Neural network-based method to stratify people at risk for developing diabetic foot: A support system for health professionals

Ana Cláudia Barbosa Honório Ferreira; Danton Diego Ferreira; Bruno Henrique Groenner Barbosa; Uiara Aline de Oliveira; Estefânia Aparecida Padua; Felipe Oliveira Chiarini; Maria Helena Baena de Moraes Lopes

doi:10.1371/journal.pone.0288466

Peer Review History

Original SubmissionSeptember 27, 2022
13 Dec 2022 Decision Letter - Imran Ashraf, Editor PONE-D-22-26803Neural network-based method to stratify people at risk for developing diabetic foot: a support system for health professionalsPLOS ONE Dear Dr. Ferreira, 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. ============================== Please submit your revised manuscript by Jan 27 2023 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). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. 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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. Please provide additional details regarding participant consent. In the ethics statement in the Methods and online submission information, please ensure that you have specified what type you obtained (for instance, written or verbal, and if verbal, how it was documented and witnessed). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information. 3. Please include your full ethics statement in the ‘Methods’ section of your manuscript file. In your statement, please include the full name of the IRB or ethics committee who approved or waived your study, as well as whether or not you obtained informed written or verbal consent. If consent was waived for your study, please include this information in your statement as well. 4. Your ethics statement should only appear in the Methods section of your manuscript. If your ethics statement is written in any section besides the Methods, please move it to the Methods section and delete it from any other section. Please ensure that your ethics statement is included in your manuscript, as the ethics statement entered into the online submission form will not be published alongside your manuscript. 5. Please upload a new copy of Figure xxxx as the detail is not clear. Please follow the link for more information: " ext-link-type="uri" xlink:type="simple">https://blogs.plos.org/plos/2019/06/looking-good-tips-for-creating-your-plos-figures-graphics/" https://blogs.plos.org/plos/2019/06/looking-good-tips-for-creating-your-plos-figures-graphics [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: Partly ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: No 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: No 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: Yes 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 review of the paper entitled “Neural network-based method to stratify people at risk for developing diabetic foot: a support system for health professionals” Background and Objective:Diabetes Mellitus (DM) is a chronic disease with a high prevalence worldwide. Diabetic foot is one of the DM complications and compromises health and quality of life, due to the risk of lower limb amputation. This work is aimed to build a risk classification system for the development of the diabetic foot, using Artificial Neural Networks (ANN). Methods: It is a methodological study in which two databases were used, one for system design (training and validation) containing 250 people with DM and another for testing, containing 141 people. Each person answered a questionnaire with 54 questions about foot care and sociodemographic information. People from both databases were classified by specialists as high or low risk for diabetic foot. Supervised ANN (multi-layer Perceptron - MLP) models were exploited and a smartphone app was built. The app returns a personalized report indicating self-care for each user. System Usability Scale (SUS) was used for the usability evaluation. I have found the following concerns which need to correct… 1. The introduction part is in multiple paragraphs, please rearrange the article text. Arrange multiple paragraphs in one if applicable. 2. I never found any novel work that is highlighted. 3. Its confusion for readers that what is the data in your article? Still authors talking about the patients, please make sure that which kind of data are you using? 4. ANN is a very much older method, if authors try to apply any deep learning method, it could be helpful. 5. Authors only talking about the ANN is different forms, and try to change the dataset but not the original method. But still readers are unaware of data, what is the input to the system? 6. Figure one and other figures are not according to the standard. It should be 300dpi. 7. Please add more recent papers in the references. 8. Try to compare your work with others in the same domain. Reviewer #2: The manuscript focuses on detecting Diabetes Mellitus (DM) by using AI technique. Reducing the contributory reasons of DM from 54 to 10, appears to be a contribution of this paper. Although, with the available compute power today, processing 54 attributes for class identification is not a challenge, however, from an end-user perspective, data collection against 10 questions would be more efficient. As a suggestive added check, identification of most important parameters in low risk categorise may be re-validated. The quality of images used in the paper also needs improvement for enhanced visibility. The manuscript is written nicely and presents an important issue related to healthcare. The paper is recommended for approval. ****** 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: Raye Mahmood Ahmad ******** [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.0288466.r001
Revision 1
3 Jan 2023 Author Response Reviewer #1: The review of the paper entitled “Neural network-based method to stratify people at risk for developing diabetic foot: a support system for health professionals” Background and Objective: Diabetes Mellitus (DM) is a chronic disease with a high prevalence worldwide. Diabetic foot is one of the DM complications and compromises health and quality of life, due to the risk of lower limb amputation. This work is aimed to build a risk classification system for the development of the diabetic foot, using Artificial Neural Networks (ANN). Methods: It is a methodological study in which two databases were used, one for system design (training and validation) containing 250 people with DM and another for testing, containing 141 people. Each person answered a questionnaire with 54 questions about foot care and sociodemographic information. People from both databases were classified by specialists as high or low risk for diabetic foot. Supervised ANN (multi-layer Perceptron - MLP) models were exploited and a smartphone app was built. The app returns a personalized report indicating self-care for each user. System Usability Scale (SUS) was used for the usability evaluation. I have found the following concerns which need to correct… 1. The introduction part is in multiple paragraphs, please rearrange the article text. Arrange multiple paragraphs in one if applicable. RESPONSE: Thank you for this suggestion. We have rearranged the introduction section in which some paragraphs were arranged in one. 2. I never found any novel work that is highlighted. RESPONSE: We have rewritten this sentence in the new manuscript version. 3. Its confusion for readers that what is the data in your article? Still authors talking about the patients, please make sure that which kind of data are you using? RESPONSE: Our database consisted of 54 pieces of information from 250 people with diabetes. These data were collected via questionnaire in a health institution in a city in the state of Minas Gerais, Brazil. The instrument (questionnaire) used for data collection covered the risk factors for the development of diabetic foot proposed by the International Consensus on Diabetic Foot, and by the Primary Care Booklet to Assist People with DM from the Ministry of Health of Brazil, which resulted in a total of 54 variables. Variables related to people's self-care habits concerning their health, foot care, perceived changes in the feet, as well as sociodemographic and socioeconomic variables were included. This information is in the first two paragraphs of Section 2. To make this part clearer, we rewrote the first paragraph. 4. ANN is a very much older method, if authors try to apply any deep learning method, it could be helpful. RESPONSE: In this paper we used a multi-layer perceptron (MLP), which is the most popular type of artificial neural network (ANN). An MLP is an ANN capable of handling both linearly separable and non-linearly separable data. It belongs to a class of neural networks known as feed-forward neural networks, which connect the neurons in one layer to the next layer in a forward manner. It consists of interconnected neurons which process data through three or more layers. The basic structure of an MLP consists of an input layer, one or more hidden layers and an output layer. There is no restriction on the number of hidden layers, however, an MLP usually has a small number of hidden layers. In our work we used only one hidden layer. Due to their simplicity, MLPs usually require short training times to learn the representations in data and produce an output. A Deep Neural Network (DNN) is simply an artificial neural network with deep layers. Deep layers in this context mean that the network has several layers stacked together for processing and learning from data. It is important to note that an MLP is considered an example of DNNs. Due to their complex nature, DNNs usually require long periods of time to train the network on the input data. Additionally, they require powerful computers with specialized processing units such as Tensor Processing Units (TPU) and Neural Processing Units (NPU). It is possible that the use of a DNN instead of the simple MLP employed may lead to a model with slightly higher accuracy. On the other hand, it will require more data to be trained, a specialized processing unit, and probably would result in a more complex model. As we discussed in Section 5.1, we opted by more parsimonious models, and, in this case, the MLP is more appropriated than a DNN. 5. Authors only talking about the ANN is different forms, and try to change the dataset but not the original method. But still readers are unaware of data, what is the input to the system? RESPONSE: To make the data description better, we have rewritten section 2 so that more details about the variables and their codification was added in the new manuscript version. In addition, we included paragraph 4 in Section 4 of the new manuscript version, where we described the MLP inputs for each approach developed. 6. Figure one and other figures are not according to the standard. It should be 300dpi. RESPONSE: The figures were edited accordingly. 7. Please add more recent papers in the references. RESPONSE: He revised the paper and included seven new references of the last two years. 8. Try to compare your work with others in the same domain. RESPONSE: In Section 5.2, Table 3, of the submitted manuscript version, we have already presented a comparison between our best model and a Competitive Neural Layer model proposed in reference [11] for the same purpose. In this case, the model proposed in [11] was implemented and applied to the same testing data set. In addition, in the Discussion subsection we have related our achievements with other works in the same domain. Reviewer #2: The manuscript focuses on detecting Diabetes Mellitus (DM) by using AI technique. Reducing the contributory reasons of DM from 54 to 10, appears to be a contribution of this paper. Although, with the available compute power today, processing 54 attributes for class identification is not a challenge, however, from an end-user perspective, data collection against 10 questions would be more efficient. As a suggestive added check, identification of most important parameters in low risk categorise may be re-validated. The quality of images used in the paper also needs improvement for enhanced visibility. RESPONSE: Thank you for your positive comments and suggestions. We have included a sentence in paragraph five of the new manuscript version to highlight the contribution of the paper in making the data collection easier for the end-user by reducing the contributory reasons of the diabetic foot risk. Regarding the identification of most important parameters in low risk group, we have rewritten the last paragraph of Section 5.2 to make this part clearer. The manuscript is written nicely and presents an important issue related to healthcare. The paper is recommended for approval. RESPONSE: Thank you for the positive comments. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0288466.r002
21 Feb 2023 Decision Letter - Imran Ashraf, Editor PONE-D-22-26803R1Neural network-based method to stratify people at risk for developing diabetic foot: a support system for health professionalsPLOS ONE Dear Dr. Ferreira, 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. Please submit your revised manuscript by Apr 07 2023 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). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-emailutm_source=authorlettersutm_campaign=protocols. We look forward to receiving your revised manuscript. Kind regards, Imran Ashraf, Ph.D. Academic Editor PLOS ONE Journal Requirements: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. [Note: HTML markup is below. Please do not edit.] 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 #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 #1: Yes Reviewer #3: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: (No Response) 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 #1: Yes 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 #1: Yes Reviewer #3: (No Response) ****** 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: Review of the article entitled “Neural network-based method to stratify people at risk for developing diabetic foot: a support system for health professionals”. Diabetes Mellitus (DM) is a chronic disease with a high 16 prevalence worldwide. Diabetic foot is one of the DM complications and compromises 17 health and quality of life, due to the risk of lower limb amputation. This work is aimed 18 to build a risk classification system for the development of the diabetic foot, using 19 Artificial Neural Networks (ANN). The article has been prepared according to my suggestion. Now have the following minor concerns. 1. Thoroughly check the grammatical errors. 2. Add some references in the background study which improve your paper’s literature review, for example… 10.1109/ACCESS.2019.2896961 10.1109/ACCESS.2021.3056516 https://doi.org/10.3390/app9010069 https://doi.org/10.1155/2022/7954333 https://doi.org/10.3390/math10050796 https://doi.org/10.1155/2022/4942637 Reviewer #3: In this paper, the authors are proposed “Neural network-based method to stratify people at risk for developing diabetic foot: a support system for health professionals ”. The strengths of the paper are that it is well structured, the description of the related work is well done and that results are extensively compared to results of the similar research. The authors addressed all the comments. So, i recommend to accept this manuscript. ****** 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 #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. 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.0288466.r003
Revision 2
18 Mar 2023 Author Response Response to reviewer #1 We carefully reviewed the manuscript. The suggested references are related to image processing and segmentation methods based on Deep Learning approaches. Thus, we decided to reference these on the conclusion section where we described our intention for future works. Response to Reviewer #3 Thank you for the positive comments. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0288466.r004
20 Apr 2023 Decision Letter - Imran Ashraf, Editor PONE-D-22-26803R2Neural network-based method to stratify people at risk for developing diabetic foot: a support system for health professionalsPLOS ONE Dear Dr. Ferreira, 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. Please submit your revised manuscript by Jun 04 2023 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). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-emailutm_source=authorlettersutm_campaign=protocols. We look forward to receiving your revised manuscript. Kind regards, Imran Ashraf, Ph.D. Academic Editor PLOS ONE Journal Requirements: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. [Note: HTML markup is below. Please do not edit.] 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: (No Response) ******** 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: Partly ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: 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: 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: 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: Review of the manuscript entitled “Neural network-based method to stratify people at risk for developing diabetic foot: a support system for health professionals”. Diabetes Mellitus (DM) is a chronic disease with a high prevalence worldwide. Diabetic foot is one of the DM complications and compromises health and quality of life, due to the risk of lower limb amputation. This work is aimed to build a risk classification system for the development of diabetic foot, using Artificial Neural Networks (ANN). Methods: It is a methodological study in which two databases were used, one for system design (training and validation) containing 250 people with DM and another for testing, containing 141 people. Each person answered a questionnaire with 54 questions about foot care and socio-demographic information. People from both databases were classified by specialists as high or low risk for the diabetic foot. Supervised ANN (multi-layer Perceptron - MLP) models were exploited and a smartphone app was built. The app returns a personalized report indicating self-care for each user. The system Usability Scale (SUS) was used for the usability evaluation. Results: I have read the updated paper and still I found some issues which need to be corrected. 1. Discussion is poor, please add a table in the discussion section that should compare your results with other authors in the same domain. 2. Please add more recent medical and deep learning research papers in the background section. https://doi.org/10.1109/ACCESS.2019.2896961 https://doi.org/10.1109/ACCESS.2021.3056516 https://doi.org/10.3390/app9010069 https://doi.org/10.1155/2022/7954333 https://doi.org/10.3390/math10050796 https://doi.org/10.1155/2022/4942637 3. Still have a lot of grammatical errors. ****** 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 ******** [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.0288466.r005
Revision 3
4 Jun 2023 Author Response Answers to the Reviewers Reviewer #1: Review of the manuscript entitled “Neural network-based method to stratify people at risk for developing diabetic foot: a support system for health professionals”. Diabetes Mellitus (DM) is a chronic disease with a high prevalence worldwide. Diabetic foot is one of the DM complications and compromises health and quality of life, due to the risk of lower limb amputation. This work is aimed to build a risk classification system for the development of diabetic foot, using Artificial Neural Networks (ANN). Methods: It is a methodological study in which two databases were used, one for system design (training and validation) containing 250 people with DM and another for testing, containing 141 people. Each person answered a questionnaire with 54 questions about foot care and socio-demographic information. People from both databases were classified by specialists as high or low risk for the diabetic foot. Supervised ANN (multi-layer Perceptron - MLP) models were exploited and a smartphone app was built. The app returns a personalized report indicating self-care for each user. The system Usability Scale (SUS) was used for the usability evaluation. Results: I have read the updated paper and still I found some issues which need to be corrected. 1. Discussion is poor, please add a table in the discussion section that should compare your results with other authors in the same domain. Authors: Thank you for your comment. We have included a further discussion at the end of Section 5.1, in which we considered recent works using machine learning approaches to access diabetic foot development. In addition, we have included Table 4 in the revised manuscript, which presents a comparison of methods in terms of feature selection and classification techniques, sample size, clinical exams, number of available variables, number of selected variables, and performance. 2. Please add more recent medical and deep learning research papers in the background section. https://doi.org/10.1109/ACCESS.2019.2896961 https://doi.org/10.1109/ACCESS.2021.3056516 https://doi.org/10.3390/app9010069 https://doi.org/10.1155/2022/7954333 https://doi.org/10.3390/math10050796 https://doi.org/10.1155/2022/4942637 Authors: Thank you for your suggestion, these are interesting papers. We have included more recent medical and deep learning research papers in the Introduction section. 3. Still have a lot of grammatical errors Authors: We are very sorry for missing these grammatical errors. We sent the revised manuscript for a professional grammar review and hope that there is no further error in this regard. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0288466.r006
28 Jun 2023 Decision Letter - Melissa Orlandin Premaor, Editor Neural network-based method to stratify people at risk for developing diabetic foot: a support system for health professionals PONE-D-22-26803R3 Dear Dr. Danton Ferreira, PhD, 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, Melissa Orlandin Premaor, M.D., Ph.D Academic Editor PLOS ONE 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 #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 #1: Yes Reviewer #3: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes 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 #1: Yes 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 #1: Yes 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 #1: The review of the article entitled “Diabetes Mellitus (DM) is a chronic disease with a high worldwide prevalence. Diabetic foot is one of the DM complications and compromises health and quality of life, due to the risk of lower limb amputation. This work aimed to build a risk classification system for the evolution of diabetic foot, using Artificial Neural Networks (ANN). Methods: This methodological study used two databases, one for system design (training and validation) containing 250 participants with DM and another for testing, containing 141 participants. Each subject answered a questionnaire with 54 questions about foot care and sociodemographic information. Participants from both databases were classified by specialists as high or low risk for diabetic foot. Supervised ANN (multi-layer Perceptron - MLP) models were exploited and a smartphone app was built. Now the paper is written and organized well. I have no new questions to ask, all of my suggestions are fulfilled by the authors. Reviewer #3: The research meets all applicable standards for the ethics of experimentation and research integrity. So i recommend to accept the research ****** 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 #3: No ******** https://doi.org/10.1371/journal.pone.0288466.r007
Formally Accepted
3 Jul 2023 Acceptance Letter - Melissa Orlandin Premaor, Editor PONE-D-22-26803R3 Neural network-based method to stratify people at risk for developing diabetic foot: a support system for health professionals Dear Dr. Ferreira: 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. Melissa Orlandin Premaor Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0288466.r008

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