{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T00:03:52Z","timestamp":1794269032435,"version":"4.9.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T00:00:00Z","timestamp":1743724800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T00:00:00Z","timestamp":1743724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Head Face Med"],"DOI":"10.1186\/s13005-025-00496-8","type":"journal-article","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T01:38:37Z","timestamp":1743817117000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis"],"prefix":"10.1186","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5885-7694","authenticated-orcid":false,"given":"Alexander Maniangat","family":"Luke","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4208-4823","authenticated-orcid":false,"given":"Nader Nabil Fouad","family":"Rezallah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,4]]},"reference":[{"issue":"12","key":"496_CR1","doi-asserted-by":"crossref","first-page":"6871","DOI":"10.1007\/s00784-022-04724-2","volume":"26","author":"R Patcas","year":"2022","unstructured":"Patcas R, Bornstein MM, Sch\u00e4tzle MA, Timofte R. Artificial intelligence in medico-dental diagnostics of the face: a narrative review of opportunities and challenges. Clin Oral Invest. 2022;26(12):6871\u20139.","journal-title":"Clin Oral Invest"},{"key":"496_CR2","doi-asserted-by":"publisher","unstructured":"Agrawal P, Nikhade P, Nikhade PP. Artificial intelligence in dentistry: past, present, and future. Cureus. 2022;14(7):e27405. https:\/\/doi.org\/10.7759\/cureus.27405.","DOI":"10.7759\/cureus.27405"},{"key":"496_CR3","doi-asserted-by":"crossref","first-page":"S94","DOI":"10.1111\/jcpe.12677","volume":"44","author":"JE Frencken","year":"2017","unstructured":"Frencken JE, Sharma P, Stenhouse L, Green D, Laverty D, Dietrich T. Global epidemiology of dental caries and severe periodontitis\u2013a comprehensive review. J Clin Periodontol. 2017;44:S94\u2013105.","journal-title":"J Clin Periodontol"},{"issue":"1","key":"496_CR4","first-page":"29","volume":"5","author":"M Kumari","year":"2022","unstructured":"Kumari M, Rafia AK, Shree R. Changing concepts in the diagnosis of dental caries: a review. Sci Arch Dent Sci. 2022;5(1):29\u201335.","journal-title":"Sci Arch Dent Sci"},{"issue":"5","key":"496_CR5","doi-asserted-by":"crossref","first-page":"20210010","DOI":"10.1259\/dmfr.20210010","volume":"50","author":"A Wenzel","year":"2021","unstructured":"Wenzel A. Radiographic modalities for diagnosis of caries in a historical perspective: from film to machine-intelligence supported systems. Dentomaxillofacial Radiology. 2021;50(5): 20210010.","journal-title":"Dentomaxillofacial Radiology"},{"issue":"3","key":"496_CR6","first-page":"427","volume":"65","author":"AF Dayo","year":"2021","unstructured":"Dayo AF, Wolff MS, Syed AZ, Mupparapu M. Radiology of dental caries. Dental Clinics. 2021;65(3):427\u201345.","journal-title":"Dental Clinics"},{"issue":"5","key":"496_CR7","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.3390\/diagnostics12051029","volume":"12","author":"S Patil","year":"2022","unstructured":"Patil S, Albogami S, Hosmani J, Mujoo S, Kamil MA, Mansour MA, et al. Artificial intelligence in the diagnosis of oral diseases: applications and pitfalls. Diagnostics. 2022;12(5):1029.","journal-title":"Diagnostics"},{"key":"496_CR8","doi-asserted-by":"crossref","first-page":"104115","DOI":"10.1016\/j.jdent.2022.104115","volume":"122","author":"H Mohammad-Rahimi","year":"2022","unstructured":"Mohammad-Rahimi H, Motamedian SR, Rohban MH, Krois J, Uribe SE, Mahmoudinia E, Schwendicke F. Deep learning for caries detection: a systematic review. J Dent. 2022;122:104115.","journal-title":"J Dent."},{"key":"496_CR9","doi-asserted-by":"crossref","first-page":"103260","DOI":"10.1016\/j.jdent.2019.103260","volume":"92","author":"F Schwendicke","year":"2020","unstructured":"Schwendicke F, Elhennawy K, Paris S, Friebertsh\u00e4user P, Krois J. Deep learning for caries lesion detection in near-infrared light transillumination images: a pilot study. J Dent. 2020;92:103260.","journal-title":"J Dent."},{"issue":"3","key":"496_CR10","doi-asserted-by":"crossref","first-page":"510","DOI":"10.3390\/iot2030026","volume":"2","author":"H Hassani","year":"2021","unstructured":"Hassani H, Amiri Andi P, Ghodsi A, Norouzi K, Komendantova N, Unger S. Shaping the future of smart dentistry: from Artificial Intelligence (AI) to Intelligence Augmentation (IA). IoT. 2021;2(3):510\u201323.","journal-title":"IoT"},{"key":"496_CR11","unstructured":"Slosiarova N, Mesarcik M, Jurkacek P, Podrouzek J. Trustworthy AI in dental care beyond Artificial Intelligence Act. Proc. of the 2nds International Workshop on Imagining the AI Landscape After the AI Act \u2013 HHAI 23, Munich, Germany, June 26-27, 2023, CEUR-WS.org, online https:\/\/ceur-ws.org\/Vol-3456\/short1-2.pdf."},{"key":"496_CR12","doi-asserted-by":"publisher","unstructured":"Anil S, Sudeep K, Saratchandran S, Sweety VK. Revolutionizing dental caries diagnosis through artificial intelligence. 2023. https:\/\/doi.org\/10.5772\/intechopen.112979.","DOI":"10.5772\/intechopen.112979"},{"key":"496_CR13","doi-asserted-by":"publisher","unstructured":"Ing ME. Dental caries. In: Dental science for the medical professional: an evidence-based approach. Cham: Springer International Publishing. 2023. pp. 69\u201387. https:\/\/doi.org\/10.1007\/978-3-031-38567-4_7.","DOI":"10.1007\/978-3-031-38567-4_7"},{"issue":"11","key":"496_CR14","doi-asserted-by":"crossref","first-page":"2188","DOI":"10.3390\/healthcare10112188","volume":"10","author":"A Fatima","year":"2022","unstructured":"Fatima A, Shafi I, Afzal H, D\u00edez IDLT, Lourdes DRSM, Bre\u00f1osa J, Ashraf I. Advancements in dentistry with artificial intelligence: current clinical applications and future perspectives. Healthcare. 2022;10(11): 2188.","journal-title":"Healthcare"},{"issue":"7","key":"496_CR15","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.3390\/diagnostics13071353","volume":"13","author":"S Dixit","year":"2023","unstructured":"Dixit S, Kumar A, Srinivasan K. A current review of machine learning and deep learning models in oral cancer diagnosis: recent technologies, open challenges, and future research directions. Diagnostics. 2023;13(7):1353.","journal-title":"Diagnostics"},{"key":"496_CR16","doi-asserted-by":"crossref","first-page":"(1)","DOI":"10.1186\/s12903-023-03624-5","volume":"24","author":"MI Karobari","year":"2024","unstructured":"Karobari MI, Batul R, Khan M, et al. Micro computed tomography (Micro-CT) characterization of root and root canal morphology of mandibular first premolars: a systematic review and meta-analysis. BMC Oral Health. 2024;24:(1).","journal-title":"BMC Oral Health"},{"issue":"8","key":"496_CR17","doi-asserted-by":"crossref","first-page":"911","DOI":"10.3390\/bioengineering10080911","volume":"10","author":"IDS Chen","year":"2023","unstructured":"Chen IDS, Yang CM, Chen MJ, Chen MC, Weng RM, Yeh CH. Deep learning-based recognition of periodontitis and dental caries in dental x-ray images. Bioengineering. 2023;10(8): 911.","journal-title":"Bioengineering"},{"key":"496_CR18","doi-asserted-by":"crossref","unstructured":"Zhu H, Cao Z, Lian L, Ye G, Gao H, Wu J. CariesNet: a deep learning approach for segmentation of multi-stage caries lesion from oral panoramic X-ray image. Neural Comput & Applic. 2023;35;16051\u20139.","DOI":"10.1007\/s00521-021-06684-2"},{"issue":"1","key":"496_CR19","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1007\/s00784-021-04040-1","volume":"26","author":"Y Bayraktar","year":"2022","unstructured":"Bayraktar Y, Ayan E. Diagnosis of interproximal caries lesions with deep convolutional neural network in digital bitewing radiographs. Clin Oral Invest. 2022;26(1):623\u201332.","journal-title":"Clin Oral Invest"},{"key":"496_CR20","doi-asserted-by":"publisher","unstructured":"Huang Y, Lee S. Deep Learning for Caries Detection using Optical Coherence Tomography. medRxiv. 2021. Preprint. https:\/\/doi.org\/10.1101\/2021.05.04.21256502.","DOI":"10.1101\/2021.05.04.21256502"},{"key":"496_CR21","doi-asserted-by":"crossref","first-page":"4613","DOI":"10.3390\/s21134613","volume":"21","author":"Y-C Mao","year":"2021","unstructured":"Mao Y-C, Chen T-Y, Chou H-S, Lin S-Y, Liu S-Y, Chen Y-A, Liu Y-L, Chen C-A, Huang Y-C, Chen S-L, et al. Caries and restoration detection using bitewing film based on transfer learning with CNNs. Sensors. 2021;21:4613.","journal-title":"Sensors"},{"key":"496_CR22","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1007\/s10278-021-00487-6","volume":"34","author":"V De Araujo Faria","year":"2021","unstructured":"De Araujo Faria V, Azimbagirad M, Viani Arruda G, Fernandes Pavoni J, Cezar Felipe J, Dos Santos EMCMF, Murta Junior LO. Prediction of radiation-related dental caries through PyRadiomics features and artificial neural network on panoramic radiography. J Digit Imaging. 2021;34:1237\u201348.","journal-title":"J. Digit. Imaging."},{"key":"496_CR23","doi-asserted-by":"crossref","first-page":"15447","DOI":"10.1038\/s41598-021-95024-4","volume":"11","author":"S-H Hur","year":"2021","unstructured":"Hur S-H, Lee E-Y, Kim M-K, Kim S, Kang J-Y, Lim JS. Machine learning to predict distal caries in mandibular second molars associated with impacted third molars. Sci Rep. 2021;11:15447.","journal-title":"Sci Rep"},{"key":"496_CR24","doi-asserted-by":"crossref","first-page":"16807","DOI":"10.1038\/s41598-021-96368-7","volume":"11","author":"S Lee","year":"2021","unstructured":"Lee S, Oh S-I, Jo J, Kang S, Shin Y, Park J-W. Deep learning for early dental caries detection in bitewing radiographs. Sci Rep. 2021;11:16807.","journal-title":"Sci Rep"},{"key":"496_CR25","doi-asserted-by":"crossref","first-page":"12609","DOI":"10.1038\/s41598-021-92121-2","volume":"11","author":"S Vinayahalingam","year":"2021","unstructured":"Vinayahalingam S, Kempers S, Limon L, Deibel D, Maal T, Hanisch M, Berg\u00e9 S, Xi T. Classification of caries in third molars on panoramic radiographs using deep learning. Sci Rep. 2021;11:12609.","journal-title":"Sci Rep"},{"key":"496_CR26","doi-asserted-by":"crossref","first-page":"103849","DOI":"10.1016\/j.jdent.2021.103849","volume":"115","author":"S Mertens","year":"2021","unstructured":"Mertens S, Krois J, Cantu AG, Arsiwala LT, Schwendicke F. Artificial intelligence for caries detection: randomized trial. J Dent. 2021;115: 103849.","journal-title":"J Dent"},{"key":"496_CR27","doi-asserted-by":"crossref","first-page":"5192","DOI":"10.3390\/s21155192","volume":"21","author":"M Moran","year":"2021","unstructured":"Moran M, Faria M, Giraldi G, Bastos L, Oliveira L, Conci A. Classification of approximal caries in bitewing radiographs using convolutional neural networks. Sensors. 2021;21: 5192.","journal-title":"Sensors"},{"key":"496_CR28","doi-asserted-by":"crossref","first-page":"1672","DOI":"10.3390\/diagnostics11091672","volume":"11","author":"L Lian","year":"2021","unstructured":"Lian L, Zhu T, Zhu F, Zhu H. Deep learning for caries detection and classification. Diagnostics. 2021;11: 1672.","journal-title":"Diagnostics"},{"key":"496_CR29","doi-asserted-by":"crossref","first-page":"763","DOI":"10.21037\/atm-21-119","volume":"9","author":"L Zheng","year":"2021","unstructured":"Zheng L, Wang H, Mei L, Chen Q, Zhang Y, Zhang H. Artificial intelligence in digital cariology: a new tool for the diagnosis of deep caries and pulpitis using convolutional neural networks. Ann Transl Med. 2021;9:763.","journal-title":"Ann Transl Med"},{"key":"496_CR30","first-page":"1","volume":"1","author":"IS Bayrakdar","year":"2021","unstructured":"Bayrakdar IS, Orhan K, Akarsu S, \u00c7elik \u00d6, Atasoy S, Pekince A, Odaba\u015f A. Deep-learning approach for caries detection and segmentation on dental bitewing radiographs. Oral Radiol. 2021;1:1\u201312.","journal-title":"Oral Radiol"},{"key":"496_CR31","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1038\/s41415-021-3526-6","volume":"231","author":"H Devlin","year":"2021","unstructured":"Devlin H, Williams T, Graham J, Ashley M. The ADEPT Study: a comparative study of dentists\u2019 ability to detect enamel-only proximal caries in bitewing radiographs with and without the use of assist dent artificial intelligence software. Br Dent J. 2021;231:481\u20135.","journal-title":"Br Dent J"},{"key":"496_CR32","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1007\/s11548-021-02319-y","volume":"16","author":"H Chen","year":"2021","unstructured":"Chen H, Li H, Zhao Y, Zhao J, Wang Y. Dental disease detection on periapical radiographs based on deep convolutional neural networks. Int J Comput Assist Radiol Surg. 2021;16:649\u201361.","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"496_CR33","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1007\/s13755-019-0096-y","volume":"8","author":"V Geetha","year":"2020","unstructured":"Geetha V, Aprameya KS, Hinduja DM. Dental caries diagnosis in digital radiographs using back-propagation neural network. Health Inf Sci Syst. 2020;8:8.","journal-title":"Health Inf Sci Syst"},{"key":"496_CR34","doi-asserted-by":"crossref","first-page":"103425","DOI":"10.1016\/j.jdent.2020.103425","volume":"100","author":"AG Cantu","year":"2020","unstructured":"Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi JG, Gaudin R, Elhennawy K, Schwendicke F. Detecting caries lesions of different radiographic extension on bitewings using deep learning. J Dent. 2020;100: 103425.","journal-title":"J Dent"},{"key":"496_CR35","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s11265-016-1214-6","volume":"90","author":"J Choi","year":"2018","unstructured":"Choi J, Eun H, Kim C. Boosting proximal dental caries detection via combination of variational methods and convolutional neural network. J Signal Process Syst. 2018;90:87\u201397.","journal-title":"J Signal Process Syst"},{"key":"496_CR36","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.jdent.2018.07.015","volume":"77","author":"JH Lee","year":"2018","unstructured":"Lee JH, Kim D-H, Jeong S-N, Choi S-H. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent. 2018;77:106\u201311.","journal-title":"J Dent"},{"key":"496_CR37","unstructured":"Srivastava MM, Kumar P, Pradhan L, Varadarajan S. Detection of tooth caries in bitewing radiographs using deep learning. arXiv preprint arXiv:1711.07312. 2017."},{"key":"496_CR38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-021-00444-8","volume":"8","author":"L Alzubaidi","year":"2021","unstructured":"Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data. 2021;8:1\u201374.","journal-title":"J Big Data"},{"issue":"07","key":"496_CR39","first-page":"271","volume":"2","author":"MN Akhter","year":"2023","unstructured":"Akhter MN, Hussain SS, Riaz N, Zulfiqar R. Using technological diagnostic tools to find early caries: a systematic review. Dinkum Journal of Medical Innovations. 2023;2(07):271\u201383.","journal-title":"Dinkum Journal of Medical Innovations"},{"key":"496_CR40","doi-asserted-by":"crossref","first-page":"e2371","DOI":"10.7717\/peerj-cs.2371","volume":"10","author":"M Dashti","year":"2024","unstructured":"Dashti M, Londono J, Ghasemi S, Zare N, Samman M, Ashi H, Amirzade-Iranaq MH, Khosraviani F, Sabeti M, Khurshid Z. Comparative analysis of deep learning algorithms for dental caries detection and prediction from radiographic images: a comprehensive umbrella review. PeerJ Computer Science. 2024;10: e2371.","journal-title":"PeerJ Computer Science"},{"issue":"1","key":"496_CR41","doi-asserted-by":"crossref","first-page":"100144","DOI":"10.1016\/j.jjimei.2022.100144","volume":"3","author":"SS Mahdi","year":"2023","unstructured":"Mahdi SS, Battineni G, Khawaja M, Allana R, Siddiqui MK, Agha D. How does artificial intelligence impact digital healthcare initiatives? A review of AI applications in dental healthcare. International Journal of Information Management Data Insights. 2023;3(1): 100144.","journal-title":"International Journal of Information Management Data Insights"},{"issue":"4","key":"496_CR42","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1177\/0022034520972335","volume":"100","author":"F Schwendicke","year":"2021","unstructured":"Schwendicke F, Rossi JG, G\u00f6stemeyer G, Elhennawy K, Cantu AG, Gaudin R, Chaurasia A, Gehrung S, Krois J. Cost-effectiveness of artificial intelligence for proximal caries detection. J Dent Res. 2021;100(4):369\u201376. https:\/\/doi.org\/10.1177\/0022034520972335.","journal-title":"J Dent Res"},{"issue":"3","key":"496_CR43","doi-asserted-by":"crossref","first-page":"315","DOI":"10.2334\/josnusd.16-0595","volume":"59","author":"N Abogazalah","year":"2017","unstructured":"Abogazalah N, Ando M. Alternative methods to visual and radiographic examinations for approximal caries detection. J Ora Sci. 2017;59(3):315\u201322.","journal-title":"J Ora Sci"},{"issue":"1","key":"496_CR44","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.jds.2020.06.019","volume":"16","author":"SB Khanagar","year":"2021","unstructured":"Khanagar SB, Al-Ehaideb A, Maganur PC, Vishwanathaiah S, Patil S, Baeshen HA, Bhandi S. Developments, application, and performance of artificial intelligence in dentistry\u2013A systematic review. Journal of dental sciences. 2021;16(1):508\u201322.","journal-title":"Journal of dental sciences"},{"key":"496_CR45","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.jdsr.2024.02.001","volume":"60","author":"N Ammar","year":"2024","unstructured":"Ammar N, K\u00fchnisch J. Diagnostic performance of artificial intelligence-aided caries detection on bitewing radiographs: a systematic review and meta-analysis. Jpn Dent Sci Rev. 2024;60:128\u201336. https:\/\/doi.org\/10.1016\/j.jdsr.2024.02.001.","journal-title":"Jpn Dent Sci Rev"},{"issue":"8","key":"496_CR46","doi-asserted-by":"crossref","first-page":"961","DOI":"10.3390\/healthcare9080961","volume":"9","author":"M Klumpp","year":"2021","unstructured":"Klumpp M, Hintze M, Immonen M, R\u00f3denas-Rigla F, Pilati F, Aparicio-Mart\u00ednez F, Delgado-Gonzalo R. Artificial intelligence for hospital health care: application cases and answers to challenges in European hospitals. In Healthcare. 2021;9(8):961.","journal-title":"In Healthcare"},{"issue":"17","key":"496_CR47","doi-asserted-by":"crossref","first-page":"2760","DOI":"10.3390\/diagnostics13172760","volume":"13","author":"R Najjar","year":"2023","unstructured":"Najjar R. Redefining radiology: a review of artificial intelligence integration in medical imaging. Diagnostics. 2023;13(17): 2760.","journal-title":"Diagnostics"},{"issue":"24","key":"496_CR48","doi-asserted-by":"publisher","first-page":"00591","DOI":"10.1016\/j.adaj.2024.10.007","volume":"S0002\u20138177","author":"GRM La Rosa","year":"2024","unstructured":"La Rosa GRM. Artificial intelligence in demineralized lesion detection: evaluating clinical benefits and economic disadvantages of artificial intelligence-based models. J Am Dent Assoc. 2024;S0002\u20138177(24):00591\u20139. https:\/\/doi.org\/10.1016\/j.adaj.2024.10.007.","journal-title":"J Am Dent Assoc"}],"container-title":["Head & Face Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13005-025-00496-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13005-025-00496-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13005-025-00496-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T16:48:28Z","timestamp":1744217308000},"score":1,"resource":{"primary":{"URL":"https:\/\/head-face-med.biomedcentral.com\/articles\/10.1186\/s13005-025-00496-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,4]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["496"],"URL":"https:\/\/doi.org\/10.1186\/s13005-025-00496-8","relation":{},"ISSN":["1746-160X"],"issn-type":[{"value":"1746-160X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,4]]},"assertion":[{"value":"26 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate:"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publications"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"24"}}