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Using deep learning to identify inherited retinal diseases based on wide-field retinal imaging data.

Code ↔ Paper

7 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 7 matches
  1. [1] § 2 Methods › 2.4 Deep learning models ↔ KcrossValidationProcess.py, lines 32–93 · score 0.62 · cross entropy loss, subsets, optimizer, weights, fold, cropping
  2. [2] § 2 Methods › 2.4 Deep learning models ↔ Augmentations.py, lines 5–16 · score 0.61 · horizontal flips, translations, vertical, probabilistically, augmentation, training
  3. [3] § 2 Methods › 2.6 Performance evaluation and statistical analysis ↔ MainProcess.py, lines 89–162 · score 0.57 · confusion matrices, F1 score, recall, AUC, precision, accuracy
  4. [4] § 2 Methods › 2.6 Performance evaluation and statistical analysis ↔ Utils.py, lines 103–121 · score 0.56 · confusion matrices, F1 score, recall, AUC, precision, accuracy
  5. [5] § 2 Methods › 2.4 Deep learning models ↔ Utils.py, lines 124–133 · score 0.55 · 47.5–72.5 %, center cropping
  6. [6] § 2 Methods › 2.4 Deep learning models ↔ Main.py, lines 32–38 · score 0.51 · cross entropy loss, weights, cropping, batch, training, class
  7. [7] § 3 Results ↔ Utils.py, lines 103–121 · score 0.50 · confusion matrices, F1 score, AUC, precision, accuracy, predictive

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 416 lines · 15 KB · no license · 3 matches

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It can be read at the source: Utils.py.

Overview

Authors: Leo Joskowicz1, Tim Buchbinder1, Eldan Chodorov1, Assaf Hoogi2, Katherine Matos3, Antonio Rivera3, Dror Sharon3, Eyal Banin3, Jaime Levy3
ORCID iDs: Assaf Hoogi, Jaime Levy
  1. School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
  2. School of Computer Science, Ariel University, Ariel, Israel,‌‌
  3. Department of Ophthalmology, Hadassah Medical Center, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
Institutions: Hebrew University of Jerusalem (Israel); Ariel University (Israel); Hadassah Medical Center (Israel)
Journal: PloS one, volume 21, issue 5, article e0348866
Dates: received 2 December 2024; accepted 22 April 2026; published online 11 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pone.0348866 · PMID 42113772 · PMCID PMC13160341 · OpenAlex W7160864879
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other (modality), human (organism), clinical / translational (subfield)
Methods: Statistics, Machine learning
MeSH: Deep Learning*, Retina*, Retinal Diseases*, Convolutional Neural Networks, Fundus Oculi, Humans, Image Processing, Computer-Assisted, Optical Imaging, Retrospective Studies (* major topic)
Topic: Retinal Imaging and Analysis (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Citations: cited by 1 paper (Europe PMC); 34 references in the paper

Abstract

Objective: To evaluate a novel image-based deep learning method for the automated identification of inherited retinal diseases (IRDs) and to explore the feasibility of predicting selected causative gene groups using a multimodal analysis of wide-field fundus autofluorescence (FAF) and pseudocolor fundus (pCF) images.

Design: The method was evaluated using a retrospective dataset of patient studies containing FAF and pCF images, as well as genetic tests for IRD.

Participants: Patients with confirmed IRD for which both wide-field FAF and pCF images and genetic tests for IRD performed at Hadassah University Medical Center were included. The dataset consisted of 409 patients (330 patients with IRD with the 25 most commonly affected genes in our population and patients without IRD, and 79 patients without IRD).

Methods: Nine EfficientNet-V2-m convolutional neural networks were trained for the following three classification tasks: a binary IRD vs. non-IRD classification, and classification into two groups of five causative genes (Groups 1 and 2). For each task, three models were trained on the FAF images only, the pCF images only, and both the FAF and pCF images. The performance of the models was then evaluated and compared using 5-fold cross-validation.

Main outcome measures: Accuracy, precision, F1 scores, AUC, and confusion matrices.

Results: The multimodal classification models that were trained on both the FAF and pCF images yielded the best results. The binary classification model had a mean (±SD) accuracy of 0.95 ± 0.01, a mean precision of 0.92 ± 0.01, and a mean F1 score of 0.90 ± 0.02. The Group 1 classification model had a mean accuracy of 0.92 ± 0.03, a mean precision of 0.93 ± 0.03, and a mean F1 score of 0.89 ± 0.03. Finally, the Group 2 classification model had a mean accuracy of 0.85 ± 0.03, a mean precision of 0.87 ± 0.04, and a mean F1 score of 0.83 ± 0.04.

Conclusions: Our results indicate that determining whether a patient has IRD can be performed with high accuracy within this retrospective cohort based on FAF and pCF images using image-based deep learning classifiers. This image-based approach may assist clinicians during the patient’s initial visit by providing decision support prior to genetic testing. It may also help prioritize patients for genetic workup, particularly in settings in which genetic testing is not readily available. Further prospective and external validation is required before clinical implementation.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

timshik/Deep-learning-based-identification-of-inherited-retinal-disease-using-wide-field-retina-imaging

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: d4196159f13abe62b7baba1111d6fd5fac070876, 24 October 2024
Languages: Python (17)
Size: 19 files, 17 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: PyTorch (11 files), NumPy (7 files), Matplotlib (5 files), OpenCV (4 files), scikit-learn (4 files), pandas (2 files), Pillow (2 files), SciPy (2 files), imageio (1 file), imbalanced-learn (1 file), MONAI (1 file), Plotly (1 file), scikit-image (1 file), seaborn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
18 files, not copied: shown from their source

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At the source: github.com/timshik/Deep-learning-based-identification-of-inherited-retinal-disease-using-wide-field-retina-imaging

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 17 scripts, each with its path and the digest of its content;
  • 7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability

All data supporting the findings of this study are available within the manuscript and in supplementary materials. Due to patient privacy and ethical restrictions, raw retinal images cannot be made publicly available. However, all aggregated performance metrics, confusion matrices, model architecture details, and hyperparameters are provided within the manuscript and supplementary materials. The code is available at: https://github.com/timshik/Deep-learning-based-identification-of-inherited-retinal-disease-using-wide-field-retina-imaging.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 9 MeSH terms, 29 references.

Cite

This paper

Joskowicz, L., Buchbinder, T., Chodorov, E., Hoogi, A., Matos, K., Rivera, A., Sharon, D., Banin, E., & Levy, J. (2026). Using deep learning to identify inherited retinal diseases based on wide-field retinal imaging data. PloS one, 21(5), e0348866. https://doi.org/10.1371/journal.pone.0348866

BibTeX

@article{joskowicz2026using,
author = {Joskowicz, Leo and Buchbinder, Tim and Chodorov, Eldan and Hoogi, Assaf and Matos, Katherine and Rivera, Antonio and Sharon, Dror and Banin, Eyal and Levy, Jaime},
title = {{Using deep learning to identify inherited retinal diseases based on wide-field retinal imaging data}},
journal = {PloS one},
year = {2026},
month = may,
volume = {21},
number = {5},
pages = {e0348866},
publisher = {PLOS},
issn = {1932-6203},
doi = {10.1371/journal.pone.0348866},
url = {https://doi.org/10.1371/journal.pone.0348866},
pmid = {42113772},
pmcid = {PMC13160341}
}

RIS

TY - JOUR
AU - Joskowicz, Leo
AU - Buchbinder, Tim
AU - Chodorov, Eldan
AU - Hoogi, Assaf
AU - Matos, Katherine
AU - Rivera, Antonio
AU - Sharon, Dror
AU - Banin, Eyal
AU - Levy, Jaime
TI - Using deep learning to identify inherited retinal diseases based on wide-field retinal imaging data
T2 - PloS one
J2 - PLoS One
PY - 2026
DA - 2026/05/11
VL - 21
IS - 5
SP - e0348866
SN - 1932-6203
PB - PLOS
DO - 10.1371/journal.pone.0348866
UR - https://doi.org/10.1371/journal.pone.0348866
LA - en
ER -

CSL-JSON

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