Preoperative evaluation of C2 pedicle screw placement using a deep learning model: Development and validation study

Junhao Bao; Wei Wang; Yuelin Wu; Hao Ren; Zhaoquan Liang; Qiang Xiao; Yeyang Wang; Fengshi Jing; Weibin Cheng; Li Zhang

doi:10.1371/journal.pone.0342349

Peer Review History

Original SubmissionNovember 20, 2025
8 Dec 2025 Decision Letter - Dean Chou, Editor Dear Dr. Zhang, 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 Jan 22 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. A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. 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There is no requirement to cite these works unless the editor has indicated otherwise. [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 Reviewer #3: Partly ******** 2. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: 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 Reviewer #3: No ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ****** Reviewer #1: This manuscript presents the development and validation of a deep learning–based workflow (C2-Net) for preoperative evaluation of C2 pedicle screw placement feasibility using CT images. The authors integrated an automated segmentation model and a classification module, and validated performance against a 3D-printed model–based ground truth and surgeons of different experience levels. However, several areas require clarification and expansion to strengthen the article. 1.The 4.78-mm MPD cutoff requires stronger justification The chosen cutoff results in an almost perfectly balanced distribution (245 high-risk vs. 245 low-risk pedicles). This raises concerns about whether the threshold reflects clinical reality or merely statistical optimization. Please provide: 1)A detailed explanation of how 4.78 mm was determined. 2)Whether a ROC-based Youden index was used in previous work. 3)Sensitivity analyses using alternative thresholds (e.g., 4.0 mm, 5.0 mm). 2.Deep learning module descriptions are insufficient for reproducibility. Detailed reporting of model architectures and training are required. Like, complete U-Net configuration (encoder/decoder depth, filters, image size); Full C3D architecture (kernel sizes, number of channels, FC layer dimensions); Optimization details: learning rate, optimizer type, batch size, dropout, epoch count, early stopping criteria; Data augmentation strategies, if any; Measures taken to prevent data leakage (e.g., patient-level separation). Reviewer #2: Abnormalities in the C2 pedicle root led to the failure of screw placement. The main issues included the overly thin diameter of the pedicle root, variations in the vertebral artery, and the bone condition of the pedicle root, such as osteoporosis, bone destruction or hyperplasia. However, this study excluded variations in the vertebral artery, as it solely relied on the diameter of the pedicle root as the criterion. Moreover, it performed screw placement on 3D printed specimens, which did not match the bone condition of the human body. We know that this varies from person to person. Ultimately, the diameter of the pedicle root was only used as one of the reference reasons. Therefore, I believe this research should continue. It should be verified in surgical patients to determine the success of this model. For example, using the CT imaging data of patients who have undergone surgery for a double-blind assessment, and then comparing it with the CT obtained during postoperative follow-up to evaluate it, this would be more convincing. Reviewer #3: This is a unique study that uses AI to determine the feasibility of C2 pedicle screw insertion compared to junior and senior surgeons. The statistical descriptions are technically sound but there are several concerns that need to be addressed: • It appears that the MPD cutoff 4.78 mm is central to the whole study. The authors need to provide further discussion on how this value was picked and provide analyses of different MPD cutoffs • Further clinical context on the application of their tool needs to be provided. Brief discussion of how C2-Net could integrate into navigation/planning systems and handle patients with vertebral artery anomalies. • The claim that the model outperforms junior residents is not supported in the results (P>0.05). Perhaps this is a typo in the results? • The external test of 50 patients is small and this needs to be discussed in their limitations • The authors need to explicitly discuss data leakage risks of the left and right pedicles appearing in different splits ****** 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 Reviewer #3: 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. https://doi.org/10.1371/journal.pone.0342349.r001
Revision 1
19 Jan 2026 Author Response Dear Dr. Chou and Reviewers, We sincerely thank the Academic Editor and all reviewers for their careful evaluation and constructive comments. We have revised the manuscript thoroughly in accordance with the suggestions. All modifications are highlighted in the tracked-changes version. Below we provide a detailed, point-by-point response. Line numbers refer to the revised manuscript with track changes. Response to Journal Requirements 1. Compliance with PLOS ONE style We have revised the manuscript to fully comply with PLOS ONE formatting and file-naming requirements, including figure captions, references, and structure. 2. Code sharing All author-generated code has been made publicly available without restrictions at: https://github.com/Ingram66/C2-NET 3–5. Funding statement Funding information has been removed from the Acknowledgments section and retained only in the Funding Statement. The grant number has been corrected from 2024A03J074 → 2024A03J1074. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 6. Data statement In accordance with PLOS policy, the contact point has been changed to a non-author institutional office: The Medical Ethics Committee of Guangdong Second Provincial General Hospital (Email: gd2hllwyh@163.com). This has been updated in the Data Statement (Lines 437–439). 7. Figure captions Separate captions for each figure have now been provided in the manuscript. Response to Reviewer #1 Comment 1 The 4.78-mm MPD cutoff requires stronger justification… sensitivity analyses using alternative thresholds are recommended. Response We appreciate this important comment. We have clarified the origin and role of the 4.78-mm threshold: 1. Derivation of 4.78 mm The 4.78-mm threshold was derived from our previous independent CTA-based 3D-printed study using ROC analysis and the Youden index, and was not optimized on the current dataset. In the present study, this value was used only for data labeling (MPD ≥4.78 mm as low-risk; <4.78 mm as high-risk) and not as an intraoperative decision rule. Importantly, the labels in the external test set were determined by actual screw placement outcomes on 3D-printed models, rather than by MPD measurements, thereby avoiding circular validation. Added in Materials and methods--Training and validation set, Lines 109-111 Clarified in Discussion, Lines 362-366 2. Sensitivity analysis A supplementary analysis was conducted using an alternative 4.30-mm cutoff, selected with reference to the commonly accepted 80% screw–pedicle ratio principle, rather than as a mathematically exact derivation from the 3.5-mm screw diameter. The model was retrained using this threshold, while the external test set and its labels—based on cortical breach observed in 3D-printed screw placement—remained unchanged to avoid data leakage. Under this setting, model performance decreased compared with the ROC-derived 4.78-mm threshold, which supports the robustness and clinical appropriateness of the original cutoff. Added in Materials and methods--Training and validation set, Lines 115-118 Added in Discussion, Lines 369-372 Results provided in Supplementary Table S1 Comment 2 Deep learning module descriptions are insufficient for reproducibility. Response We agree that detailed methodological reporting is essential. Accordingly, we have revised the manuscript to comprehensively describe the model architecture, training strategy, and data handling procedures to facilitate reproducibility. Added in Materials and methods -- Deep learning model construction, Lines 176-196. Comment 3 Risk of data leakage between left and right pedicles. Response Dataset partitioning was performed strictly at the patient level before any preprocessing or augmentation. All slices and both left/right pedicles from the same patient were assigned exclusively to one subset (training, validation, or testing). Therefore, no image, slice, or anatomical structure from a given patient was ever present across different subsets, effectively preventing data leakage. Clarified in Materials and methods--Training and validation set, Lines 112-116 Response to Reviewer #2 Comment The study did not consider vertebral artery anomalies and used 3D-printed models rather than real bone conditions. Response We thank the reviewer for this important comment. We acknowledge that the present study did not incorporate vertebral artery anomalies and that the experiments were performed on 3D-printed models rather than real intraoperative bone conditions. In this study, cases with high-riding vertebral artery or other vascular variations were intentionally excluded, and the proposed model was designed to evaluate pedicle morphology only. We have now expanded the Discussion to clarify that clinical application of the system requires integration with CTA-based vascular assessment and that prospective validation in real surgical patients will be necessary to confirm safety and generalizability. Added in Discussion, Lines 347-349 and 415-419 Response to Reviewer #3 Comment 1 Need further discussion of the 4.78-mm value and alternative cutoffs. Response Addressed as described for Reviewer #1. A sensitivity analysis is now presented (Supplementary Table S1). Comment 2 Claim that the model outperforms junior residents is not supported (P>0.05). Response Thank you for noting this. The statement has been corrected to reflect no statistically significant difference between C2-Net and junior surgeons. Revised in Discussion, Lines 351-355 Comment 3 External test set of 50 patients is small. Response This limitation has been explicitly acknowledged. Added in Discussion, Lines 418-420 Comment 4 Need explicit discussion of data leakage. Response Addressed as above; Clarified in Materials and methods--Training and validation set, Lines 112-116 Supplementary Material Cutoff scheme Accuracy Sensitivity Specificity AUC 4.78 mm (primary, ROC-derived) 0.89 0.9 0.89 0.94 80% screw–pedicle ratio (sensitivity analysis) 0.66 0.72 0.6 0.71 Table S1. Sensitivity analysis of model performance under an alternative cutoff definition (same 3D-printed test set) We sincerely appreciate the reviewers’ valuable suggestions, which have substantially improved the manuscript. We hope the revised version is now suitable for publication in PLOS ONE. Sincerely, Li Zhang Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0342349.r002
22 Jan 2026 Decision Letter - Dean Chou, Editor Preoperative Evaluation of C2 Pedicle Screw Placement Using a Deep Learning Model: Development and Validation Study PONE-D-25-56509R1 Dear Dr. Zhang, 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, Dean Chou, MD Academic Editor PLOS One Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0342349.r003
Formally Accepted
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