A machine learning model of microscopic agglutination test for diagnosis of leptospirosis

Yuji Oyamada; Ryo Ozuru; Toshiyuki Masuzawa; Satoshi Miyahara; Yasuhiko Nikaido; Fumiko Obata; Mitsumasa Saito; Sharon Yvette Angelina M. Villanueva; Jun Fujii

doi:10.1371/journal.pone.0259907

Peer Review History

Original SubmissionJune 24, 2021
24 Jun 2021 Author Response Transfer Alert This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present. Attachments Attachment Submitted filename: Response_to_reviewers_20210514.docx https://doi.org/10.1371/journal.pone.0259907.r001
17 Sep 2021 Decision Letter - Kalimuthusamy Natarajaseenivasan, Editor PONE-D-21-20745A Machine Learning Model of Microscopic Agglutination Test for Diagnosis of LeptospirosisPLOS ONE Dear Dr. Ozuru, 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. ACADEMIC EDITOR: Include a brief description about the machine learning and its advantage in the field of medicine. Appropriate literature back up is necessary to support some new descriptions. Please submit your revised manuscript by Oct 30 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. 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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 2. Thank you for stating in your Funding Statement: This work was partially supported by JSPS KAKENHI Grant Number 18K16174 and 21K16320 to R.O., the discretionary fund of Tottori University President to Y.O. and R.O, and the Research Program of the International Platform for Dryland Research and Education, Tottori University to J.F. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Please provide an amended statement that declares all the funding or sources of support (whether external or internal to your organization) received during this study, as detailed online in our guide for authors at http://journals.plos.org/plosone/s/submit-now. Please also include the statement “There was no additional external funding received for this study.” in your updated Funding Statement. Please include your amended Funding Statement within your cover letter. We will change the online submission form on your behalf. [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: N/A Reviewer #2: Yes ****** 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: In this manuscript by Oyamada et al., the authors established a machine learning model to improve the microscopic agglutination test for the diagnosis of leptospirosis, one of the most widespread zoonoses in the world. The number of leptospirosis cases is underestimated in many countries due to the technical difficulties of the current available methods, thus, the development of this new method will greatly contribute to increase the number of laboratory-confirmed leptospirosis cases and to know the true burden of this neglected infectious disease. I have some comments that can be useful to improve the manuscript within the scope of PLOS ONE: 1. INTRODUCTION: The detailed explanation of the microscopic agglutination test (MAT) is correctly provided, however, the background of machine learning methods applied to diagnosis in medicine is not presented. Please provide a background, with the appropriate references, on how image analysis and machine learning techniques have contributed to the diagnosis of other diseases. Some examples are: https://doi.org/10.1016/j.cmpb.2018.05.034 doi: 10.3389/fonc.2021.608191. 2. Provide also in the Introduction the appropriate background and references for image classification techniques, including the one used in this study (binary) using support vector machines, so that readers outside the field can understand the methodology presented in this study. With the use of appropriate references, the Fig. 1 can be omitted or presented as a supporting information. 3. MATERIALS and METHODS: Please provide a flowchart of the pipeline developed in this study as a Figure as in Ref. doi: 10.3389/fonc.2021.608191. (Fig. 2). 4. With the use of a flowchart, I suggest to brush-up the section “Materials and Methods”. For example some phrases such as,”In the remainder of this part,……, will be explained step by step” (last lanes of Image features paragraph) can be deleted. Other redundant phrases can be also deleted. 5. I suggest that a selection of panels for the main results of this study might be presented as Figures and that any other information presented in the current version as Fig. might be presented as “Supplementary Information” (i.e. Figures 1~5). Some panels of Figure 7-19 can be combined in one figure. The comparison of related-panels in one Fig. (as presented in Fig. 22) will help readers to follow the results. 6. Is it possible to compare HoW-0 and HoW-4 features of true negative, false negative, true positive, false positive in one Figure? Minor comments: 1. Please include line numbers in the Manuscript which will help the revision process. 2. Check spelling, i.e.: “grasses” should be “glasses” in the second paragraph of Introduction. 3. The reference (Boser et al.,1992) for SVM is mentioned in the paragraph of “Image feature classification”, however, is not added in the Reference section. Reviewer #2: In this study, automatic identification algorithm of MAT was developed and the possibility of machine learning diagnosis of MAT images was showed. The manuscript is well written and results are interesting. The presented data in revised manuscript is completely supports the conclusion of this study. The authors should be commended for conducting this novel and unique study to automatize the cumbersome and time-consuming MAT procedure. The only limitation of the study is evaluation restricted with one serovar. ****** 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: Yes:** SUMAIYA K [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. https://doi.org/10.1371/journal.pone.0259907.r002
Revision 1
1 Oct 2021 Author Response Dear Dr. Natarajaseenivasan, Editor of PLOS ONE, We would like to thank all the editor and reviewers very much for reviewing our manuscript. We would like to submit a revised version of our manuscript entitled “A Machine Learning Model of Microscopic Agglutination Test for Diagnosis of Leptospirosis”, manuscript number PONE-D-21-20745 with all modifications following reviewers’ advice. Our responses to reviewers’ comments are below. ACADEMIC EDITOR: Include a brief description about the machine learning and its advantage in the field of medicine. Appropriate literature back up is necessary to support some new descriptions. We appreciate your decision regarding our manuscript. Following the advice of the editor and reviewers, a description of the significance of machine learning in medicine has been added to INTRODUCTION section (page 4, lines 79-80, page 5, lines 81-101). Accordingly, some references have been added (references 7-14). Moreover, Tables 5 “The elapsed time for training per dataset [sec.].” and 6 “The elapsed time for test per patch [msec.].”, which were referred but not contained in the manuscript were added at the end of RESULTS section. Additionally, we have removed paragraphs from the DISCUSSION section that are not needed in this paper. We trust that this revised manuscript will answer all the questions and be improved to fit PLOS ONE. Reviewer #1: In this manuscript by Oyamada et al., the authors established a machine learning model to improve the microscopic agglutination test for the diagnosis of leptospirosis, one of the most widespread zoonoses in the world. The number of leptospirosis cases is underestimated in many countries due to the technical difficulties of the current available methods, thus, the development of this new method will greatly contribute to increase the number of laboratory-confirmed leptospirosis cases and to know the true burden of this neglected infectious disease. I have some comments that can be useful to improve the manuscript within the scope of PLOS ONE: We appreciate your detailed reviews and constructive comments. The manuscript has been revised as follows according to the directions. 1. INTRODUCTION: The detailed explanation of the microscopic agglutination test (MAT) is correctly provided, however, the background of machine learning methods applied to diagnosis in medicine is not presented. Please provide a background, with the appropriate references, on how image analysis and machine learning techniques have contributed to the diagnosis of other diseases. Some examples are: https://doi.org/10.1016/j.cmpb.2018.05.034 doi: 10.3389/fonc.2021.608191. Descriptions of machine learning and its medical applications are added in lines 79-89. We also cited several papers as references 7-9. 2. Provide also in the Introduction the appropriate background and references for image classification techniques, including the one used in this study (binary) using support vector machines, so that readers outside the field can understand the methodology presented in this study. With the use of appropriate references, the Fig. 1 can be omitted or presented as a supporting information. We added background explanations on image identification and classification in lines 90-101. New citations #11-14 have been added accordingly. Just in case, the original description of binary classification and Fig. 1 have been moved to Supporting information (S1 File and S1 Fig, respectively). 3. MATERIALS and METHODS: Please provide a flowchart of the pipeline developed in this study as a Figure as in Ref. doi: 10.3389/fonc.2021.608191. (Fig. 2). We inserted a flowchart as Fig 1 as you suggested. 4. With the use of a flowchart, I suggest to brush-up the section “Materials and Methods”. For example some phrases such as,”In the remainder of this part,……, will be explained step by step” (last lanes of Image features paragraph) can be deleted. Other redundant phrases can be also deleted. Based on the flowchart, we modified the section “Materials and Methods”. Specifically, the following points have been corrected. - Deleted some redundant phrases such as you pointed out. - Moved image processing methods to Supporting information (S1 File). - Moved Table 1 and 2 to the end of “preparation of image datasets of MAT” 5. I suggest that a selection of panels for the main results of this study might be presented as Figures and that any other information presented in the current version as Fig. might be presented as “Supplementary Information” (i.e. Figures 1~5). Some panels of Figure 7-19 can be combined in one figure. The comparison of related-panels in one Fig. (as presented in Fig. 22) will help readers to follow the results. We reorganized figures and tables. Concretely, - Figs 1-4 are moved to Supporting information (S1-S4 Figs). - Among Figs 7-19, Figs 7 (Raw images), 12 (Wavelet-3), and 18 (HoW-3) are reorganized as Fig 4, because we think it is important to compare them together. The rest have been combined and moved to Supporting information (S5 Fig). 6. Is it possible to compare HoW-0 and HoW-4 features of true negative, false negative, true positive, false positive in one Figure? The comparison results of your proposal are now shown in Fig 7. Minor comments: 1. Please include line numbers in the Manuscript which will help the revision process. We included line numbers in the manuscript. 2. Check spelling, i.e.: “grasses” should be “glasses” in the second paragraph of Introduction. The following spelling corrections and proofreading of the manuscript have been made. ABSTRACT Line33: Leptospiras -> Leptospires Line40: aggregation -> agglutination Line42: images created -> images created Line42: Leptospira- -> Leptospira- INTRODUCTION Line58: 70% occurring -> 70% is occurring Line67: dilution agglutinating -> dilution which agglutinates Line69: i.e. -> i.e. Line70: personnel making -> personnel, thus making Line71: grasses -> glasses Line72: takes time -> time consuming Lines74-75: partially because -> because Lines75-76: MAT such as dark-field microscopes, objective lenses, illuminations and cameras, but -> MAT (dark-field microscopes, objective lenses, illuminations and cameras) vary, but also Line77: at the -> among Lines133-135: raw images because raw images are too complex and redundant, which is potentially badly distributed data. -> raw images, that are considered too complex, redundant and potentially badly distributed data. Line135: interesting -> characteristic Line136: a sort of numerical -> numerical Line137: Image -> Appropriate image Line138: which means each -> such that Line139: takes an important role -> is important Line141: machine learning -> a machine learning Line147: a -> an MATERIALS AND METHODS Line152: that takes -> which predict Line174: said University -> university Line178: syrian -> Syrian Line184: was -> were Line186: slide glasses -> glass slides Line187: Nikon -> Nikon, Tokyo, Japan Lines188-189: Olympus, Tokyo, Japan Lines249-250: objects such as dust that has strong -> objects as dust with strong Line252: Specifically, the designed -> The designed Line255: histogram of its coefficients -> histogram Line292: Namely, lower-level -> Lower-level Line293: larger-level -> higher-level Line297: an image and its multi-level -> same microscopic field in various multi-level Line299: levels, coarser -> levels, that coarser Line300: lower level -> lower-level Line300: finer in higher -> finer ones are in higher Lines301-302: features are depicted -> features such as points, curves, and edges with large coefficient changes are depicted Line317: agglutination -> agglutinated Line320: histograms of different -> different Line340: utilizes -> utilized Line341: well-used -> the well-used Line350: Using kernel trick -> Using a kernel trick Line351: An SVM -> a SVM Line361: utilizes -> utilized Line363: hyperparameter -> the hyperparameter RESULTS Line394: for -> with Line405: test -> tested Line435: is fastened -> becomes faster Lines438-439: system that -> system, in which Line443: all the data -> all datasets Lines458-459: red x and purple + symbols represent negative and positive data respectively and -> red (x) and purple (+) symbols represent negative and positive data, respectively, and Line463: distinctly -> clearly Lines471-472: Red x and purple + symbol represent the feature -> Red (x) and purple (+) symbols represent the features Line524: lower level -> lower-level Lines539-541: from -1 to 1, -1, where -1 means all predictions are wrong, 0 means the predictions are equivalent to random prediction, and 1 means all predictions are correct. -> from -1 to 1, of which all predictions are wrong (value is -1), equivalent to random prediction (value is 0), or correct (value is 1). Line549: \\ -> (\\) Line550: / -> (/) Line550: do -> indicate Line567: training -> trainings Line568: dataset -> datasets DISCUSSION Line589: considered -> considered as Lines596-598: However, these alternatives are just simple rapid screening tests, and MAT is needed as ever in high-density areas where precisely testing with more serovars. -> However, these alternative tests are for screening, whereas MAT is needed for precise serotype diagnosis in areas of high prevalence. Line603: primarily attempted -> attempted Lines606-607: MAT images, especially combined HoW resulted in the MCC score of 1.0, which means perfect prediction. -> MAT images in a good precision, especially with combined HoW which resulted in the MCC score of 1.0 (a perfect prediction). Lines608-609: the skilled examiners’ knowledge and experience -> nowledge and experience of the skilled examiner Line610: adding parameters progress capability -> addition of parameters progresses capability Lines612-613: There still remains some space to improve the proposed method. -> Some spaces f improvements still remain in the proposed method. Lines613-614: procedure by comparing MAT images -> procedure determines positivity by comparing test MAT images Line621-626: Although a lot of image data was acquired as a result, the limitation of this study is that we tested only one serotype condition, serovar Manilae, with a positive and a negative serum, and an image acquisition device. We need to study and validate with data from other serovars and devices but conclude at least that the direction of the image analysis has been adequately investigated. -> The limitation of this study is that we tested only one serotype condition, serovar Manilae, with a positive and a negative serum with an image acquisition device. Although we can conclude at least that the direction of the image analysis has been adequately investigated, we need to study and validate with data from other serovars and devices in the future. Line636: Usually, MAT requires -> Standard MAT procedure requires Line637: complicated -> laborsome Line638-639: procedures, -> procedures in addition to the image analysis algorithm presented in this paper, Line642-644: Furthermore, we can collect a huge amount of data from all over the world that is an image of the future research style. -> Moreover, the cloud-based system will lead to collect a large amount of data from all over the world that will progress diagnosis of diseases further. CONCLUSION Lines647-648: toward ultimate -> toward the ultimate Line648: Leptospirosis -> leptospirosis Line652: HoW features -> HoW Line652: these -> this Line654: in process -> in the process 3. The reference (Boser et al.,1992) for SVM is mentioned in the paragraph of “Image feature classification”, however, is not added in the Reference section. Boser et al. 1992 is cited as #17 in the revised manuscript. Reviewer #2: In this study, automatic identification algorithm of MAT was developed and the possibility of machine learning diagnosis of MAT images was showed. The manuscript is well written and results are interesting. The presented data in revised manuscript is completely supports the conclusion of this study. The authors should be commended for conducting this novel and unique study to automatize the cumbersome and time-consuming MAT procedure. The only limitation of the study is evaluation restricted with one serovar. Thank you for identifying critical points of our study. We appreciate your comments. We also understand importance of evaluating the algorithm with other serovars and actual clinical samples, therefore we are planning future experiments utilizing the WHO panel and clinical samples in the coming project. Attachments Attachment Submitted filename: Response_to_reviewers_20211001_2.docx https://doi.org/10.1371/journal.pone.0259907.r003
29 Oct 2021 Decision Letter - Kalimuthusamy Natarajaseenivasan, Editor A Machine Learning Model of Microscopic Agglutination Test for Diagnosis of Leptospirosis PONE-D-21-20745R1 Dear Dr. Ozuru, 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, Kalimuthusamy Natarajaseenivasan 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 ******** 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: (No Response) Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: (No Response) 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 Response) Reviewer #2: 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 #1: (No Response) 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: (No Response) Reviewer #2: (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 #1: No Reviewer #2: Yes:** SUMAIYA K https://doi.org/10.1371/journal.pone.0259907.r004
Formally Accepted
6 Nov 2021 Acceptance Letter - Kalimuthusamy Natarajaseenivasan, Editor PONE-D-21-20745R1 A Machine Learning Model of Microscopic Agglutination Test for Diagnosis of Leptospirosis Dear Dr. Ozuru: 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. Kalimuthusamy Natarajaseenivasan Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0259907.r005

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