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PLOS ONE

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Japan Society of Promotion of Science(JSPS)

https://www.jsps.go.jp”

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the protocol been described in sufficient detail??>

To answer this question, please click the link to protocols.io in the Materials and Methods section of the manuscript (if a link has been provided) or consult the step-by-step protocol in the Supporting Information files.

Reviewer #1: Yes

Reviewer #2: Partly

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3. Does the protocol describe a validated method??>

Reviewer #1: Yes

Reviewer #2: Yes

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4. If the manuscript contains new data, have the authors made this data fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: N/A

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5. Is the article presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Following a thorough review of the manuscript, the following issues and problems were identified. By addressing these issues, it is anticipated that the paper will exhibit enhanced quality and reliability, thereby more clearly demonstrating the value of the proposed system.

1. The methodology is inadequate in terms of detail.- The automatic segmentation process and the deep learning model used are not clearly explained. In order to enhance reproducibility, it is essential to provide more specific information, including the procedure, the software used, and the parameter settings.

2. Lack of evaluation metrics- Objective metrics are absent, hindering the evaluation of the effectiveness of the proposed system. For instance, the anatomical accuracy of the generated model and quantitative evaluation of training effects are required.

3. The number of samples and diversity (age, gender, body shape, etc.) of the CT data used are not clearly stated. These factors are of particular relevance given their impact on the generalisability of results.

4. Specific solutions to AR issuesThe issue of spatial consistency in AR is mentioned, but no specific approach to solving this problem or future research policy is presented.

5. Verification of cost-effectiveness and comparison with other existing methodsThe assertion that the system is "cost-effective" is not supported by specific evidence or comparative data.

A comparative analysis between the proposed system and other extant surgical training methods is absent. This would allow for a more robust demonstration of the system's superiority.

In addition, the long-term effectiveness of the system merits further investigation. The present paper does not examine the long-term effects of training using this system or its impact on actual surgical outcomes.

Reviewer #2: General Comments

Thank you for the opportunity to review “Development of Anatomically Accurate Digital Organ Models for Surgical Simulation and Training – A Methodology for Reconstructing and Applying 3D Organ Datasets in Real Space”. Overall, I think this is a worthwhile manuscript. I have a number of edits, suggestions, and comments that I would like the authors to consider before this manuscript is ready for publication.

The main comment I have for the authors about this manuscript is that while the guide that the authors created is certainly worthwhile and provides readers with a low-cost mechanism for segmentation and CAD-based design for anatomical structures, the authors overstate how easily the models created through this process could be 3D printed for surgical simulation (not to mention implementation in AR, with no mention of VR). Anatomically accurate simulation not only has to have organs and tissues that are correct size and location, but also, the texture, density, tensile strength (among many other qualities) has to be 1:1 to human tissue. Additive manufacturing practices to print these organs is evolving, and in addition, identifying the correct materials to replicate the different qualities of individual tissue types is still largely unknown (outside of a small number of tissues).

I would like the authors to update their manuscript to focus much more on the step-by-step process of their segmentation standard operating procedure to better aid readers in replicating this process simply by reading this manuscript. In addition, the authors should only mention 3D-printing or AR as mediums that could then utilize these 3D digital models with the appropriate 3D-printers, software, and materials, and dedicate software developers to incorporate the 3D models into surgical training AR (or VR) software.

Please note, all page numbers listed below are in reference to the PDF proof of the manuscript.

Introduction

This section was succinct but provided an OK background to the problem. I would like to see the authors provide an expanded background into the tangible methods of creating surgical simulation models. The vast majority of surgeons would not be able to use a 3D digitized model of an organ(s) for surgical simulation. A thorough discussion of 3D-printing and mixed reality in the world of surgery simulation would enhance this section greatly.

Page 13 line71 – please define an acronym the first time you use it (STL).

Materials and Methods

Page 15 Lines 85-88 – Did the authors begin with their DICOM file in Blender and then move it to 3D Slicer? From my experience it would be flipped. I suggest writing this section in the correct chronological order of operations.

Page 15 lines 90-101 and Page 16 lines 108-117: I am confused about the function of the multi-planar reconstruction images that are automated through a python script. Can the authors please elaborate on the function of this reconstruction images and the benefit of creating them through a script. Why is this an important step instead of simply creating the surface model that 3D Slicer can do?

AR Section – Did the authors create any type of UI or interactions for the models beyond the baseline that Apple provides with the AR Kit? Please elaborate if so.

Results

Please refer to main comment at the beginning of this review with request to update the results section to largely remove the 3D printed simulator outside of potentially showing how the authors produced their model. This is important, because while the authors 3D printed a surgical simulator based on the segmented model, there appears to be no assessment of the 3D printed simulator.

Discussion

Page 23 lines 252-254: I would like the authors to elaborate more on the creation of organs or tissues that are not auto extracted and how they could create them by simply referring to the image data.

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what does this mean? ). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

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Development of Anatomically Accurate Digital Organ Models for Surgical Simulation and Training

PONE-D-24-54417R1

Dear Dr. Kimura,

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,

James J Cray Jr., Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

?>

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the protocol been described in sufficient detail??>

To answer this question, please click the link to protocols.io in the Materials and Methods section of the manuscript (if a link has been provided) or consult the step-by-step protocol in the Supporting Information files.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Does the protocol describe a validated method??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. If the manuscript contains new data, have the authors made this data fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: N/A

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5. Is the article presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: The revised paper was judged to have reached a level of scientific merit that adequately addresses the points raised in the peer review. In particular, the main strength is the improved methodological transparency provided by the publication of the protocol (DOI: 10.17504/protocols.io.bp2l6d19dvqe/v4) and the GitHub code sharing (https://github.com/tk1971-Jpn). The technical originality of the specific example of the laparoscopic cholecystectomy simulator (Figure 6) and the mechanism for reproducing the metal base coordinate system using Blender-Python integration (Figure 5) is high. However, the following areas for improvement remain.

#1 Main issues of concern

1) Inadequate verification of data accuracy

Inter-observer variability in the process of modifying automatically extracted data has not been evaluated. Comparison of cube coefficients and statistical analysis of surface shape by three or more surgeons is required. Quantitative comparative data, such as mean surface distance of the liver margin, are lacking for the difference in accuracy of automatic segmentation between Horos and 3D Slicer.

2) AR projection accuracy issues

Quantitative data on positional errors between virtual and real objects (e.g. verification of 5mm grid pattern projection) have not been measured. Measurement of projection errors in millimetres is essential to ensure accuracy of 1.5mm or less, which is required for laparoscopic surgery training.

3) Technical significance

It is worth noting that the paper clearly defines a method for removing unnecessary parts from existing organ data (subtractive approach) and aims to improve the efficiency of simulator design (p.18, line360) [1]. However, there is a lack of comparative studies with automatic segmentation methods other than deep learning (nnU-Net) (e.g. atlas-based), and clarification of the technical limitations is needed.

#2 Future perspectives

It is necessary to systematically evaluate the impact of different CT scanning protocols (tube voltage 80-140kVp, reconstruction algorithm FBP vs. IR) on data quality. The versatility of the software should be enhanced by adding examples of analysis of data from the user's own institution in addition to the TCIA data.

It is also hoped to implement tissue hardness simulation (Young's modulus range 10-100kPa) using the Blender physics engine.

#3 Conclusion

The response to the peer review comments is adequate and we recommend publication at this stage. The remaining issues are considered to be within an acceptable range, indicating the possibility of future development. The next important step towards practical application in education will be the quantitative evaluation of the training effect (rate of reduction in operative time, rate of reduction in complications). The academic significance is high as a model that combines the democratising effect of open source infrastructure and medical AI clinical knowledge. In conclusion, the revised manuscript adequately addresses the reviewers' comments and is recommended for publication at this time. The remaining issues are considered to be within an acceptable range, indicating the possibility of future development. The next important step will be to quantitatively evaluate the training effects (reduction in operative time and complication rate) for practical application in the educational field.

Reviewer #2: I want to thank the authors for addressing all of the comments I made previously. I believe that this revised manuscript is much stronger and is ready for acceptance.

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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.

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Reviewer #1: Yes:  Takashi Kamio

Reviewer #2: No

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