PONE-D-21-10289

A Machine learning framework for the evaluation of myocardial dysfunction in patients with noncompaction cardiomyopathy and rigid body rotation

PLOS ONE

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PLOS ONE

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Additional Editor Comments (if provided):

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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: No

Reviewer #2: No

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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: Yes

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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: In this study, authors presented a study of NCC identification by using ML method. They focused on evaluation of the RBR criteria. They stated that good performance was obtained. However, there were several major issues that the result was therefore not convincing.

Comments:

1. The language usage throughout this paper need to be improved, the author should do some proofreading on it. Give the article a mild language revision to get rid of few complex sentences that hinder readability and eradicate typo erros.

2. Overall, the basic background is not introduced well. To my understanding, differentiation between NCC patient to non NCC patient was the key objective. However, no explanation about the ‘3 most common criteria’ was provided while limited information about the RBR without explanation about the correlation between RBR and NCC and the correlation between RBR and other echocardiographic parameters. It was confusing that whether the NCC or the RBR was the objective of this work.

3. Introduction needs to explain the main contributions of the work more clearly. The novelty of this paper is not clear.

4. Literature review techniques has to be strengthened. The authors should consider more recent research done in the field of their study.

5. The method section is not clear. Above all, it is not clear that what kinds of parameters were collected. How did you collected the parameters that were not mentioned in method section. What is the characteristic of the data. Second, it is not clear whether the image quality check was only performed on data for LS analysis? What about the other parameters?

6. There is a missing of citation for the random forest algorithm. How did you determine the key features? It is not clear about the architecture of the MLP. Also, it is not clear about the split of data for training, validation and test.

7. Result section. Correlation was presented, but where was the result of RF for parameters selection?

8. It is unclear how NC/C was generated.

9. Discussion section. what is boundary subsets?

10. Authors stated that ‘cardiac mechanics seems to be more promising as a specific marker, and trabeculae may be considered just a red flag cardiac mechanics’. However, while the cardiac mechanics parameters, ie. GLS and LVEF were the result of various phenotypes, it is not clear how the authors came to this statement. Please elaborate.

Reviewer #2: This paper uses a machine learning (ML) based model to evaluate myocardium function, and the role of echocardiographic predictors for rigid body rotation in Noncompaction cardiomyopathy (NCC). The manuscript is clear, straightforward, easy to follow. The results are sufficient and convincing. I think this study has the potential for NCC risk stratification. However, I would like to draw the author's attention to the following major concerns:

1)In this study, the authors used 50 patients for training and testing the machine learning model. Considering, the model is a deep neural network. The data set the size of 50 patients is insufficient.

2)The authors do not explain clearly its used deep neural network. As a key point, what is the architecture of the deep neural network? Does the network use a general architecture, or is it specially optimized for NCC? Do different network architectures change the results？The authors should do a more thorough literature survey. Just to name a few:

-Cardiac Functional Analysis with Cine MRI via Deep Learning Reconstruction

-Segmentation and quantification of infarction without contrast agents via spatiotemporal generative adversarial learning.

-Automated left ventricular dimension assessment using artificial intelligence developed and validated by a UK-wide collaborative

3) In this study, the authors used a single echocardiographic ultrasound system from a single center. I think this point is the most important limitation of this study. The generalization error is a big problem for machine learning (ML) based models. It would be better to discuss this point in the manuscript.

4) There are some grammar errors and typos. I suggest the authors make an solid, overall proofreading.

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Reviewer #1: No

Reviewer #2: No

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A Machine learning framework for the evaluation of myocardial dysfunction in patients with noncompaction cardiomyopathy and rigid body rotation

PONE-D-21-10289R1

Dear Dr. Salemi,

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,

Zhifan Gao

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

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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: No

Reviewer #2: No

**********

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: Authors presented an exploratory investigation of the markers that could potentially help in differentiate diagnosis for NCC patients. Significant improvements have been made. There are several minor issues

1. In Figure 1, it seems that the white line was supposed to be the difference between green line and pink line, while it is confusing in the caption that what does the “curve mean” mean?

2. It will be appreciated to see the ROC analysis for differentiating the cases with or without RBR in patients with NCC.

Reviewer #2: (No Response)

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7. 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