Evidence map›Paper›PMID 42260691›Full record

ArticleInternational journal of retina and vitreous2026

Code-free automated machine learning for OCT-based classification of vitreoretinal interface diseases.

Lorenzo Ferro Desideri, Enrico Bernardi, Carla Troyas, Yousif Subhi, Mehmet Cem Sabaner, Martin Zinkernagel, Rodrigo Anguita

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In one paragraph

Article in International journal of retina and vitreous, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Lorenzo Ferro DesideriDepartment of Ophthalmology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland. lorenzoferrodes@gmail.com.
Enrico BernardiDepartment of Ophthalmology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Carla TroyasMoorfields Eye Hospital NHS Foundation Trust, London, UK.
Yousif SubhiDepartment of Ophthalmology, Rigshospitalet, Glostrup, Denmark.
Mehmet Cem SabanerDepartment of Ophthalmology, Kastamonu University, Training and Research Hospital, Kastamonu, 37150, Turkey.
Martin ZinkernagelDepartment of Ophthalmology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Rodrigo AnguitaDepartment of Ophthalmology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDifferentiation of vitreoretinal interface disorders on optical coherence tomography (OCT) relies on expert interpretation and can be challenging in borderline cases. Automated machine learning (AutoML) platforms may enable clinician-driven artificial intelligence development without coding expertise. This study evaluated the performance of a code-free AutoML approach for OCT-based classification.

methodsIn this cross-sectional image classification study, 434 OCT B-scans from publicly available datasets were manually labeled into four categories: epiretinal membrane (ERM), lamellar macular hole (LMH), full-thickness macular hole (MH), and normal retina. Images were uploaded to a cloud-based AutoML platform (Google Cloud Vertex AI), which automatically performed data splitting (80% training, 10% validation, 10% test), model training, and optimization. Performance was assessed using precision, recall, average precision, and confusion matrix analysis.

resultsThe model achieved an overall average precision of 0.988, with precision and recall of 97.6%. MH and normal retina were classified with perfect precision and recall (100%). ERM showed high precision (100%) with slightly reduced recall (92.9%), while LMH demonstrated complete recall (100%) with lower precision (83.3%). Misclassifications were limited to anatomically related entities.

conclusionsCode-free AutoML enables accurate OCT-based classification of vitreoretinal interface disorders using a clinician-driven workflow. This approach may facilitate broader adoption of artificial intelligence in ophthalmology and support rapid clinical research prototyping.

Indexed as

Automated machine learningEpiretinal membraneImage classificationMacular holeOptical coherence tomographyVitreoretinal interface

Identifiers

PMID42260691
PMCPMC13474496

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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.