-->PONE-D-25-59697-->-->Automated spermatogenic staging in PAS-stained testes of Sprague-Dawley rats using a deep learning model for nomal and atrophied tissues-->-->PLOS One

Dear Dr. Son,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’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.-->-->

While it is an very interesting study, the robustness of the predictions shall be made basing on multiple observations from different slides of atrophied tissues.-->-->Low precision score in predicting the Stage V should be justified and the possible technical reasons should be described thoroughly-->-->Provide details on biological processes in which multi-class staging is carried out and justify its applicability and that it is better than the binary staging process.-->-->Provide complete technical details and restrict to the results obtained without overemphasising.-->-->Strong rationale for carrying out this study and justification that is based on previous studies should be included in the introduction. Moreover, the superiority of the assessment made over the existing ones should also be thoroughly presented.-->-->Discussion should be robust and avoid reiterating the observed results. Conclusions should focus on what are the benefits of this study in research and clinical terms and what are the future possibilities for the results presented in this study.-->-->

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Academic Editor

PLOS One

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Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. -->

Reviewer #1: Partly

Reviewer #2: Partly

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-->2. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #1: Yes

Reviewer #2: Yes

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-->3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: Yes

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-->4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.-->

Reviewer #1: Yes

Reviewer #2: No

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-->5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #1: The present study presents a region-based convolutional neural network designed to predict spermatogenic stages from PAS-stained testicular tissue sections. The authors have incorporated robust statistical procedures and report that the model can distinguish 14 stages with an average accuracy of 0.86. Additionally, the framework appears capable of quantitative grading by estimating proportional differences between atrophied and normal seminiferous tubules. Although the manuscript presents the data nicely, the placement of figure descriptions within the main text disrupts the logical flow of the manuscript. All figure legends should be positioned alongside the respective figures or compiled at the end of the discussion section.

Below are a few concerns regarding the model development and interpretation:

Major concerns

1. The manuscript does not sufficiently explain how the model mitigates overfitting or handles class imbalance. This issue is particularly relevant because the model was trained using images derived from only a single atrophic slide, raising concerns about the generalizability and robustness of the classifier.

2. As shown in Figure 4, while most stages show satisfactory performance, the average precision for stage V remains below 0.7. The authors should provide a justification or discuss potential biological or technical reasons underlying this discrepancy.

3. Given the substantial morphological similarity among spermatogenic stages, developing a multi-class classifier is inherently challenging. Are there any other existing image-based/histology-based models for a similar multi-class classification? The authors should also clarify how a multi-class stage prediction is a better approach than a preliminary binary classification (atrophy vs. normal).

Minor concerns/Points to add in the discussion

1. The authors should briefly explain the use of PAS staining over H&E for identifying spermatogenic stages. Adding this clarification will contextualize the methodological choice and help readers, especially those less familiar with testicular histology, better understand the rationale behind the selected staining approach.

2. A clearer explanation of how determining the precise stage of spermatogenesis contributes to clinical decision-making, particularly in infertility diagnostics or testicular pathology, would strengthen the manuscript.

3. Figure 7 shows differences between the model’s predictions and the literature for stages I, VI, and XII. These discrepancies require further clarification. The authors should elaborate on whether the inconsistencies arise from staining variation, annotation subjectivity, or intrinsic challenges in differentiating these stages.

Reviewer #2: The manuscript addresses the application of deep learning–based object detection for automated spermatogenic stage classification and atrophy detection in rat testes. While the study presents promising results, several aspects require clarification and improvement, particularly regarding dataset size, methodological transparency, statistical analysis, and interpretation of results.

Abstract

1.The abstract overemphasizes model performance without sufficiently contextualizing the limited sample size.

2.The number of animals and WSIs analyzed should be explicitly stated.

3.The term “atrophy grading” should be clarified as quantitative detection/counting rather than conventional histopathological grading.

4.Questions:

- How many rats and WSIs were included in the training, validation, and test datasets

- Were atrophied samples included during model training or used only for inference?

Introduction

1.The novelty of this study relative to previous AI-based spermatogenic staging studies is not clearly articulated.

2.The rationale for selecting an object detection framework (R-CNN family) over segmentation or classification-based approaches requires stronger justification.

3.Prior studies using deep learning for testicular histopathology should be discussed in more depth to better position the contribution of this work.

4.Questions:

- What specific limitations of existing automated spermatogenic staging methods does this study aim to overcome?

- Why was object detection considered more suitable than instance or semantic segmentation for this task?

Materials and Methods

1.The small dataset size (number of animals, WSIs, and annotated tubules) should be more explicitly described and discussed.

2.Details regarding annotation procedures, consensus building, and inter-pathologist variability are insufficient.

3.The data split strategy should be clarified to ensure that data from the same animal were not used across training and test sets.

4. The statistical methods used for stage frequency comparisons require a clearer description, including assumptions and multiple comparison handling.

5.Questions:

- How was annotation consistency ensured among pathologists?

- Were WSIs from the same rat separated across training, validation, and testing datasets?

- How was class imbalance among the 14 spermatogenic stages addressed during training?

Results

1.The relatively high mAR combined with lower stage-wise AP for certain stages suggests increased false positives and should be discussed quantitatively.

2.Lower performance for stages II–III, V, and XI warrants deeper analysis beyond morphological similarity.

3.The reported WSI inference time (~211 seconds) should be discussed in terms of feasibility for routine or large-scale toxicological studies.

4.Questions:

- Were confidence thresholds optimized for WSI-level inference?

- How consistent was model performance across individual animals?

-Was model robustness evaluated against staining or sectioning variability?

Discussion

1.The discussion partially reiterates results rather than critically interpreting them.

2.The limited number of animals (especially for WSI-level analysis) should be more explicitly acknowledged as a major limitation.

3.The implications for regulatory toxicology and standard non-clinical studies require clearer contextualization.

4.Questions:

- Could data augmentation or balanced sampling strategies improve performance for short-duration stages?

- How generalizable is the model to different ages, strains, or staining protocols?

6. Conclusion

1. The conclusion is overly long and contains repetitive statements already addressed in the Discussion.

2. It should be condensed to focus on the principal findings and their implications.

3. Claims regarding “objective standards” should be tempered given the limited dataset size.

4. Questions

- Can the authors explicitly state the main scientific contribution and the main practical contribution of this study?

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Reviewer #1: No

Reviewer #2: No

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Submitted filename: Plos_one_review.docx

Automated spermatogenic staging in periodic acid-Schiff-stained testes of Sprague–Dawley rats using a deep learning model for normal and atrophied tissues

PONE-D-25-59697R1

Dear Dr. Son,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Suresh Yenugu

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.-->

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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-->2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. -->

Reviewer #1: Yes

Reviewer #2: Yes

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-->3. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #1: Yes

Reviewer #2: Yes

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-->4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: Yes

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-->5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.-->

Reviewer #1: Yes

Reviewer #2: Yes

**********

-->6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #1: All the concerns are now addressed by the authors. Both major and minor points are discussed and included in the manuscript.

Reviewer #2: The authors have satisfactorily addressed all reviewer comments and revised the manuscript accordingly. The manuscript has improved substantially and is now suitable for publication

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Reviewer #1: Yes: Manjari Kiran

Reviewer #2: No

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