Artificial intelligence that determines the clinical significance of capsule endoscopy images can increase the efficiency of reading

Junseok Park; Youngbae Hwang; Ji Hyung Nam; Dong Jun Oh; Ki Bae Kim; Hyun Joo Song; Su Hwan Kim; Sun Hyung Kang; Min Kyu Jung; Yun Jeong Lim

doi:10.1371/journal.pone.0241474

Peer Review History

Original SubmissionMay 27, 2020
26 Jun 2020 Decision Letter - Dilbag Singh, Editor PONE-D-20-15923 Artificial intelligence based on binary classification according to clinical significance of images improves the reading efficiency of capsule endoscopy PLOS ONE Dear Dr. LIM, 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. ============================== Although, authors written a good paper, but referees are asking major revision. So, I invite you to submit a revised manuscript. ============================== Please submit your revised manuscript by Aug 10 2020 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. 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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? 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 Reviewer #3: Partly ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: No ****** 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 Reviewer #3: No ****** 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 Reviewer #3: Yes ****** 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: After initial screening I have observed following issues:- 1. Authors should add more comparative analyses, 2. It is advised to explain the equations present in the paper in more efficient manner. 3. Some confusing and long lines are there, authors should adjust these lines. 4. The literature can be improved by adding some recent plos one papers and also: Efficient Prediction of Drug-drug interaction using Deep Learning Models, Imputing missing values using cumulative linear regression, Visibility improvement and mass segmentation of mammogram images using quantile separated histogram equalisation with local contrast enhancement, Classification of COVID-19 patients from chest CT images using multi-objective differential evolution--based convolutional neural networks, Deep learning approach for microarray cancer data classification, Deep Transfer Learning based Classification Model for COVID-19 Disease, 5. Conclusion can be improved further by presenting more statistical computed values. Reviewer #2: 1. Author should reconsider the title. It is very confusing. 2. Abstract is too long. Authors need to be rewrite it for better understanding. 3. It is advised that ROC curve that is Figure 4 must be reevaluated by considering ROC analysis of competitive techniques. 4. Figure 5 is confusing, so authors should provide more description about this figure. 5. It is advised to compare with more state-of-art techniques for comparative analyses. 6. Parameter setting of the proposed model should be presented in tabular form. 7. There are some typos and grammatical mistakes present in the manuscript. 8. Paper organization is missing at the end of introduction 9. Headings and sub-headings have not been marked 10. It is advisable to authors consider following useful references Fusion of medical images using deep belief networks, Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks, Efficient prediction of drug–drug interaction using deep learning models, Deep Transfer Learning based Classification Model for COVID-19 Disease , Multi-objective differential evolution based random forest for e-health applications Reviewer #3: Paper can be improved further 1. Add suitable research motivation 2. Add more relevant references from plos one. 3. Equations should be explained carefully 4. More comparative analysis are required 5. Add statistical analysis in conclusion or discussion session ****** 6. 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 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.] 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. 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Revision 1
21 Aug 2020 Author Response Our responses to each of the reviewers’ comments in a point-wise manner below. 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 https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf In the revised submission, we have corrected the organization according to the formatting instructions provided by the journal. 2. Please amend either the abstract on the online submission form (via Edit Submission) or the abstract in the manuscript so that they are identical. We have ensured that the Abstract submitted through the online submission form and that included in the revised article match. 3. We note that Figure 2-3 in your submission contain satellite images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright. Thank you for your concern regarding the copyright issues. However, we would like to assure you that the images included in this article do not contain any copyrighted satellite information. If you are referring to the colored features in images 2 and 3, they were created with open-source technology of the “Class activation mapping,” and a relevant reference has been cited in this regard. You can also find the information at the following website: https://github.com/zhoubolei/CAM Reviewers' comments: Reviewer #1: After initial screening I have observed following issues: 1. Authors should add more comparative analyses, As recommended by the reviewer, we have added results of per lesion analysis including lesion detection rates and reading time of reviewers. We also found a correlation between the total number of extracted images and the number of images exceeding the significance threshold. The statistical correlations and interpretations have been discussed in the Results and Discussion sections. 2. It is advised to explain the equations present in the paper in more efficient manner. We have explained the learning, validation, and test processes in AI development, and have provided more details about the parameters. We have also proved that random selection of images to construct each dataset was performed in an appropriate way by comparing the AUC values. 3. Some confusing and long lines are there, authors should adjust these lines. We have ensured that there are no confusing sentences in the revised text. The manuscript has been professionally edited by an English language editing company and the editing certificate is being provided herewith for your perusal. 4. The literature can be improved by adding some recent plos one papers and also: Efficient Prediction of Drug-drug interaction using Deep Learning Models, Imputing missing values using cumulative linear regression, Visibility improvement and mass segmentation of mammogram images using quantile separated histogram equalisation with local contrast enhancement, Classification of COVID-19 patients from chest CT images using multi-objective differential evolution--based convolutional neural networks, Deep learning approach for microarray cancer data classification, Deep Transfer Learning based Classification Model for COVID-19 Disease. We would like to thank the reviewer for the earnest suggestion. Unfortunately, we believe that the papers suggested for inclusion by the reviewer are not suitable for use as references in our article. However, PLoS ONE has published several valuable articles on the use of AI in medical imaging diagnosis and we have cited three of such papers in the revised manuscript. 5. Conclusion can be improved further by presenting more statistical computed values. As recommended by the reviewer, we have enriched the information in the Discussion and Conclusions sections with statistical values. Reviewer #2: 1. Author should reconsider the title. It is very confusing. As suggested by the reviewer, we have modified the title for clarity. We have also got the manuscript edited by a professional English language editing company and have submitted the editing certificate. 2. Abstract is too long. Authors need to be rewrite it for better understanding. We have shortened the abstract to 245 words. The re-written Abstract contains all the components prescribed in the author guidelines. 3. It is advised that ROC curve that is Figure 4 must be reevaluated by considering ROC analysis of competitive techniques. We have explained the learning, validation, and test processes in AI development, and have provided more details about the parameters. We have also proved that random selection of images to construct each dataset was performed in an appropriate way by comparing the AUC values. 4. Figure 5 is confusing, so authors should provide more description about this figure. We have added the results of per lesion analysis including lesion detection rates and reading time of reviewers. The statistical correlations and interpretations have been discussed in the Results and Discussion sections. In the revised manuscript, numbers that were previously not mentioned have been introduced. We are sure these additions will help readers in better understanding the graph. 5. It is advised to compare with more state-of-art techniques for comparative analyses. Although our study is related to the recent research on AI, it mainly covers the field of clinical medicine. The primary objective of this study was to assess the clinical effectiveness of AI, not to prove the technical performance of AI. Analysis of whether the detection of clinical lesions could be improved with the help of AI was properly assessed using classical techniques of statistics. The results of the statistical analyses effectively demonstrated the differences between the two reviewer groups. In addition, the AI technology used to assist the CE reading was also selected from the recently developed high-performance networks. As reported in the reference number 11 cited in the manuscript, the Inception-ResNet-V2 shows the third highest accuracy among various deep learning models. Because the other two models with high accuracy involve many more parameters, we selected Inception-ResNet-V2 to evaluate the accuracy and training/inference time simultaneously. 6. Parameter setting of the proposed model should be presented in tabular form. We have presented the details of the parameters in Table 1 in the revised manuscript as suggested by the reviewer. 7. There are some typos and grammatical mistakes present in the manuscript. We have corrected all the typographical and grammatical errors. The editing certificate is being submitted with the revised manuscript. 8. Paper organization is missing at the end of introduction We have organized the manuscript in accordance with the formatting instructions provided by the journal. 9. Headings and sub-headings have not been marked In the revised paper, we have marked the headings and sub-headings according to the formatting instructions. 10. It is advisable to authors consider following useful references Fusion of medical images using deep belief networks, Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks, Efficient prediction of drug–drug interaction using deep learning models, Deep Transfer Learning based Classification Model for COVID-19 Disease, Multi-objective differential evolution based random forest for e-health applications Unfortunately, the papers suggested by the reviewer are not suitable for use as references in our article. However, PLoS ONE has published several valuable papers on the use of AI in medical imaging diagnosis and three of such papers have been cited in the revised manuscript. Reviewer #3: Paper can be improved further 1. Add suitable research motivation We have modified the Introduction section to highlight the motivation for this research. We have emphasized upon the distinction of our study and have stated the goals clearly. We have presented a practical comparison method considering the reality in capsule endoscope reading and have stated that achieving a statistical interpretation through the use of the presented method was the goal of our study. 2. Add more relevant references from plos one. PLoS ONE has published several valuable papers on the use of AI in medical imaging diagnosis and three of such papers have been cited in the revised manuscript. 3. Equations should be explained carefully We have explained the learning, validation, and test processes in AI development, and have provided more details about the parameters. We also proved that random selection of images to construct each dataset was performed in an appropriate way by comparing the AUC values. 4. More comparative analysis are required As suggested by you, we have added results of the per lesion including lesion detection rates and reading time of reviewers. We also found a correlation between the total number of extracted images and the number of images exceeding the significance threshold. The statistical correlations and interpretations have been discussed in the Results and Discussion sections. 5. Add statistical analysis in conclusion or discussion session As suggested by the reviewer, we have enriched the Discussion and Conclusions sections by including the results of statistical analysis. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0241474.r002
16 Oct 2020 Decision Letter - Sudipta Roy, Editor Artificial intelligence that determines the clinical significance of capsule endoscopy images can increase the efficiency of reading PONE-D-20-15923R1 Dear Dr. LIM, 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 for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. 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, Sudipta Roy Academic Editor PLOS ONE Additional Editor Comments (optional): The paper is technically sound and did in-depth analysis. Authors have address all the points very carefully. 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 #3: All comments have been addressed ******** 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 #3: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #3: 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 #3: Yes ****** 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 #3: 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 #3: (No Response) ****** 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 #3: No https://doi.org/10.1371/journal.pone.0241474.r003
Formally Accepted
20 Oct 2020 Acceptance Letter - Sudipta Roy, Editor PONE-D-20-15923R1 Artificial intelligence that determines the clinical significance of capsule endoscopy images can increase the efficiency of reading Dear Dr. LIM: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. 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. If we can help with anything else, please email us at plosone@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. Sudipta Roy Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0241474.r004

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