https://orcid.org/0000-0002-4974-1569
Peer Review History
| Original SubmissionJune 28, 2025 |
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Dear Dr. Ali, 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. Please submit your revised manuscript by Sep 14 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 plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.
If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols . We look forward to receiving your revised manuscript. Kind regards, Javed Rashid, PhD Academic Editor PLOS ONE Journal requirements: When submitting your revision, we need you to address these additional requirements. 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and 2. Thank you for stating the following financial disclosure: [The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number “NBU-FFR-2025-2990-05“]. Please state what role the funders took in the study. If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript." If this statement is not correct you must amend it as needed. Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf. 3. Please include a separate caption for each figure in your manuscript. 4. 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. Additional Editor Comments: Based on a thorough review of the manuscript "Deep-Fed: A Comprehensive Solution for Precise Bone Fracture Identification in Athletes" (Manuscript Number: PONE-D-25-34607), the following major review comments are suggested for revision to enhance the quality, clarity, and scientific rigor of the submission: 1. Abstract Completeness: The abstract is significantly truncated, limiting the ability to assess the full scope of the study, including methodology, results, and implications. The authors should provide a complete abstract (within the 300-word limit per PLOS ONE guidelines) that concisely summarizes the background, objectives, methods (e.g., federated deep learning with ConvNet and FractureNet), key results (e.g., accuracy rates across all datasets), and the significance of Deep-Fed for sports medicine. 2. Data Availability Statement: The manuscript mentions three datasets (Deep-I, Deep-II, Deep-III) but lacks a clear Data Availability Statement as required by PLOS ONE. The authors must confirm whether the data underlying the findings are fully available without restriction and provide specific details (e.g., repository links, dataset names, or a statement if data are within the manuscript/supporting files). If data sharing is restricted, a detailed explanation and contact information for access approval must be included. 3. Mathematical and Algorithmic Clarity: The manuscript contains incomplete or erroneous mathematical expressions and algorithms (e.g., truncated equations on Pages 19, 20, 24, and incomplete Algorithm 4 on Page 27). The authors should revise and complete all mathematical formulations (e.g., normalization, convolution, pooling operations) and provide full, pseudocode for Algorithms 1-4, ensuring they are executable and aligned with the described methodology. 4. Consistency in Results: The reported accuracy rates vary across the manuscript (e.g., 96.23%, 97.11%, 96.73% on Page 10 vs. 95.6%, 97.34%, 96.12% on Page 30 vs. 97.23%, 98.11%, 98.23% on Page 33). The authors need to reconcile these discrepancies, provide a single, consistent set of results, and justify any variations through additional analysis or experimental conditions. 5. Literature Comparison and Table Completion: Table 1 on Page 16 is incomplete, with only one reference ([25]) detailed. The authors should expand this table to include all cited works (e.g., [23], [24]) with their core methods, accuracy, and limitations, ensuring a comprehensive comparison with Deep-Fed. Additionally, the literature review should be updated to reflect how Deep-Fed addresses the identified gaps more effectively. 6. Ethical and Funding Details: The manuscript states "N/A" for the ethics statement and provides a partial funding acknowledgment (Northern Border University, Arar, KSA) without full details. The authors must either provide a detailed ethics statement (if applicable, e.g., IRB approval for dataset use) or justify why it is not required. For funding, a complete financial disclosure statement (including author initials, grant numbers, funder URLs, and sponsor roles) must be included as per PLOS ONE requirements. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? Reviewer #1: Yes Reviewer #2: Yes ********** 2. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: Yes Reviewer #2: Yes ********** 3. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy Reviewer #1: Yes Reviewer #2: Yes ********** 4. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes Reviewer #2: Yes ********** Reviewer #1: Title: Deep-Fed: A Comprehensive Solution for Precise Bone Fracture Identification in Athletes The paper seems technically good. It has proposed an innovative prediction of bone fractures by using federated deep learning framework. The framework integrates deep learning techniques within a privacy-preserving structure. It involves standardized data preprocessing, data augmentation to improve robustness, and feature extraction using a convolutional neural network. The diagnosis is performed by the FractureNet model, while federated learning ensures training across multiple sources without sharing raw data, maintaining patient privacy Suggestions for improvements: Introduction: In the Introduction section, please incorporate a few recent references into the opening paragraphs. Additionally, the citation sequence is currently incorrect; references should begin from [1], not [5]. Ensure that all factual claims and prior findings—such as "X-rays, though effective in detecting obvious breaks, may miss 61 subtle or hairline fractures, especially in complex regions of anatomy such as the wrists or feet.”—are properly cited. Moreover, revise the transitions between paragraphs to improve the logical flow and narrative consistency within the Introduction. In the Contribution section, the proposed model is referred to as Dual-Deep, whereas the title mentions Deep-Fed. Please use a consistent model name throughout the manuscript, aligned with the one used in the title. Lastly, ensure the entire paper is thoroughly proofread for grammatical consistency and clarity. Literature Review: Some of the papers discussed in the Literature Review are missing from the comparison table. For instance, please include “Cao et al. [24] – SAFracNet” and “Cheng et al. [22]” in the comparison table for completeness. Additionally, revise and clarify certain sentences toward the end of this section. For example, consider rephrasing the following for improved clarity and coherence: “The scope therefore covers all aspects—from the collection of comprehensive data to input standardization via preprocessing. Furthermore, data augmentation is applied to improve model robustness, followed by feature extraction using a ConvNet architecture, and final diagnosis by the FractureNet model. Each of these steps ensures data privacy and security through federated learning.” Methodology: The term Dual-Deep is again used here instead of the correct model’s name Deep-Fed. Please ensure consistent terminology throughout the paper. At the beginning of the Methodology section, provide a brief summary or overview of the proposed framework as illustrated in Figure 1. Please add further details about Federated Learning for more clarity on the setup and configuration. Also, ensure that all text in figures is readable and the images are of high quality. Currently, some figures are blurry and difficult to read. Results: Some of the baseline studies used for comparison in this section are not listed in the comparison table of the Literature Review. Please update the table accordingly. Consider replacing or supplementing the current table with a line graph showing log loss for better visual interpretation. In addition, including a ROC curve would enhance the presentation and interpretation of your model’s performance. References: Please include a few recent references from the year 2025 to reflect the current state of research. Reviewer #2: The manuscript introduces Deep-Fed, a federated deep learning framework for bone fracture detection, targeting athlete-specific applications. The concept of integrating federated learning with deep CNN-based feature extraction is relevant and addresses current challenges in medical imaging privacy and robustness. The manuscript is structured well, but there are several concerns regarding technical clarity, experimental transparency, and reproducibility that need to be addressed. Major Comments o The paper does not include code availability, architectural specifics, or training logs, making reproduction difficult. o The manuscript mentions three datasets (Deep-I, II, III) but lacks clarity on: - Whether these datasets were used in federated (distributed) settings or centrally. - How the data was partitioned (e.g., balanced, unbalanced). o The manuscript claims privacy preservation via federated learning but does not provide empirical privacy evaluations (e.g., differential privacy noise impact, data leakage checks). o Baselines are compared in terms of classical metrics, but recent federated learning frameworks for medical imaging are not cited or compared (e.g., FedAvg, FedProx). Minor Comments o Terms like FL, ConvNet, CNN, FedAvg appear before definition in some places. o Phrases like "extremely accurate", "overwhelmingly outperforming" are too subjective. o Figures (e.g., Figure 4–6) are not thoroughly discussed. Some could be merged or clarified. o Table 2 mistakenly labels two columns as "Baseline 2". o Frequent grammatical issues such as "the author have" instead of "authors have", inconsistent article usage. ********** 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.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step.
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| Revision 1 |
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Dear Dr. Ali, 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. Please submit your revised manuscript by Dec 19 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 plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.
If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols . We look forward to receiving your revised manuscript. Kind regards, Lorenzo Faggioni, M.D., Ph.D. Academic Editor PLOS ONE Journal Requirements: 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. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author Reviewer #1: All comments have been addressed Reviewer #3: (No Response) ********** 2. Is the manuscript technically sound, and do the data support the conclusions??> Reviewer #1: Yes Reviewer #3: Yes ********** 3. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: Yes Reviewer #3: Yes ********** 4. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy Reviewer #1: Yes Reviewer #3: Yes ********** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes Reviewer #3: Yes ********** Reviewer #1: The authors have incorporated the comments raised in the initial review. The revised manuscript shows significant improvement in terms of clarity, structure, and technical depth. The inclusion of comparative analyses and detailed explanations in the methodology section has strengthened the scientific rigor of the paper. Only a few minor comments remain that authors should incorporate: In the abstract of the paper, please briefly federated learning advantages (privacy and decentralization) explicitly for clarity to readers. Figures: Ensure that all figures (especially Fig. 1) are of high resolution and properly labeled (font size and clarity for print version). References: Please double-check reference formatting consistency (e.g., ensure all journal names are italicized and years are in parentheses). Language: A quick proofreading is recommended to fix small grammatical issues (e.g., missing articles or spacing inconsistencies in equations). Overall, I appreciate authors for their efforts. I believe the above suggestions will further improve the quality of the paper. Reviewer #3: Recommendations for Abstract revision first: Eliminate Redundant Statements: Comment: The statements “These challenges underscore the need for advanced, accurate, and secure diagnostic solutions” and “designed to enhance the accuracy and reliability… while preserving patient privacy” restate goals that are inherently implied in any diagnostic study. These should be removed to avoid redundancy. Reason: The goals of improving accuracy and privacy are universally understood in diagnostic research. The core contribution should be emphasized: “This study proposes Deep-Fed, a federated deep learning framework for fracture diagnosis in athletes.” Omit Standard Techniques Unless Novel: Comment: The phrase “To address data diversity and robustness, the system incorporates standardized preprocessing and data augmentation techniques” can be removed, as preprocessing and augmentation are common practices in deep learning for medical imaging. Reason: These techniques are routine and only need to be mentioned if they are part of a novel approach or unique integration. Their inclusion in this context does not add specific value to the abstract and detracts from its focus. Focus on Specific Implementation of Generic Concepts: Comment: The sentence “Federated learning enables decentralized training… ensuring compliance with privacy regulations and mitigating risks of data leakage” should be revised to focus on the unique application of federated learning in the framework. Suggested Revision: Replace with: “Deep-Fed trains FractureNet using federated averaging across distributed athletic clinics without exchanging raw images.” Reason: Readers are familiar with federated learning’s benefits. It is more impactful to describe how federated learning is specifically applied in your model, such as the aggregation protocol or communication strategy. Streamline Evaluation Reporting: Comment: The evaluation results should be more concise, avoiding subjective statements like “demonstrated strong consistency” and “highlighting robustness and generalization.” Suggested Revision: Replace with: “Evaluated on Deep-I, Deep-II, and Deep-III datasets, Deep-Fed achieved 96.23%, 97.11%, and 96.73% accuracy, respectively, outperforming baselines.” Reason: Consistent high performance across datasets inherently implies robustness. Avoid subjective descriptors unless they are substantiated by quantitative metrics, such as standard deviations or other performance measures. Eliminate Redundant Qualifiers: Comment: The phrase “outperforming different baseline approaches” can be shortened to “outperforming baselines.” Reason: The word “different” is unnecessary because baselines, by definition, are varied models. Every term in the abstract should contribute new, essential information, and redundant qualifiers weaken the clarity and conciseness. By applying these recommendations, the abstract will be reduced by approximately 30%, sharpening the technical focus and aligning it with high-impact journal standards where brevity, novelty, and clarity are prioritized. Other suggestions are given below: 1- The work appears incremental: it combines a standard CNN backbone with an undefined “FractureNet” classifier and applies generic federated learning, offering no clear technical advance beyond existing FL-based medical imaging pipelines. 2- The introduction recycles abstract-level motivations (e.g., “accurate and timely detection,” “privacy concerns”) without advancing to a focused research gap; it should explicitly cite 2–3 recent FL-medical works and pinpoint their exact limitations (e.g., accuracy drop under non-IID, communication overhead) to justify Deep-Fed. Contributions are listed generically (“high accuracy,” “privacy-preserving”) and repeat abstract claims; they must be rephrased as measurable, non-obvious advances—e.g., “(1) FractureNet: a lightweight dual-branch classifier reducing parameters by 40 % while preserving accuracy; Contributions should not include performance results (e.g., accuracy, traffic reduction); these belong in Results. Instead, list only the key technical components Avoid vague phrases like “Deep-Fed utilizes advanced deep learning techniques”—CNNs and standard federated learning are not advanced. Replace with precise, non-hype language. (keep 3 core contributions in the list only) Important references in Introduction are missing. no logical flow among sentences and paragraphs even technique transformation. The paragraph (lines 93–102) is entirely generic praise of deep learning and fails to engage with the specific domain of bone fracture detection; it should be replaced or heavily revised to summarize prior CNN-based fracture detection studies (e.g., accuracy of ResNet/DenseNet on MURA or custom datasets, limitations in multi-site generalization, or sensitivity to imaging variations), Same for the above paragraph about Machine learning. 3- The dataset section lacks any reference or provenance for Deep-I, Deep-II, and Deep-III—critically undermining reproducibility and credibility. If these are public (e.g., MURA, custom hospital IRB-approved), cite them explicitly; if private, state: “anonymized multi-center athletic injury datasets under IRB #XYZ.” The dataset section should be restructured into one concise, formal paragraph followed by a compact table—eliminating Figure 2 and redundant text. Focus solely on objective dataset properties (name, source, modality, split sizes, class distribution, acquisition variability, and non-IID nature across sites) without speculative commentary on deep learning suitability or expected performance gains, which belong in Discussion or Results. 4- The data preprocessing description is adequate in scope, Image brightness and Gaussian noise can also be merged in it very smartly. The data augmentation section is excessively detailed and generic (e.g., listing rotation, flip)—collapse it into 1–2 sentences within the preprocessing paragraph, e.g.: “Standard on-the-fly augmentations (random rotation [−15°, +15°], horizontal flip, brightness/contrast jitter [0.8, 1.2]) were applied during training to enhance robustness.” Remove the standalone section to avoid inflating routine practices. 5- The feature extraction section (lines 284–334) is unnecessarily verbose and pedagogic, explaining basic CNN operations, equations, pseudocode (Algorithm 2), and a toy 32×32 pipeline with Fig. 3 — all must be deleted. CNN feature extraction is automatic and standard; simply state: “Feature extraction is performed using a [specify actual backbone, e.g., DenseNet-121 or custom CNN] with [input size, e.g., 224×224], outputting a fixed-dimensional vector per image.” No specific CNN architecture is disclosed — this is a critical omission. Remove all tutorial content, algorithm, and figure. 6- The entire Fracture Diagnosis section (lines 338–375) repeats basic CNN mechanics already covered and fails to define FractureNet’s novelty—Improve all equations (10–13), Algorithm 3, and generic convolution/pooling/softmax explanations. Simply state: “FractureNet is a lightweight two-layer MLP (512→256→2) with dropout (0.3) applied to the CNN-extracted features, outputting binary fracture probabilities via sigmoid.” No technical distinction is made between the generic CNN and “FractureNet”—the latter is not a specialized module but a standard classifier head. Remove claims of “powerful feature extraction” and “hierarchical ability”; instead, explicitly name the full architecture (e.g., “Deep-Fed uses DenseNet-121 backbone + FractureNet (MLP head) trained end-to-end in federated mode”) to avoid misleading readers. 7- The Federated Learning Implementation section provides a clear foundational description of FedAvg and privacy mechanisms. To strengthen it for publication, condense the tutorial content (remove Algorithm 4, equations 14–18, and redundant explanations) into one focused paragraph that precisely specifies your protocol: e.g., “Deep-Fed uses FedAvg with 10 clinical clients, 5 local epochs, batch size 32, and learning rate 0.01; model updates are aggregated as θ^{t+1} = ∑ (n_k / N) θ_k^t. Differential privacy (σ=1.0) is applied via gradient clipping and Gaussian noise during upload.” 8- Results need to be improved and more results are required to strengthen the claims. Currently, It lacks critical context—no baseline names in Figure, no ablation studies, no statistical significance, and no error metrics (sensitivity, specificity, AUC). Add a compact comparison table with specific baselines (e.g., “ResNet-50 centralized: 94.2%, DenseNet-121 FedAvg: 95.1%”) and 95% CI or p-values; include per-dataset breakdown and key clinical metrics (e.g., “Sensitivity: 96.8%, Specificity: 96.5% on Deep-III”). Include standard deviation of accuracy across datasets and one ablation (e.g., “w/ vs. w/o federated training”, “w/ vs. w/o augmentation”). Replace vague praise (“strong generalization”) with one sentence: “Deep-Fed maintains <0.5% accuracy variance across Deep-I/II/III, outperforming centralized training by 2.1% on average under non-IID conditions.” Include small, annotated image panel (e.g., Fig. X: “Success/Failure Cases”) to visually demonstrate clinical relevance and model failure modes. Specially, Improve Figure quality and type. Replace large, outdated bar charts with compact, high-resolution visualizations (e.g., grouped bar plots with error bars or heatmaps) using a clean, modern style (e.g., seaborn/matplotlib with minimal clutter, consistent fonts, and 300+ DPI). Add only once algorithm as Proposed Model rather thane every step contains one Algorithms with no information. Write like a paper, not a thesis: Cut the tutorials, equations, and algorithms—no one needs a CNN 101 or FedAvg pseudocode in 2025. Every sentence must advance your contribution, not teach basics. A paper should feel tight and surgical, not a 50-page chapter. A strong idea can die from poor communication: You have good results and a relevant problem, but FractureNet is a ghost, FL is vanilla, and half the text is filler. Define your novelty in one clear sentence, back it with one table and one figure, and let the results speak—don’t bury them in pedagogy. ********** 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 #3: Yes: Arifa Javed ********** [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.] To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation. NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications. |
| Revision 2 |
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Dear Dr. Ali, 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. Please submit your revised manuscript by Mar 23 2026 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 plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.
If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols . We look forward to receiving your revised manuscript. Kind regards, Lorenzo Faggioni, M.D., Ph.D. Academic Editor PLOS One Journal Requirements: 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. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author Reviewer #1: All comments have been addressed Reviewer #4: (No Response) ********** 2. Is the manuscript technically sound, and do the data support the conclusions??> Reviewer #1: Yes Reviewer #4: Yes ********** 3. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: Yes Reviewer #4: Yes ********** 4. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy Reviewer #1: Yes Reviewer #4: Yes ********** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes Reviewer #4: Yes ********** Reviewer #1: (No Response) Reviewer #4: This manuscript is interesting, but the following issues should be addressed: 1) A formal inferential statistical analysis should be performed to test whether the diagnostic performance of DeepFed was statistically significantly better than the baseline models. Also in the Abstract accuracy rates should be reported for DeepFed I-II-III and each baseline model along with some variability index (e.g., 95% CI) and the p-values of each statistical comparison. 2) Overall, it it strongly advisable that the manuscript follow a standard format for a scientific journal (i.e., Introduction, Materials and Methods, Results and Conclusions). The Introduction should be shortened by at least 30% by leaving out unnecessary, well-known preliminary information (especially e.g. at lines 26-48), and one sentence should be added to mention the key role of AI tools in fostering the development of imaging biobanks for enhanced precision medicine (see e.g. doi 10.1007/s00330-021-08431-6). Furthermore, the key concepts of the Literature Review section should be condensed in the revised Discussion section, which should be focused on discussing the key experimental findings of the authors' own study in view of the existing literature, highlighting any relevant comparisons and differences. ********** 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 #4: 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.] To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation. NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications. |
| Revision 3 |
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<p>Deep-Fed: A Comprehensive Solution for Precise Bone Fracture Identification in Athletes PONE-D-25-34607R3 Dear Dr. Ali, 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. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support . If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Lorenzo Faggioni, M.D., Ph.D. Academic Editor PLOS One Reviewers' comments: Reviewer's Responses to Questions Comments to the Author Reviewer #4: All comments have been addressed ********** 2. Is the manuscript technically sound, and do the data support the conclusions??> Reviewer #4: Yes ********** 3. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #4: Yes ********** 4. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy Reviewer #4: Yes ********** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #4: Yes ********** Reviewer #4: Thank you for your reply. All comments have been addressed successfully, and the manuscript has been significantly improved. ********** 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 #4: No ********** |
| Formally Accepted |
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PONE-D-25-34607R3 PLOS One Dear Dr. Ali, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS One. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. You will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Lorenzo Faggioni Academic Editor PLOS One |
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