{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T20:30:56Z","timestamp":1774557056583,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T00:00:00Z","timestamp":1717804800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T00:00:00Z","timestamp":1717804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s00371-024-03384-5","type":"journal-article","created":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T03:25:41Z","timestamp":1717817141000},"page":"1061-1077","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["HRDC challenge: a public benchmark for hypertension and hypertensive retinopathy classification from fundus images"],"prefix":"10.1007","volume":"41","author":[{"given":"Bo","family":"Qian","sequence":"first","affiliation":[]},{"given":"Xiangning","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhouyu","family":"Guan","sequence":"additional","affiliation":[]},{"given":"Dawei","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Anran","family":"Ran","sequence":"additional","affiliation":[]},{"given":"Tingyao","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zheyuan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Wen","sequence":"additional","affiliation":[]},{"given":"Xinming","family":"Shu","sequence":"additional","affiliation":[]},{"given":"Jinyang","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Shichang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Guanyu","family":"Xing","sequence":"additional","affiliation":[]},{"given":"Julio","family":"Silva-Rodr\u00edguez","sequence":"additional","affiliation":[]},{"given":"Riadh","family":"Kobbi","sequence":"additional","affiliation":[]},{"given":"Ping","family":"Li","sequence":"additional","affiliation":[]},{"given":"Tingli","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Bi","sequence":"additional","affiliation":[]},{"given":"Jinman","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Weiping","family":"Jia","sequence":"additional","affiliation":[]},{"given":"Huating","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Qin","sequence":"additional","affiliation":[]},{"given":"Ping","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ching-Yu","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Pheng-Ann","family":"Heng","sequence":"additional","affiliation":[]},{"given":"Tien Yin","family":"Wong","sequence":"additional","affiliation":[]},{"given":"Carol Y.","family":"Cheung","sequence":"additional","affiliation":[]},{"given":"Yih-Chung","family":"Tham","sequence":"additional","affiliation":[]},{"given":"Nadia Magnenat","family":"Thalmann","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Sheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,8]]},"reference":[{"key":"3384_CR1","doi-asserted-by":"publisher","first-page":"31595","DOI":"10.1007\/s11042-020-09630-x","volume":"79","author":"Q Abbas","year":"2020","unstructured":"Abbas, Q., Ibrahim, M.E.: Densehyper: an automatic recognition system for detection of hypertensive retinopathy using dense features transform and deep-residual learning. Multimed. Tools Appl. 79, 31595\u201331623 (2020)","journal-title":"Multimed. Tools Appl."},{"issue":"20","key":"3384_CR2","doi-asserted-by":"publisher","first-page":"6936","DOI":"10.3390\/s21206936","volume":"21","author":"Q Abbas","year":"2021","unstructured":"Abbas, Q., Qureshi, I., Ibrahim, M.E.: An automatic detection and classification system of five stages for hypertensive retinopathy using semantic and instance segmentation in densenet architecture. Sensors 21(20), 6936 (2021)","journal-title":"Sensors"},{"key":"3384_CR3","doi-asserted-by":"crossref","unstructured":"Akbar, S., Akram, M.U., Sharif, M., Tariq, A., Khan, S.A.: Decision support system for detection of hypertensive retinopathy using arteriovenous ratio. Artif. Intell. Med. 90, 15\u201324 (2018)","DOI":"10.1016\/j.artmed.2018.06.004"},{"key":"3384_CR4","doi-asserted-by":"crossref","unstructured":"Arsalan, M., Haider, A., Choi, J., Park, K.R.: Diabetic and hypertensive retinopathy screening in fundus images using artificially intelligent shallow architectures. J. Pers. Med. 12(1), 7 (2021)","DOI":"10.3390\/jpm12010007"},{"key":"3384_CR5","doi-asserted-by":"crossref","unstructured":"Badawi, S.A., Fraz, M.M., Shehzad, M., Mahmood, I., Javed, S., Mosalam, E., Nileshwar, A.K.: Detection and grading of hypertensive retinopathy using vessels tortuosity and arteriovenous ratio. J. Digit. Imaging. pp. 1\u201321 (2022)","DOI":"10.1007\/s10278-021-00545-z"},{"key":"3384_CR6","doi-asserted-by":"crossref","unstructured":"Cavallari, M., Stamile, C., Umeton, R., Calimeri, F., Orzi, F., et\u00a0al.: Novel method for automated analysis of retinal images: results in subjects with hypertensive retinopathy and CADASIL. BioMed Res. Int. 2015 (2015)","DOI":"10.1155\/2015\/752957"},{"issue":"1","key":"3384_CR7","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1038\/s41572-022-00342-0","volume":"8","author":"CY Cheung","year":"2022","unstructured":"Cheung, C.Y., Biousse, V., Keane, P.A., Schiffrin, E.L., Wong, T.Y.: Hypertensive eye disease. Nat. Rev. Dis. Primers. 8(1), 14 (2022)","journal-title":"Nat. Rev. Dis. Primers."},{"key":"3384_CR8","volume-title":"High Blood Pressure","author":"B Chhajer","year":"2014","unstructured":"Chhajer, B.: High Blood Pressure. Diamond Pocket Books Pvt Ltd, New Delhi (2014)"},{"key":"3384_CR9","doi-asserted-by":"crossref","unstructured":"Dai, L., Sheng, B., Chen, T., Wu, Q., Liu, R., Cai, C., Wu, L., Yang, D., Hamzah, H., Liu, Y., et\u00a0al.: A deep learning system for predicting time to progression of diabetic retinopathy. Nat. Med. pp. 1\u201311 (2024)","DOI":"10.1038\/s41591-023-02702-z"},{"issue":"1","key":"3384_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-021-23458-5","volume":"12","author":"L Dai","year":"2021","unstructured":"Dai, L., Wu, L., Li, H., Cai, C., Wu, Q., Kong, H., Liu, R., Wang, X., Hou, X., Liu, Y., et al.: A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat. Commun. 12(1), 1\u201311 (2021)","journal-title":"Nat. Commun."},{"key":"3384_CR11","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"10","key":"3384_CR12","doi-asserted-by":"publisher","first-page":"2828","DOI":"10.1109\/TMI.2022.3172773","volume":"41","author":"H Fang","year":"2022","unstructured":"Fang, H., Li, F., Fu, H., Sun, X., Cao, X., Lin, F., Son, J., Kim, S., Quellec, G., Matta, S., et al.: Adam challenge: detecting age-related macular degeneration from fundus images. IEEE Trans. Med. Imaging 41(10), 2828\u20132847 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"9","key":"3384_CR13","doi-asserted-by":"publisher","first-page":"3243","DOI":"10.1007\/s00371-022-02559-2","volume":"38","author":"Y Fu","year":"2022","unstructured":"Fu, Y., Chen, Q., Zhao, H.: Cgfnet: cross-guided fusion network for rgb-thermal semantic segmentation. Vis. Comput. 38(9), 3243\u20133252 (2022)","journal-title":"Vis. Comput."},{"key":"3384_CR14","unstructured":"Ganaie, M.A., Hu, M., et\u00a0al.: Ensemble deep learning: a review. arXiv preprint arXiv:2104.02395 (2021)"},{"key":"3384_CR15","doi-asserted-by":"crossref","unstructured":"Guan, Z., Li, H., Liu, R., Cai, C., Liu, Y., Li, J., Wang, X., Huang, S., Wu, L., Liu, D., et\u00a0al.: Artificial intelligence in diabetes management: advancements, opportunities, and challenges. Cell Rep. Med. (2023)","DOI":"10.1016\/j.xcrm.2023.101213"},{"key":"3384_CR16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"1","key":"3384_CR17","doi-asserted-by":"publisher","first-page":"014503","DOI":"10.1117\/1.JMI.4.1.014503","volume":"4","author":"S Holm","year":"2017","unstructured":"Holm, S., Russell, G., Nourrit, V., McLoughlin, N.: Dr hagis-a fundus image database for the automatic extraction of retinal surface vessels from diabetic patients. J. Med. Imaging 4(1), 014503 (2017)","journal-title":"J. Med. Imaging"},{"key":"3384_CR18","unstructured":"Kanavati, F., Tsuneki, M.: Partial transfusion: on the expressive influence of trainable batch norm parameters for transfer learning. In: Medical Imaging with Deep Learning, pp. 338\u2013353. PMLR (2021)"},{"key":"3384_CR19","first-page":"10","volume":"1","author":"T Kauppi","year":"2007","unstructured":"Kauppi, T., Kalesnykiene, V., Kamarainen, J.K., Lensu, L., Sorri, I., Raninen, A., Voutilainen, R., Uusitalo, H., K\u00e4lvi\u00e4inen, H., Pietil\u00e4, J.: The diaretdb1 diabetic retinopathy database and evaluation protocol. BMVC 1, 10 (2007)","journal-title":"BMVC"},{"key":"3384_CR20","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.media.2018.10.004","volume":"51","author":"M Khened","year":"2019","unstructured":"Khened, M., Kollerathu, V.A., Krishnamurthi, G.: Fully convolutional multi-scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers. Med. Image Anal. 51, 21\u201345 (2019)","journal-title":"Med. Image Anal."},{"key":"3384_CR21","unstructured":"Kumar, A., Raghunathan, A., Jones, R., Ma, T., Liang, P.: Fine-tuning can distort pretrained features and underperform out-of-distribution. arXiv preprint arXiv:2202.10054 (2022)"},{"key":"3384_CR22","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s00371-018-1582-y","volume":"36","author":"X Li","year":"2020","unstructured":"Li, X., Huang, H., Zhao, H., Wang, Y., Hu, M.: Learning a convolutional neural network for propagation-based stereo image segmentation. Vis. Comput. 36, 39\u201352 (2020)","journal-title":"Vis. Comput."},{"key":"3384_CR23","unstructured":"Li, Y., Wang, Z., Yin, L., Zhu, Z., Qi, G., Liu, Y.: X-net: a dual encoding\u2013decoding method in medical image segmentation. Vis. Comput. pp. 1\u201311 (2023)"},{"key":"3384_CR24","doi-asserted-by":"crossref","unstructured":"Liu, R., Wang, X., Wu, Q., Dai, L., Fang, X., Yan, T., Son, J., Tang, S., Li, J., Gao, Z., et\u00a0al.: Deepdrid: diabetic retinopathy-grading and image quality estimation challenge. Patterns p. 100512 (2022)","DOI":"10.1016\/j.patter.2022.100512"},{"key":"3384_CR25","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11,976\u201311,986 (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"3384_CR26","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"3384_CR27","doi-asserted-by":"crossref","unstructured":"Nagpal, D., Panda, S.N., Malarvel, M.: Hypertensive retinopathy screening through fundus images-a review. In: 2021 6th International Conference on Inventive Computation Technologies (ICICT), pp. 924\u2013929. IEEE (2021)","DOI":"10.1109\/ICICT50816.2021.9358746"},{"key":"3384_CR28","unstructured":"Organization, W.H., et\u00a0al.: Hypertension control: report of a WHO Expert Committee. World Health Organization (1996)"},{"key":"3384_CR29","doi-asserted-by":"publisher","first-page":"101570","DOI":"10.1016\/j.media.2019.101570","volume":"59","author":"JI Orlando","year":"2020","unstructured":"Orlando, J.I., Fu, H., Breda, J.B., Van Keer, K., Bathula, D.R., Diaz-Pinto, A., Fang, R., Heng, P.A., Kim, J., Lee, J., et al.: Refuge challenge: a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Med. Image Anal. 59, 101570 (2020)","journal-title":"Med. Image Anal."},{"key":"3384_CR30","unstructured":"Pavao, A., Guyon, I., Letournel, A.C., Bar\u00f3, X., Escalante, H., Escalera, S., Thomas, T., Xu, Z.: Codalab competitions: an open source platform to organize scientific challenges. Ph.D. thesis, Universit\u00e9 Paris-Saclay, FRA (2022)"},{"issue":"3","key":"3384_CR31","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1038\/s41551-018-0195-0","volume":"2","author":"R Poplin","year":"2018","unstructured":"Poplin, R., Varadarajan, A.V., Blumer, K., Liu, Y., McConnell, M.V., Corrado, G.S., Peng, L., Webster, D.R.: Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat. Biomed. Eng. 2(3), 158\u2013164 (2018)","journal-title":"Nat. Biomed. Eng."},{"key":"3384_CR32","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.media.2019.101561","volume":"59","author":"P Porwal","year":"2020","unstructured":"Porwal, P., Pachade, S., Kokare, M., Deshmukh, G., Son, J., Bae, W., Liu, L., Wang, J., Liu, X., Gao, L., et al.: Idrid: diabetic retinopathy-segmentation and grading challenge. Med. Image Anal. 59, 101\u2013561 (2020)","journal-title":"Med. Image Anal."},{"key":"3384_CR33","doi-asserted-by":"crossref","unstructured":"Qian, B., Chen, H., Wang, X., Guan, Z., Li, T., Jin, Y., Wu, Y., Wen, Y., Che, H., Kwon, G., et\u00a0al.: Drac 2022: a public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images. Patterns (2024)","DOI":"10.1016\/j.patter.2024.100929"},{"key":"3384_CR34","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"issue":"17","key":"3384_CR35","doi-asserted-by":"publisher","first-page":"2054","DOI":"10.1016\/j.jacc.2018.07.099","volume":"72","author":"S Rajagopalan","year":"2018","unstructured":"Rajagopalan, S., Al-Kindi, S.G., Brook, R.D.: Air pollution and cardiovascular disease: Jacc state-of-the-art review. J. Am. Coll. Cardiol. 72(17), 2054\u20132070 (2018)","journal-title":"J. Am. Coll. Cardiol."},{"issue":"8","key":"3384_CR36","doi-asserted-by":"publisher","first-page":"1439","DOI":"10.3390\/diagnostics13081439","volume":"13","author":"MZ Sajid","year":"2023","unstructured":"Sajid, M.Z., Qureshi, I., Abbas, Q., Albathan, M., Shaheed, K., Youssef, A., Ferdous, S., Hussain, A.: Mobile-hr: an ophthalmologic-based classification system for diagnosis of hypertensive retinopathy using optimized mobilenet architecture. Diagnostics 13(8), 1439 (2023)","journal-title":"Diagnostics"},{"key":"3384_CR37","doi-asserted-by":"crossref","unstructured":"Shajini, M., Ramanan, A.: A knowledge-sharing semi-supervised approach for fashion clothes classification and attribute prediction. Vis. Comput. 38(11), 3551\u20133561 (2022)","DOI":"10.1007\/s00371-021-02178-3"},{"key":"3384_CR38","unstructured":"Sheng, B., Guan, Z., Lim, L.L., Jiang, Z., Mathioudakis, N., Li, J., Liu, R., Bao, Y., Bee, Y.M., Wang, Y.X., et\u00a0al.: Large language models for diabetes care: potentials and prospects. Sci. Bull. pp. S2095\u20139273 (2024)"},{"key":"3384_CR39","unstructured":"Silva-Rodriguez, J., Chakor, H., Kobbi, R., Dolz, J., Ayed, I.B.: A foundation language-image model of the retina (flair): encoding expert knowledge in text supervision. arXiv preprint arXiv:2308.07898 (2023)"},{"issue":"4","key":"3384_CR40","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/TMI.2004.825627","volume":"23","author":"J Staal","year":"2004","unstructured":"Staal, J., Abr\u00e0moff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501\u2013509 (2004)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3384_CR41","doi-asserted-by":"crossref","unstructured":"Suman, S., Tiwari, A.K., Singh, K.: Computer-aided diagnostic system for hypertensive retinopathy: a review. Comput. Methods Prog. Biomed. p. 107627 (2023)","DOI":"10.1016\/j.cmpb.2023.107627"},{"key":"3384_CR42","doi-asserted-by":"crossref","unstructured":"Tsukikawa, M., Stacey, A.W.: A review of hypertensive retinopathy and chorioretinopathy. Clin. Optomet. pp. 67\u201373 (2020)","DOI":"10.2147\/OPTO.S183492"},{"issue":"1","key":"3384_CR43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-79139-8","volume":"11","author":"M Wiesenfarth","year":"2021","unstructured":"Wiesenfarth, M., Reinke, A., Landman, B.A., Eisenmann, M., Saiz, L.A., Cardoso, M.J., Maier-Hein, L., Kopp-Schneider, A.: Methods and open-source toolkit for analyzing and visualizing challenge results. Sci. Rep. 11(1), 1\u201315 (2021)","journal-title":"Sci. Rep."},{"issue":"22","key":"3384_CR44","doi-asserted-by":"publisher","first-page":"2310","DOI":"10.1056\/NEJMra032865","volume":"351","author":"TY Wong","year":"2004","unstructured":"Wong, T.Y., Mitchell, P.: Hypertensive retinopathy. N. Engl. J. Med. 351(22), 2310\u20132317 (2004)","journal-title":"N. Engl. J. Med."},{"key":"3384_CR45","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.neucom.2020.01.085","volume":"396","author":"X Wu","year":"2020","unstructured":"Wu, X., Sahoo, D., Hoi, S.C.: Recent advances in deep learning for object detection. Neurocomputing 396, 39\u201364 (2020)","journal-title":"Neurocomputing"},{"issue":"3","key":"3384_CR46","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1109\/TMI.2016.2633551","volume":"36","author":"F Xie","year":"2016","unstructured":"Xie, F., Fan, H., Li, Y., Jiang, Z., Meng, R., Bovik, A.: Melanoma classification on dermoscopy images using a neural network ensemble model. IEEE Trans. Med. Imaging 36(3), 849\u2013858 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"3384_CR47","doi-asserted-by":"publisher","first-page":"e0233166","DOI":"10.1371\/journal.pone.0233166","volume":"15","author":"L Zhang","year":"2020","unstructured":"Zhang, L., Yuan, M., An, Z., Zhao, X., Wu, H., Li, H., Wang, Y., Sun, B., Li, H., Ding, S., et al.: Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: a cross-sectional study of chronic diseases in central china. PLoS ONE 15(5), e0233166 (2020)","journal-title":"PLoS ONE"},{"issue":"10304","key":"3384_CR48","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1016\/S0140-6736(21)01330-1","volume":"398","author":"B Zhou","year":"2021","unstructured":"Zhou, B., Carrillo-Larco, R.M., Danaei, G., Riley, L.M., Paciorek, C.J., Stevens, G.A., Gregg, E.W., Bennett, J.E., Solomon, B., Singleton, R.K., et al.: Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. The Lancet 398(10304), 957\u2013980 (2021)","journal-title":"The Lancet"},{"key":"3384_CR49","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.compmedimag.2016.05.004","volume":"55","author":"C Zhu","year":"2017","unstructured":"Zhu, C., Zou, B., Zhao, R., Cui, J., Duan, X., Chen, Z., Liang, Y.: Retinal vessel segmentation in colour fundus images using extreme learning machine. Comput. Med. Imaging Graph. 55, 68\u201377 (2017)","journal-title":"Comput. Med. Imaging Graph."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03384-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-024-03384-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03384-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T12:39:47Z","timestamp":1738586387000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-024-03384-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,8]]},"references-count":49,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["3384"],"URL":"https:\/\/doi.org\/10.1007\/s00371-024-03384-5","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,8]]},"assertion":[{"value":"24 March 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 June 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}