PCOMPBIOL-D-25-00649

Graph-Enhanced Deep Learning for Diabetic Retinopathy Diagnosis: A Quality-Aware and Uncertainty-Driven Approach

PLOS Computational Biology

Dear Dr. Khan,

Thank you for submitting your manuscript to PLOS Computational Biology. After careful consideration, we feel that it has merit but does not fully meet PLOS Computational Biology's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

​Please submit your revised manuscript within 60 days Sep 15 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at ploscompbiol@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pcompbiol/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter

We look forward to receiving your revised manuscript.

Kind regards,

Piero Fariselli

Academic Editor

PLOS Computational Biology

Jennifer Flegg

Section Editor

PLOS Computational Biology

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: • Add the fullform of CNN and DR in the abstract.

• In the abstract add more information about the results.

• Formatting of the introduction is not correct. Starting is of single column and rest is of two column.

• The introduction should provide a stronger motivation for the study by clearly stating the research gap it addresses.

• Format the paper correctly.

• The objectives of the research should be explicitly outlined at the end of the introduction.

• At the end of the introduction add the organization of the paper.

• In fig 1, add the details for the CNN layer.

• Caption of fig 2, is very long. Try to reduce it.

• In table 2, how the hyper-parameter values are selected.

• In table 4, add the year of publication.

• Author can read the following papers to increase the technical strength of the paper:

Ensemble deep learning and EfficientNet for accurate diagnosis of diabetic retinopathy

A Comprehensive Image Processing Framework for Early Diagnosis of Diabetic Retinopathy

Reviewer #2: This work proposes a deep learning approach of diabetic retinopathy diagnosis with uncertainty estimation along with quality assessment. Even though the approach seems promising, at this stage it is unclear how it particularly innovates state-of-the-art approaches. Points to be addressed:

- The authors in "Related works" section critically refer to previous works like Feng et. al. as they test on limited dataset and not in realistic settings, raising this as one of the significant shortcomings. Nonetheless, the authors themselves test their model with only two standard datasets (APTOS2019 and Messidor-2). An external verification would help overcome the limitations highlighted in existing state-of-the-art methods.

- The role of data augmentation procedures (CLAHE, Ben Graham and rotations, flips, brightness adjustments) is not clear in the results. The authors mention as an advantage of their model (in the Introduction) not carrying out "extensive preprocessing," but they in fact make significant use of such preprocessing operations. It is undefined whether they use such procedures for comparison or as central constituents of their pipeline, defying their claim of "minimal preprocessing.

- Explainability: a very brief explainability analysis is provided. The authors should clearly analyze how such regions change in alignment with severity outcome of DR as well as clearly mention their clinically relevant implication. It is stated in far too little detail.

- There is not enough detail in uncertainty estimation description to reproduce. There is a need for clear description of implementation with exact parameters and seed selection settings.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: None

Reviewer #2: None

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Reviewer #1: No

Reviewer #2: No

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Reproducibility:

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PCOMPBIOL-D-25-00649R1

Graph-Enhanced Deep Learning for Diabetic Retinopathy Diagnosis: A Quality-Aware and Uncertainty-Driven Approach

PLOS Computational Biology

Dear Dr. Khan,

Thank you for submitting your manuscript to PLOS Computational Biology. After careful consideration, we feel that it has merit but does not fully meet PLOS Computational Biology's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript within 30 days Nov 23 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at ploscompbiol@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pcompbiol/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

* A rebuttal letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. This file does not need to include responses to formatting updates and technical items listed in the 'Journal Requirements' section below.

* A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

* An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Piero Fariselli

Academic Editor

PLOS Computational Biology

Jennifer Flegg

Section Editor

PLOS Computational Biology

Journal Requirements:

1) Please upload Figure 5 as a separate Figure file in .tif or .eps format. For more information about how to convert and format your figure files please see our guidelines: 

https://journals.plos.org/ploscompbiol/s/figures

Note: If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Reviewers' comments:

Reviewer's Responses to Questions

Reviewer #1: The author addressed all the previous comments but still some issues are pending

• Check my previous comments. Some comments are not addressed correctly.

• The abstract needs to be rewritten to point out significance and impact of the paper.

• In the related work, it is recommended to refer the contribution made by the researchers and the novelty of the research.

• I recommend that the authors add some more current articles to improve the paper's overall quality. The preparation of a comparative analysis of the current publications on this subject should also be included.

• Avoid presenting with lengthy paragraph.

• The references section lacks key foundational or recent works that are crucial to the topic.

• Ensure that all cited works are relevant and up-to-date to reflect the current state of research.

• A more balanced mix of theoretical and empirical studies in the citations would strengthen the paper.

• Finally, a final proof-reading is highly suggested, in order to correct some typos.

Reviewer #2: I find that the authors have responded well to my concerns. They added an external validation set, which strengthens the consistency and generalizability of their method; indeed the method still performs well. They also better explained the preprocess problem making their statements more clear/robust On the explainability section, they expanded the description and linked it to the clinical interpretability. Regarding the uncertainty estimation part, they explained the environment for their implementation, making it possible to reproduce their approach.

I think now the article is clearer, more complete, and methodologically adequate.

I recommend adding a README file to the GitHub repository to enhance the reproducibility of the work. I would include instructions on installing dependencies, preparing the dataset, running the code, and reproducing the main results.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: None

Reviewer #2: None

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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Reproducibility:

To enhance the reproducibility of your results, we recommend that authors of applicable studies deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Dear Dr. Khan,

We are pleased to inform you that your manuscript 'Graph-Enhanced Deep Learning for Diabetic Retinopathy Diagnosis: A Quality-Aware and Uncertainty-Driven Approach' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Piero Fariselli

Academic Editor

PLOS Computational Biology

Jennifer Flegg

Section Editor

PLOS Computational Biology

***********************************************************

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #2: The authors have fully addressed all comments and implemented all requested changes.

**********

Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

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PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No