ai-corona: Radiologist-assistant deep learning framework for COVID-19 diagnosis in chest CT scans

Mehdi Yousefzadeh; Parsa Esfahanian; Seyed Mohammad Sadegh Movahed; Saeid Gorgin; Dara Rahmati; Atefeh Abedini; Seyed Alireza Nadji; Sara Haseli; Mehrdad Bakhshayesh Karam; Arda Kiani; Meisam Hoseinyazdi; Jafar Roshandel; Reza Lashgari

doi:10.1371/journal.pone.0250952

Peer Review History

Original SubmissionFebruary 10, 2021
5 Mar 2021 Decision Letter - Gulistan Raja, Editor PONE-D-21-04656 ai-corona: Deep Radiologist-Assistant for COVID-19 Diagnosis in Chest CT Scans PLOS ONE Dear Dr. Lashgari, 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. The manuscript had be reviewed by 2 reviewers and both reviewers were of the view that manuscript describes a technically sound piece of scientific research and recommended minor revision. However, they had made certain observations to improve your work. After thorough consideration of comments of reviewers, my decision is "minor revision". Please incorporate comments raised by both reviewers. Additional Note to Authors: I noted that one of the reviewers has asked for more context in the literature review, and suggested specific papers to be cited. While you may take on-board their suggested papers if you feel that they are relevant for your manuscript, or just take on-board the general suggestion for providing some more context in the literature review, there is no requirement from the journal to cite these papers Please submit your revised manuscript by Apr 19 2021 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. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). 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To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. Please see the following video for instructions on linking an ORCID iD to your Editorial Manager account: https://www.youtube.com/watch?v=_xcclfuvtxQ 5. Please ensure that you refer to Figures 4, 8, 9 and 10 in your text as, if accepted, production will need this reference to link the reader to each figure. 6. We note you have included tables to which you do not refer in the text of your manuscript. Please ensure that you refer to Tables 3-8 in your text; if accepted, production will need this reference to link the reader to each Table. [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? 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 ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: N/A ****** 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 ****** 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: No Reviewer #2: 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: The paper describes a CNN-based model for classification of 3D-CTs as either presenting COVID-19, having several other abnormalities, or being normal. An ImageNet pretrained EfficientNetB3 is used as a slice-wise feature extractor. Then, the features from all slices are combined by averaging and classified using a fully connected layer. The model achieves compelling results, outperforming on its own even experts helped by the model for the task of COVID-19 vs others classification. The datasets employed and the validation techniques are all sound and there is a very complete array of numerical comparisons with 4 radiologists. I recommend it for publication, although there are some comments that should be addressed: The paper requires English review. I am no native English speaker, but only in the abstract, I can find already several grammatical mistakes: “We employed three independent dataset”, “Our results show thatai-coronaoutperforms all”, “for which it gained AUC score of 0.997”, “our framework’s diagnosis capabilities was evaluated”. Line 77: The differences between NCA (non COVID-19 abnormal), non-pneumonia, and normal classes should be clarified, and also what pathologies might represent each of the first two. Line 176: Average pooling is employed to combine the final activation maps from each slice into a single activation map for the whole CT. However, this ignores any kind of spatial information contained along the z-axis, as it is all averaged. Furthermore, using average pooling might bury any positive detection among many negative ones. Have you tried using another combination method that is aware of the z-axis information (such as 3D pooling, instead of 2D, or using 3D convolutions)? Have you tried using max-pooling instead of average pooling? Also, I would have suggested to add the previous and next slice to the input of each EfficientNet feature extractor, instead of repeating the same slice three times, as way to add some z-axis information to each prediction. Line 189: Do you use EfficientNetB5 or B3? B5 is mentioned here, but B3 is mentioned in the rest of the text. Also, Figure 1 shows that the input images are of resolution 512x512, but that is not the input resolution of the EfficientNetB3, nor the B5. Reviewer #2: Minor Revision # In the introductory section, the first paragraph should discuss about COVID-19 pandemic, number cases, deaths, recovered and the taxonomy of the virus etc. # Author should stress on why their contribution stand out or achieved better result than others. # There should be bullet points on authors contribution at the end of introduction. # Author should add a paragraph on COVID-19 vaccines. # Author need to add a section on “Related work” and a table to summarized contributions in terms of dataset and performance evaluation. # Author need to add a limitations and future work. # Authors can also add these references 1. Chowdhury, M. E., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B., ... & Islam, M. T. (2020). Can AI help in screening viral and COVID-19 pneumonia?. IEEE Access, 8, 132665-132676. 2. Ibrahim, A. U., Ozsoz, M., Serte, S., Al-Turjman, F., & Yakoi, P. S. (2021). Pneumonia classification using deep learning from chest X-ray images during COVID-19. Cognitive Computation, 1-13. 3. Burki, T. K. (2020). The Russian vaccine for COVID-19. The Lancet Respiratory Medicine, 8(11), e85-e86. ****** 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: Yes: Oscar José Pellicer Valero Reviewer #2: Yes:** Abdullahi Umar Ibrahim PhD [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. Attachments Attachment Submitted filename: PONE-D-21-04656.docx https://doi.org/10.1371/journal.pone.0250952.r001
Revision 1
15 Apr 2021 Author Response Dear Dr. Gulistan Raja, Academic Editor PLOS ONE Thank you for giving us the opportunity to resubmit our revised manuscript “ai-corona: Deep Radiologist-Assistant for COVID-19 Diagnosis in Chest CT Scans”. Ms. No: PONE-D-21-04656'' to PLOS ONE. In this version of the manuscript, we have addressed all the criticisms of the reviewers and made changes throughout the main manuscript text. We appreciate the time and effort that you and the reviewers have dedicated to providing your valuable feedback on our manuscript. We are grateful for the reviewer’s careful reading of the paper and their excellent criticisms and recommendations to improve the paper. The criticisms had led to significant improvements made in the manuscript. We also revised the title of the manuscript as follows: “ai-corona: Radiologist-Assistant Deep Learning Framework for COVID-19 Diagnosis in Chest CT Scans”. Below, we detail how we have responded to the two reviewers’ criticisms. Sincerely, Reza Lashgari, Ph.D ----------------------------------------------------------------------------------- Summary of the changes: All changes in the manuscript were highlighted in blue color. • The manuscript text was justified. • The entire manuscript has been carefully edited by a native English speaker to improve grammar and readability. • References, numbering, and the references section were reviewed and double-checked. • We incorporated the data availability section into the final manuscript. • Zero padding to 3 decimal points for coherence was added in Table 3. To prevent visual congestion on the "supporting information" version, blue highlighting was left out. • Changed the positioning of some of the tables and figures for better visual presentation. • We added the author contributions in the end of manuscript. Manuscript Title: We revised the manuscript tile as “ai-corona: Radiologist-Assistant Deep Learning Framework for COVID-19 Diagnosis in Chest CT Scans”. It is highly appreciated if the respected editor and reviewers accept the revised title as we changed in this revision. Changes to the Affiliations: The affiliations were revised and the symbol was removed from the authors D. Rahmati and A. Kiani. Changes to the Abstract: We significantly revised the abstract according to the reviewer’s comment. The new abstract follows the same semantic and sentencing structure as the previous one but written in more eloquent English letting a broader range of audiences understand the science behind our article. Changes to the Introduction Section: • A sentence to discuss the COVID-19 pandemic, number of cases, deaths, and etc. was added to the first paragraph with appropriate citations. • Added a sentence to discuss COVID-19 vaccines and the challenges with appropriate citation. • The Paper's contributions and achievements were summarized at the end of the introduction section. • The manuscript has been carefully edited by a native English speaker to improve grammar and eloquence. Changes to the Materials and Methods Section: • Added reference to the figures. • Addressed the individual reviewer points. • The entire manuscript has been carefully edited by a native English speaker to improve grammar and readability. Changes to the Results Section: • We included references to the figures and tables. • All of the figures and tables in the Supporting Information section are now referenced. • Added a diagnosis time comparison table and its appropriate referencing. • The paper has been carefully edited by a native English speaker to improve grammar and readability. Changes to the Conclusions Section: The conclusion section has been carefully edited by a native English speaker to improve grammar and readability. Changes to the Supporting Information Section: • Slight changes were made to the Supporting Fig 1. • Captions for figures and tables were modified. • Multiple grammatical corrections were made. Response to reviewers comments: We sincerely appreciate all your valuable comments and suggestions, which helped us to improve the quality of the article. Our responses to the respected reviewer’s comments are described below in a point-to-point manner. The changes, suggested by the reviewers, have been described to the new version of the manuscript (highlighted in blue color within the document). Response to Reviewer #1: The paper describes a CNN-based model for classification of 3D-CTs as either presenting COVID-19, having several other abnormalities, or being normal. An ImageNet pretrained EfficientNetB3 is used as a slice-wise feature extractor. Then, the features from all slices are combined by averaging and classified using a fully connected layer. The model achieves compelling results, outperforming on its own even experts helped by the model for the task of COVID-19 vs others classification. The datasets employed and the validation techniques are all sound and there is a very complete array of numerical comparisons with 4 radiologists. I recommend it for publication, although there are some comments that should be addressed: Thank you for the excellent comments and constructive review. The paper requires English review. I am no native English speaker, but only in the abstract, I can find already several grammatical mistakes: “We employed three independent dataset”, “Our results show thatai-coronaoutperforms all”, “for which it gained AUC score of 0.997”, “our framework’s diagnosis capabilities was evaluated”. Thank you. The manuscript has been carefully edited by a native speaker. Line 77: The differences between NCA (non COVID-19 abnormal), non-pneumonia, and normal classes should be clarified, and also what pathologies might represent each of the first two. We thank the reviewer for their feedback. We agree with the reviewer’s comment regarding the lack of clarity in the classification of covid categories and as a result, we have elaborated upon these differences more carefully. For the difference between the NCA and the non-pneumonia classes, we added the following explanation to the Data subsection: "having the NCA class is crucial, as it includes abnormalities such as atelectasis, cardiomegaly, lung emphysematous, hydropneumothorax, pneumothorax, cardiopulmonary edema, cavity, fibrocavitary changes, fibrobronchiectatic, mass, and nodule." Line 176: Average pooling is employed to combine the final activation maps from each slice into a single activation map for the whole CT. However, this ignores any kind of spatial information contained along the z-axis, as it is all averaged. Furthermore, using average pooling might bury any positive detection among many negative ones. Have you tried using another combination method that is aware of the z-axis information (such as 3D pooling, instead of 2D, or using 3D convolutions)? Thank you. The reviewer’s concern about the neglection of z-axis information is valid. We also were aware of this reduction and accordingly we compared our algorithm with other combination methods. In the initial phases of our model development prototyping, we experimented with 3D-CNN models that extract the z-axis information such as 3D-ResNet. The results were atrocious and this method was discarded. Have you tried using max-pooling instead of average pooling? We initially used max-pooling in our model's last layer (following the same as the MRNet paper). But our experiments indicate that average pooling has better results comparing to max-pooling in our algorithm so we only reported the algorithm with average-pooling. As we know the Average-pooling encourages the network to identify the complete extent of the object, whereas max-pooling restricts that to only the very important features, and might miss out on some details. Based on this intuition we speculate the class information is encoded in population-pixel level so the average-pooling performs better in our experiments. Also, I would have suggested to add the previous and next slice to the input of each EfficientNet feature extractor, instead of repeating the same slice three times, as way to add some z-axis information to each prediction. Thank you for the suggestion. For the model input size, we experimented with two settings; EfficientNet's standard input size and the scan's default size of 512512. The second one was shown significant results and better descriptions. It is worth mentioning that our model's input is a 3-channel grayscale image that is the standard practice of many experts in the literature. As we were satisfied with our results and we didn't have time to experiment with other methods, we decided to commit to this practice. Line 189: Do you use EfficientNetB5 or B3? B5 is mentioned here, but B3 is mentioned in the rest of the text. Also, Figure 1 shows that the input images are of resolution 512x512, but that is not the input resolution of the EfficientNetB3, nor the B5. Thank you for the important point and sorry for the error typo. The employed feature extractor is based on EfficientNetB3, but was mistakenly typed as EfficientNetB5. This is fixed now. Response to Reviewer #2: Thank you for raising the following points and constructive review and comments: # In the introductory section, the first paragraph should discuss about COVID-19 pandemic, number of cases, deaths, recovered and the taxonomy of the virus etc. You have raised an important point here and have incorporated your suggestion throughout the manuscript. Accordingly, we briefly discussed the COVID-19 pandemic, number of cases, deaths, and vaccination in the first paragraph of the introduction. Please see the highlighted blue color in the main text of the revised manuscript. # Author should stress on why their contribution stand out or achieved better result than others. In all the tables, the best results were made bold and we also added a paragraph at the end of the introduction. # There should be bullet points on authors contribution at the end of introduction. Thank you. We reimplemented the author’s contribution section as you mentioned. # Author should add a paragraph on COVID-19 vaccines. Thanks for raising this point. Following the reviewer’s suggestion and the global attempt in the development of the COVID-19 vaccines and their effect on the treatment of newly infected subjects, we briefly added a sentence in the first paragraph of the introduction section elaborating upon this issue. Particularly with the low protection capabilities of existing vaccines, we claim the development of radiologist assistance technologies for detection and treatment of COVID-19 are still essential. # Author need to add a section on “Related work” and a table to summarized contributions in terms of dataset and performance evaluation. Thank you. We also believe the contribution of each data set should be reported individually, accordingly to the introduction section, the related works were sufficiently discussed. Also, the results for the two similar works were present in Table 2. # Author need to add a limitations and future work. Our work's limitations and future directions were described in the conclusion and discussion. # Authors can also add these references 1. Chowdhury, M. E., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B., ... & Islam, M. T. (2020). Can AI help in screening viral and COVID-19 pneumonia?. IEEE Access, 8, 132665-132676. 2. Ibrahim, A. U., Ozsoz, M., Serte, S., Al-Turjman, F., & Yakoi, P. S. (2021). Pneumonia classification using deep learning from chest X-ray images during COVID-19. Cognitive Computation, 1-13. 3. Burki, T. K. (2020). The Russian vaccine for COVID-19. The Lancet Respiratory Medicine, 8(11), e85-e86. Thank you. We included these two references (1 and 2) in the introduction and reference sections. Attachments Attachment Submitted filename: final_Response to Reviewers_PLOS One.pdf* https://doi.org/10.1371/journal.pone.0250952.r002
19 Apr 2021 Decision Letter - Gulistan Raja, Editor ai-corona: Radiologist-Assistant Deep Learning Framework for COVID-19 Diagnosis in Chest CT Scans PONE-D-21-04656R1 Dear Dr. Lashgari, 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, Gulistan Raja Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0250952.r003
Formally Accepted
27 Apr 2021 Acceptance Letter - Gulistan Raja, Editor PONE-D-21-04656R1 ai-corona: Radiologist-Assistant Deep Learning Framework for COVID-19 Diagnosis in Chest CT Scans Dear Dr. Lashgari: 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. Gulistan Raja Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0250952.r004

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