Standardized evaluation of diabetic retinopathy using artificial intelligence and its association with metabolic dysfunction-associated steatotic liver disease in Japan: A cross-sectional study

Koji Komatsu; Kei Sano; Kota Fukai; Ryo Nakagawa; Takashi Nakagawa; Masayuki Tatemichi; Tadashi Nakano

doi:10.1371/journal.pone.0315752

Peer Review History

Original SubmissionAugust 24, 2024
4 Nov 2024 Decision Letter - Anna Di Sessa, Editor PONE-D-24-35486Standardized evaluation of diabetic retinopathy using artificial intelligence and its association with metabolic dysfunction-associated steatotic liver disease in Japan: A cross-sectional studyPLOS ONE Dear Dr. komatsu, 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 Dec 15 2024 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. 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If your data cannot be made publicly available for ethical or legal reasons (e.g., public availability would compromise patient privacy), please explain your reasons on resubmission and your exemption request will be escalated for approval. Additional Editor Comments : The study is interesting and well-organized. However, before considering the paper for potential publication, all the reviewers' comments need to be adequately addressed. [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: 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 manuscript presents a technically sound study that explores the relationship between diabetic retinopathy and metabolic dysfunction-associated steatotic liver disease using artificial intelligence. The statistical analysis is well-executed, with appropriate adjustments for confounders like age and BMI. However, the data used in the study is not publicly available due to privacy concerns, which limits transparency. The authors could improve the manuscript by considering ways to share anonymized data. Additionally, the study uses an opt-out consent method, and it would be helpful to clarify how this aligns with ethical standards for protecting patient privacy. Reviewer #2: Overall, the article is very well written and easily understandable. The theme itself is of relevance, given the prevalence of DM in the world population, and also it's growth, being DR one of the most damaging diabetes complications . On the medical and statistical aspects of this article, I have only one remark to make. In line 179, it's stated that " the prevalence of high DSR decreased as the SL stage increased, and this trend was consistent across sexes " when commenting about the reports show in table 2. But in said table, when you look and the numbers about the female population, that`s not the case. According to table 2, when using FIB4 classification, 8,7% of women staged as SL1 has DRS > 20 in opposition to 9, 6% of those staged as SL2. So, in this case, the trend observed among male`s in table 2 does not repeat itself in the female population. So, it`s unclear what the author`s meant with this statement. When it comes to the AI aspect of the presented work, there`s a fundamental question that should have been answered : why did the author`s chose RetCAD for this work? The only reference to this question that I`ve found in the text is in the lines 73-76 : "A previous study reported that the AI software RetCAD achieved a 95.1% area under the receiver operating characteristic (ROC) curve for detecting DR, although the severity was not determined, with a standard error of 90.1% and standard deviation of 90.6%". But this information does not answer the question, because a good tool to solve a problem is not, necessarily, the best tool for such problem. There`s plenty of research about AI analysis of retinal imaging, with a myriad of softwares available for testing and validation. There`s also different forms of analysis, as recognized in the text in the statement in lines 281- 283 " Third, our use of posterior-pole fundus images for DR evaluation might have led to the omission of peripheral retinal changes. The introduction of a wide-angle fundus imaging device or an AI software update could address this issue". Why did the author`s chose posterior-pole fundus image for this particular work them? Was it budgetary constraints? Software limitations? What`s the reason for utilizing only fundus imaging, instead of a multi-modal AI system that incorporates other forms of data such as angiography and optic coherence tomography? For more information about multi-modal systems in ophthalmology , see https://doi.org/10.1186/s40662-024-00405-1. The reasoning behind the choice of the AI system used in the work is important, because the author`s claim to have " established a standardized method for evaluating DR using AI and explored its correlation with MASLD across various stages" in lines 230-231. If other`s, reading this article, decide to validate and improve in the method`s proposed, it is important for them to known the motive`s for software choice. Was it because of it`s accuracy? Cost? Commercial availability? Widespread adoption? If the criteria utilized for such a choice was cost-benefit, for example, researchers working in a more high risk environment could consider chosing a different software, with better accuracy and higher cost. Because of the reasons previously stated, I sugest to be included in the text the motives behind the adoption of RetCAD for this work. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). 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Revision 1
28 Nov 2024 Author Response Emily Chenette Editor-in-Chief Anna Di Sessa Academic Editor It is a pleasure to communicate with you again in relation to the peer revision of our manuscript. First of all, I would like to thank the reviewers with whose comments the value of this paper increased immensely. Please note the following changes made in the manuscript in accordance with the reviewers’ comments. Additionally, the text added based on the advice is expressed in red, while the original text is in italics. We sincerely hope that the current version of the manuscript is acceptable for publication in the PLOS ONE. Koji Komatsu Reviewer #1 ♯1. The manuscript presents a technically sound study that explores the relationship between diabetic retinopathy and metabolic dysfunction-associated steatotic liver disease using artificial intelligence. The statistical analysis is well-executed, with appropriate adjustments for confounders like age and BMI. However, the data used in the study is not publicly available due to privacy concerns, which limits transparency. The authors could improve the manuscript by considering ways to share anonymized data. Additionally, the study uses an opt-out consent method, and it would be helpful to clarify how this aligns with ethical standards for protecting patient privacy. Response: Thank you for your feedback. This study does not involve any interventions, human biological specimens, or personally identifiable information requiring special consideration. Therefore, it complies with the "Ethical Guidelines for Medical and Health Research Involving Human Subjects" issued by the Ministry of Health, Labour, and Welfare. Based on your feedback, we have added more detailed content to the Materials and Methods section. Revisions：From line 87 to line 92. All studies were conducted in compliance with the tenets of the Declaration of Helsinki. Information was disclosed to the participants through an opt-out option on the Omiya City Clinic web page. Informed consent was obtained from all the participants by the opt-out method according to the Ethical Guidelines for Medical and Health Research Involving Human Subjects (Japanese Ministry of Health, Labour and Welfare). On April 8, 2022, data were accessed for research purposes. Furthermore, due to ethical regulations, the analyzed large dataset has not been made publicly available. However, it can be provided upon request for legitimate purposes. The content regarding this information has also been revised. Revisions：From line 300 to line 304. Data Availability Statement Data cannot be shared publicly because of ethical restrictions. Data are available from the Institutional Review Board of the Jikei University School of Medicine for researchers who meet the criteria for access to confidential data. The Jikei University Hospital Ethics Committee Secretariat 3-25-8 Nishi-Shimbashi, Minato-ku Tokyo, Japan, 105-8461 TEL:+81-3-3433-1111. Reviewer #2 ♯1. In line 179, it's stated that " the prevalence of high DSR decreased as the SL stage increased, and this trend was consistent across sexes " when commenting about the reports show in table 2. But in said table, when you look and the numbers about the female population, that`s not the case. According to table 2, when using FIB4 classification, 8,7% of women staged as SL1 has DRS > 20 in opposition to 9, 6% of those staged as SL2. So, in this case, the trend observed among male`s in table 2 does not repeat itself in the female population. So, it`s unclear what the author`s meant with this statement. Response: Thank you for your thorough review. As you pointed out, there was a notation error, which has been corrected as shown below. Revisions：From line 179 to line 183. Table 2 presents the participants’ characteristics according to SL stage and DRS with a cutoff value of 20 (CO20). Using the FIB-4 classification, a high DRS was observed in 16.0% (n = 95) of SL0, 11.9% (n = 74) of SL1, and 11.3% (n = 59) of SL2 cases. The prevalence of high DRS decreased as the SL stage increased. Moreover, at all SL stages, more male participants than female participants had a high DRS. ♯2. When it comes to the AI aspect of the presented work, there`s a fundamental question that should have been answered : why did the author`s chose RetCAD for this work? The only reference to this question that I`ve found in the text is in the lines 73-76 : "A previous study reported that the AI software RetCAD achieved a 95.1% area under the receiver operating characteristic (ROC) curve for detecting DR, although the severity was not determined, with a standard error of 90.1% and standard deviation of 90.6%". But this information does not answer the question, because a good tool to solve a problem is not, necessarily, the best tool for such problem. There`s plenty of research about AI analysis of retinal imaging, with a myriad of softwares available for testing and validation. There`s also different forms of analysis, as recognized in the text in the statement in lines 281- 283 " Third, our use of posterior-pole fundus images for DR evaluation might have led to the omission of peripheral retinal changes. The introduction of a wide-angle fundus imaging device or an AI software update could address this issue". Why did the author`s chose posterior-pole fundus image for this particular work them? Was it budgetary constraints? Software limitations? What`s the reason for utilizing only fundus imaging, instead of a multi-modal AI system that incorporates other forms of data such as angiography and optic coherence tomography? For more information about multi-modal systems in ophthalmology , see https://doi.org/10.1186/s40662-024-00405-1. The reasoning behind the choice of the AI system used in the work is important, because the author`s claim to have " established a standardized method for evaluating DR using AI and explored its correlation with MASLD across various stages" in lines 230-231. If other`s, reading this article, decide to validate and improve in the method`s proposed, it is important for them to known the motive`s for software choice. Was it because of it`s accuracy? Cost? Commercial availability? Widespread adoption? If the criteria utilized for such a choice was cost-benefit, for example, researchers working in a more high risk environment could consider chosing a different software, with better accuracy and higher cost. Because of the reasons previously stated, I sugest to be included in the text the motives behind the adoption of RetCAD for this work. Response: Thank you for your valuable comments. This study was conducted at Omiya City Clinic, a health screening facility in Japan. Japan has a unique preventive healthcare practice known as "Ningen Dock." In ophthalmologic examinations for "Ningen Dock," it is common to conduct visual acuity tests, intraocular pressure measurements, and posterior fundus photography assessments. This practice remained unchanged both in 2015, when data collection for this study was conducted, and as of 2024. Although optical coherence tomography (OCT) and wide-angle fundus photography have become widespread in clinics and hospitals, they are still not commonly introduced in health screening facilities. Therefore, the evaluation in this study was based on posterior fundus photography. The major advantage of conducting research at health screening facilities is the ability to collect large-scale data from a diverse age group. When handling large-scale data, image diagnostics can be prone to error, so we implemented AI-based standardized evaluations. When this study was designed in 2020, only a few software programs for AI-based fundus image evaluation were available for use in Japan. Considering the quality of fundus images at Omiya City Clinic and compatibility with AI software, we selected RetCAD from Throna, a startup at that time. We also believe that introducing multi-dimensional systems, such as OCT and wide-angle fundus photography, could lead to even more interesting results in handling a large number of subjects efficiently, and we look forward to future research in this area. Based on the above, I have added the reasons for selecting RetCAD to the Materials and methods section." Revisions：From line 105 to line 110. Fundus photographs were captured using a non-mydriatic digital fundus camera (CR-2 PlusAF; Canon, Tokyo, Japan), focusing on the macula with a 45° field of view in both eyes. Considering the quality of fundus images at Omiya City Clinic and the compatibility with AI software, we used RetCAD (version 1.3.1; Thirona Bio, Inc., San Diego, CA, USA) for analysis. To ensure anonymity, the images were processed using Mity Safety Exporter before being uploaded to RetCAD, where the DRSs were assessed on a scale of 0–100. We thank you for the time and effort spent throughout the review process of this manuscript. We believe that the excellent comments provided by all did increase the utility of this paper. We hope we were able to revise the paper in accordance with the reviewers’ expectations. We are honored to be working on this paper with you throughout this revision process. Attachments Attachment Submitted filename: DRMASLD ResponsetoReviewer.docx https://doi.org/10.1371/journal.pone.0315752.r002
1 Dec 2024 Decision Letter - Anna Di Sessa, Editor Standardized evaluation of diabetic retinopathy using artificial intelligence and its association with metabolic dysfunction-associated steatotic liver disease in Japan: A cross-sectional study PONE-D-24-35486R1 Dear Dr. komatsu, 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 will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, 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, Anna Di Sessa, PhD, MD Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0315752.r003
Formally Accepted
5 Dec 2024 Acceptance Letter - Anna Di Sessa, Editor PONE-D-24-35486R1 PLOS ONE Dear Dr. Komatsu, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, 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 customercare@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. Anna Di Sessa Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0315752.r004

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