June 31, 2024. Full-text studies utilizing AI for various clinical contexts of FECD management were included. Data extraction covered model development, predicted outcomes, validation, and model performance metrics. We graded the included studies using the Quality Assessment of Diagnostic Accuracies Studies 2 tool. This review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations. Results: Nineteen studies were analyzed. Primary AI algorithms applied in FECD diagnosis and management included neural network architectures specialized for computer vision, utilized on confocal or specular microscopy images, or anterior segment optical coherence tomography images. AI was employed in diverse clinical contexts, such as assessing corneal endothelium and edema and predicting post-corneal transplantation graft detachment and survival. Despite many studies reporting promising model performance, a notable limitation was that only three studies performed external validation. Bias introduced by patient selection processes and experimental designs was evident in the included studies. Conclusions: Despite the potential of AI algorithms to enhance FECD diagnosis and prognostication, further work is required to evaluate their real-world applicability and clinical utility. Translational Relevance: This review offers critical insights for researchers, clinicians, and policymakers, aiding their understanding of existing AI research in FECD management and guiding future health service strategies.

Indexed as

Artificial IntelligenceFuchs' Endothelial DystrophyHumansTomography, Optical Coherence

Identifiers

PMID40478592
PMCPMC12155719

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.