{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T05:48:05Z","timestamp":1784958485322,"version":"3.55.0"},"reference-count":72,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:00:00Z","timestamp":1750291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>The worst outcomes of diabetic retinopathy (DR) can be prevented by implementing DR screening programs assisted by AI. At the University Hospital of Navarre (HUN), Spain, general practitioners (GPs) grade fundus images in an ongoing DR screening program, referring to a second screening level (ophthalmologist) target patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>After collecting their requirements, HUN decided to develop a custom AI tool, called NaIA-RD, to assist their GPs in DR screening. This paper introduces NaIA-RD, details its implementation, and highlights its unique combination of DR and retinal image quality grading in a single system. Its impact is measured in an unprecedented before-and-after study that compares 19,828 patients screened before NaIA-RD\u2019s implementation and 22,962 patients screened after.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>NaIA-RD influenced the screening criteria of 3\/4\u2009GPs, increasing their sensitivity. Agreement between NaIA-RD and the GPs was high for non-referral proposals (94.6% or more), but lower and variable (from 23.4% to 86.6%) for referral proposals. An ophthalmologist discarded a NaIA-RD error in most of contradicted referral proposals by labeling the 93% of a sample of them as referable. In an autonomous setup, NaIA-RD would have reduced the study visualization workload by 4.27 times without missing a single case of sight-threatening DR referred by a GP.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>DR screening was more effective when supported by NaIA-RD, which could be safely used to autonomously perform the first level of screening. This shows how AI devices, when seamlessly integrated into clinical workflows, can help improve clinical pathways in the long term.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fdgth.2025.1547045","type":"journal-article","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:30:15Z","timestamp":1750311015000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Improving diabetic retinopathy screening using artificial intelligence: design, evaluation and before-and-after study of a custom development"],"prefix":"10.3389","volume":"7","author":[{"given":"Imanol","family":"Pinto","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00c1lvaro","family":"Olazar\u00e1n","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Jur\u00edo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Borja","family":"De la Osa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel","family":"Sainz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aritz","family":"Oscoz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jer\u00f3nimo","family":"Ballaz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javier","family":"Gorricho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mikel","family":"Galar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9","family":"Andonegui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,6,19]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"99","DOI":"10.2337\/diacare.26.2007.S99","article-title":"Diabetic retinopathy","volume":"26","author":"Fong","year":"2003","journal-title":"Diabetes Care"},{"key":"B2","doi-asserted-by":"publisher","first-page":"s33","DOI":"10.2337\/diacare.26.2007.s33","article-title":"Standards of medical care for patients with diabetes mellitus","volume":"26","year":"2003","journal-title":"Diabetes Care"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1608","DOI":"10.1016\/j.ophtha.2018.04.007","article-title":"Guidelines on diabetic eye care: the international council of ophthalmology recommendations for screening, follow-up, referral, and treatment based on resource settings","volume":"125","author":"Wong","year":"2018","journal-title":"Ophthalmology"},{"key":"B4","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1136\/bjo.2008.151126","article-title":"Costs and consequences of automated algorithms versus manual grading for the detection of referable diabetic retinopathy","volume":"94","author":"Scotland","year":"2010","journal-title":"Br J Ophthalmol"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3310\/HTA20920","article-title":"An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness","volume":"20","author":"Tufail","year":"2016","journal-title":"Health Technol Assess"},{"key":"B6","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1038\/s41433-019-0566-0","article-title":"Artificial intelligence for diabetic retinopathy screening: a review","volume":"34","author":"Grzybowski","year":"2020","journal-title":"Eye"},{"key":"B7","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1038\/s41591-021-01312-x","article-title":"How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals","volume":"27","author":"Wu","year":"2021","journal-title":"Nat Med"},{"key":"B8","doi-asserted-by":"publisher","first-page":"1168","DOI":"10.2337\/DC20-1877","article-title":"Multicenter, head-to-head, real-world validation study of seven automated artificial intelligence diabetic retinopathy screening systems","volume":"44","author":"Lee","year":"2021","journal-title":"Diabetes Care"},{"key":"B9","doi-asserted-by":"publisher","first-page":"m689","DOI":"10.1136\/bmj.m689","article-title":"Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies","volume":"368","author":"Nagendran","year":"2020","journal-title":"BMJ"},{"key":"B10","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1093\/JAMIA\/OCAD191","article-title":"Early experiences of integrating an artificial intelligence-based diagnostic decision support system into radiology settings: a qualitative study","volume":"31","author":"Fari\u010d","year":"2023","journal-title":"J Am Med Inform Assoc"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3313831.3376718","article-title":"A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy","author":"Beede","year":"2020","journal-title":"Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems; Honolulu, HI, USA"},{"key":"B12","article-title":"Data from: Kaggle competition: diabetic retinopathy detection (2016)","year":""},{"key":"B13","article-title":"Data from: APTOS 2019 blindness detection in Kaggle (2019)","year":""},{"key":"B14","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1016\/J.OPHTHA.2018.01.034","article-title":"Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy","volume":"125","author":"Krause","year":"2018","journal-title":"Ophthalmology"},{"key":"B15","article-title":"Data from: Indian diabetic retinopathy image dataset (IDRiD) (2018)","author":"Porwal","year":""},{"key":"B16","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.ins.2019.06.011","article-title":"Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening","volume":"501","author":"Li","year":"2019","journal-title":"Inf Sci (Ny)"},{"key":"B17","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-32239-7_6","article-title":"Evaluation of retinal image quality assessment networks in different color-spaces","author":"Fu","year":""},{"key":"B18","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1111\/AOS.13613","article-title":"Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the hoorn diabetes care system","volume":"96","author":"van der Heijden","year":"2018","journal-title":"Acta Ophthalmol (Copenh)"},{"key":"B19","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/S41746-019-0099-8","article-title":"Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program","volume":"2","author":"Raumviboonsuk","year":"2019","journal-title":"NPJ Digit Med"},{"key":"B20","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1001\/jamaophthalmol.2019.2004","article-title":"Performance of a deep-learning algorithm vs manual grading for detecting diabetic retinopathy in India","volume":"137","author":"Gulshan","year":"2019","journal-title":"JAMA Ophthalmol"},{"key":"B21","doi-asserted-by":"publisher","first-page":"e2134254","DOI":"10.1001\/JAMANETWORKOPEN.2021.34254","article-title":"Pivotal evaluation of an artificial intelligence system for autonomous detection of referrable and vision-threatening diabetic retinopathy","volume":"4","author":"Ipp","year":"2021","journal-title":"JAMA Network Open"},{"key":"B22","doi-asserted-by":"publisher","first-page":"100228","DOI":"10.1016\/j.xops.2022.100228","article-title":"Artificial intelligence detection of diabetic retinopathy","volume":"3","author":"Lim","year":"2023","journal-title":"Ophthalmol Sci"},{"key":"B23","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1136\/BJOPHTHALMOL-2020-316594","article-title":"Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30000 patients","volume":"105","author":"Heydon","year":"2021","journal-title":"Br J Ophthalmol"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1985","DOI":"10.18240\/IJO.2022.12.14","article-title":"Simultaneous screening and classification of diabetic retinopathy and age-related macular degeneration based on fundus photos\u2013a prospective analysis of the RetCAD system","volume":"15","author":"Skevas","year":"2022","journal-title":"Int J Ophthalmol"},{"key":"B25","doi-asserted-by":"publisher","first-page":"e15055","DOI":"10.1111\/DME.15055","article-title":"Performance of an artificial intelligence automated system for diabetic eye screening in a large English population","volume":"40","author":"Meredith","year":"2023","journal-title":"Diabet Med"},{"key":"B26","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1159\/000368426","article-title":"Screening for diabetic retinopathy in the central region of Portugal. Added value of automated \u201cDisease\/no disease\u201d grading","volume":"233","author":"Ribeiro","year":"2014","journal-title":"Ophthalmologica"},{"key":"B27","doi-asserted-by":"publisher","first-page":"28642","DOI":"10.1109\/ACCESS.2022.3157632","article-title":"Deep learning techniques for diabetic retinopathy classification: a survey","volume":"10","author":"Atwany","year":"2022","journal-title":"IEEE Access"},{"key":"B28","doi-asserted-by":"publisher","first-page":"16173","DOI":"10.1007\/s11042-019-07751-6","article-title":"Retinal image quality assessment for diabetic retinopathy screening: a survey","volume":"79","author":"Lin","year":"2020","journal-title":"Multimed Tools Appl"},{"key":"B29","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1258\/jtt.2010.091204","article-title":"Diabetic retinopathy screening using tele-ophthalmology in a primary care setting","volume":"16","author":"Andonegui","year":"2010","journal-title":"J Telemed Telecare"},{"key":"B30","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.pcd.2012.01.001","article-title":"Diabetic retinopathy screening with non-mydriatic retinography by general practitioners: 2-year results","volume":"6","author":"Andonegui","year":"2012","journal-title":"Prim Care Diabetes"},{"key":"B31","doi-asserted-by":"publisher","first-page":"1677","DOI":"10.1016\/S0161-6420(03)00475-5","article-title":"Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales","volume":"110","author":"Wilkinson","year":"2003","journal-title":"Ophthalmology"},{"key":"B32","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.2147\/OPTH.S261629","article-title":"The evolution of diabetic retinopathy screening programmes: a chronology of retinal photography from 35\u2009mm slides to artificial intelligence","volume":"14","author":"Huemer","year":"2020","journal-title":"Clin Ophthalmol"},{"key":"B33","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1177\/193229680800200106","article-title":"Technology for diabetes care and evaluation in the veterans health administration: Teleretinal imaging to screen for diabetic retinopathy in the veterans health administration","volume":"2","author":"Cavallerano","year":"2008","journal-title":"J Diabetes Sci Technol"},{"key":"B34","first-page":"s22","article-title":"The Scottish diabetic retinopathy screening programme","volume":"28","author":"Zachariah","year":"2015","journal-title":"Community Eye Health"},{"key":"B35","doi-asserted-by":"publisher","first-page":"2571","DOI":"10.1016\/J.OPHTHA.2016.08.021","article-title":"Cost-effectiveness of a national telemedicine diabetic retinopathy screening program in Singapore","volume":"123","author":"Nguyen","year":"2016","journal-title":"Ophthalmology"},{"key":"B36","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1007\/S00592-017-0974-1","article-title":"The english national screening programme for diabetic retinopathy 2003\u20132016","volume":"54","author":"Scanlon","year":"2017","journal-title":"Acta Diabetol"},{"key":"B37","doi-asserted-by":"publisher","first-page":"756","DOI":"10.1186\/S12913-021-06776-8","article-title":"Five regions, five retinopathy screening programmes: a systematic review of how Portugal addresses the challenge","volume":"21","author":"Pereira","year":"2021","journal-title":"BMC Health Serv Res"},{"key":"B38","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1002\/hcs2.10","article-title":"Two Singapore public healthcare AI applications for national screening programs and other examples","volume":"1","author":"Ta","year":"2022","journal-title":"Health Care Sci"},{"key":"B39","article-title":"Tracing the twenty-year evolution of developing AI for eye screening in Singapore: a master chronology of SiDRP, SELENA+ and EyRis","author":"Miller","year":"2023","journal-title":"Res Collect Sch Comput Inf Syst"},{"key":"B40","doi-asserted-by":"publisher","first-page":"2211","DOI":"10.1001\/JAMA.2017.18152","article-title":"Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes","volume":"318","author":"Ting","year":"2017","journal-title":"JAMA"},{"key":"B41","doi-asserted-by":"publisher","first-page":"107441","DOI":"10.1016\/J.JDIACOMP.2019.107441","article-title":"Comparison of 1-field, 2-fields, and 3-fields fundus photography for detection and grading of diabetic retinopathy","volume":"33","author":"Lee","year":"2019","journal-title":"J Diabetes Complicat"},{"key":"B42","article-title":"Grading diabetic retinopathy from stereoscopic color fundus photographs\u2014an extension of the modified airlie house classification: ETDRS report number 10","year":""},{"key":"B43","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/CVPR.2016.90","article-title":"Deep residual learning for image recognition","author":"He","year":"2016","journal-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Las Vegas, NV, USA"},{"key":"B44","doi-asserted-by":"publisher","first-page":"108","DOI":"10.3390\/info11020108","article-title":"Layered API for deep learning","volume":"11","author":"Howard","year":"2020","journal-title":"Information"},{"key":"B45","volume-title":"Deep Learning for Coders with Fastai and PyTorch","author":"Howard","year":"2020"},{"key":"B46","doi-asserted-by":"publisher","first-page":"874","DOI":"10.1016\/J.ENGAPPAI.2007.09.009","article-title":"About the relationship between roc curves and cohen\u2019s kappa","volume":"21","author":"Ben-David","year":"2008","journal-title":"Eng Appl Artif Intell"},{"key":"B47","article-title":"Data from: Michael D. Abramoff\u2019s web page, where messidor-2 dataset can be downloaded (2023)","year":""},{"key":"B48","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1177\/193229680900300315","article-title":"EyePACS: An adaptable telemedicine system for diabetic retinopathy screening","volume":"3","author":"Cuadros","year":"2009","journal-title":"J Diabetes Sci Technol"},{"key":"B49","article-title":"Data from: Full EyePACS dataset in kaggle (2023)","year":""},{"key":"B50","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1111\/J.1553-2712.1996.TB03538.X","article-title":"Statistical methodology: incorporating the prevalence of disease into the sample size calculation for sensitivity and specificity","volume":"3","author":"Buderer","year":"1996","journal-title":"Acad Emerg Med"},{"key":"B51","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.jbi.2014.02.013","article-title":"Sample size estimation in diagnostic test studies of biomedical informatics","volume":"48","author":"Hajian-Tilaki","year":"2014","journal-title":"J Biomed Inform"},{"key":"B52","doi-asserted-by":"publisher","first-page":"3211","DOI":"10.48550\/arxiv.2112.10327","article-title":"Classifier calibration: how to assess and improve predicted class probabilities: a survey","volume":"112","author":"Filho","year":"2023","journal-title":"Mach Learn"},{"key":"B53","article-title":"Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers","author":"Kull","year":""},{"key":"B54","doi-asserted-by":"crossref","DOI":"10.1145\/775047.775151","article-title":"Transforming classifier scores into accurate multiclass probability estimates","author":"Zadrozny","year":""},{"key":"B55","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3390\/E23010018","article-title":"Explainable AI: a review of machine learning interpretability methods","volume":"23","author":"Linardatos","year":"2021","journal-title":"Entropy"},{"key":"B56","article-title":"Axiomatic attribution for deep networks\u2013integrated gradients","author":"Sundararajan","year":""},{"key":"B57","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1016\/j.ophtha.2018.11.016","article-title":"Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy","volume":"126","author":"Sayres","year":"2019","journal-title":"Ophthalmology"},{"key":"B58","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1145\/304181.304187","article-title":"OPTICS: ordering points to identify the clustering structure","volume":"28","author":"Ankerst","year":"1999","journal-title":"ACM SIGMOD Rec"},{"key":"B59","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/S0734-189X(87)80186-X","article-title":"Adaptive histogram equalization and its variations","volume":"39","author":"Pizer","year":"1987","journal-title":"Comput Vis Graph Image Process"},{"key":"B60","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/B978-0-12-336156-1.50061-6","article-title":"VIII.5. \u2013 Contrast limited adaptive histogram equalization","volume-title":"Graphics Gems","author":"Zuiderveld","year":"1994"},{"key":"B61","volume-title":"Literate Programming (Center for the Study of Language and Information Publication Lecture Notes)","author":"Knuth","year":"1992"},{"key":"B62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-018-0040-6","article-title":"Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices","volume":"1","author":"Abr\u00e0moff","year":"2018","journal-title":"npj Digit Med"},{"key":"B63","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1177\/1932296820906212","article-title":"Validation of automated screening for referable diabetic retinopathy with an autonomous diagnostic artificial intelligence system in a Spanish population","volume":"15","author":"Shah","year":"2020","journal-title":"J Diabetes Sci Technol"},{"key":"B64","article-title":"Instant automatic diagnosis of diabetic retinopathy (2019)","author":"Quellec","year":""},{"key":"B65","doi-asserted-by":"publisher","first-page":"276","DOI":"10.11613\/bm.2012.031","article-title":"Interrater reliability: the kappa statistic","volume":"22","author":"McHugh","year":"2012","journal-title":"Biochem Med (Zagreb)"},{"key":"B66","doi-asserted-by":"crossref","DOI":"10.4135\/9781412983532","volume-title":"Bootstrapping: A Nonparametric Approach to Statistical Inference","author":"Mooney","year":"1993"},{"key":"B67","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-93632-8","article-title":"An interpretable multiple-instance approach for the detection of referable diabetic retinopathy in fundus images","volume":"11","author":"Papadopoulos","year":"2021","journal-title":"Sci Rep"},{"key":"B68","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"B69","doi-asserted-by":"publisher","first-page":"e0217541","DOI":"10.1371\/JOURNAL.PONE.0217541","article-title":"Reproduction study using public data of: development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"14","author":"Voets","year":"2019","journal-title":"PLoS One"},{"key":"B70","doi-asserted-by":"publisher","first-page":"100512","DOI":"10.1016\/J.PATTER.2022.100512","article-title":"Deepdrid: Diabetic retinopathy\u2013grading and image quality estimation challenge","volume":"3","author":"Liu","year":"2022","journal-title":"Patterns"},{"key":"B71","doi-asserted-by":"publisher","first-page":"046006","DOI":"10.1117\/1.JBO.19.4.046006","article-title":"Identification of suitable fundus images using automated quality assessment methods","volume":"19","author":"Sevik","year":"2014","journal-title":"J Biomed Opt"},{"key":"B72","doi-asserted-by":"publisher","first-page":"e195","DOI":"10.1016\/S2589-7500(20)30292-2","article-title":"Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015\u201320): a comparative analysis","volume":"3","author":"Muehlematter","year":"2021","journal-title":"Lancet Digit Health"}],"container-title":["Frontiers in Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2025.1547045\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:30:18Z","timestamp":1750311018000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2025.1547045\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,19]]},"references-count":72,"alternative-id":["10.3389\/fdgth.2025.1547045"],"URL":"https:\/\/doi.org\/10.3389\/fdgth.2025.1547045","relation":{},"ISSN":["2673-253X"],"issn-type":[{"value":"2673-253X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,19]]},"article-number":"1547045"}}