Evidence map›Paper›PMID 41361333›Full record

ArticleScientific reports2025

A self-supervised learning method for detection of retinitis pigmentosa and Stargardt disease.

Ali Karimi, Hamid Ahmadieh, Melika Sadeghi Tabrizi, Narsis Daftarian, Ali Farshian Abbasi, Sahba Fekri, Hadi Ghattan Kashani, Hemn Baghban Jaldian, Mahdi Yazdanpanah, Masoud Shariat Panahi and 1 more

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

The trial behind it

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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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

11 authors.

Ali KarimiSchool of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Hamid AhmadiehOphthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Melika Sadeghi TabriziSchool of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Narsis DaftarianExperimental Medicine, Department of Medicine, The University of British Columbia, Faculty of Medicine, Vancouver, British Columbia, Canada.
Ali Farshian AbbasiSchool of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Sahba FekriOphthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Hadi Ghattan KashaniApplied Artificial Intelligence Laboratory, University of Tehran, Tehran, Iran.
Hemn Baghban JaldianDepartments of Optometry, School of Rehabilitation, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Mahdi YazdanpanahAmirkabir University of Technology, Tehran, Iran.
Masoud Shariat PanahiSchool of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Hamideh SabbaghiOphthalmic Epidemiology Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran. sabbaghi.opt@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Retinitis pigmentosa (RP) and Stargardt Disease (STGD) are inherited retinal diseases that can seriously affect vision. In this study, we present a novel, two-phase self-supervised learning method that addresses the challenge of limited labeled data in medical image analysis. In the first phase, the model learns useful visual features from a large collection of unlabeled retinal images using self-supervised training. In the second phase, it is fine-tuned on a smaller set of labeled images for the classification of RP and STGD. Experimental results demonstrate that using 5844 unlabeled and 782 labeled fundus images showed that our method, based on the EfficientNet-B1 architecture, outperforms state-of-the-art supervised learning methods, achieving 98.15% accuracy and 99.68% AUC. The proposed method is flexible and scalable, making it well-suited for real-world applications where labeled data is scarce.

Indexed as

Image Processing, Computer-AssistedRetinitis PigmentosaStargardt DiseaseSupervised Machine LearningAlgorithmsFundus OculiHumansRetina

Identifiers

PMID41361333
PMCPMC12686504

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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.