{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T22:09:53Z","timestamp":1782166193125,"version":"3.54.5"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T00:00:00Z","timestamp":1732060800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T00:00:00Z","timestamp":1732060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100014761","name":"Qingdao Natural Science Foundation","doi-asserted-by":"crossref","award":["24-4-4-zrjj-90-jch"],"award-info":[{"award-number":["24-4-4-zrjj-90-jch"]}],"id":[{"id":"10.13039\/501100014761","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s00530-024-01538-y","type":"journal-article","created":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T06:59:14Z","timestamp":1732085954000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Scd-yolo: a novel object detection method for efficient road crack detection"],"prefix":"10.1007","volume":"30","author":[{"given":"Kuiye","family":"Ding","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhui","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zengbin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mao","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangxiao","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guohua","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,20]]},"reference":[{"issue":"6","key":"1538_CR1","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1016\/j.eng.2020.07.030","volume":"7","author":"Y Hou","year":"2021","unstructured":"Hou, Y., Li, Q., Zhang, C., Lu, G., Ye, Z., Chen, Y., Wang, L., Cao, D.: The State-of-the-Art review on applications of intrusive sensing, image processing techniques, and machine learning methods in pavement monitoring and analysis. Engineering 7(6), 845\u2013856 (2021)","journal-title":"Engineering"},{"key":"1538_CR2","unstructured":"Zaloshnja, E., Miller, T.R.: Cost of crashes related to road conditions, united states, 2006. In: Annals of Advances in Automotive Medicine\/Annual Scientific Conference, vol. 53, pp. 141\u2013153 (2009). Association for the Advancement of Automotive Medicine"},{"key":"1538_CR3","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.imavis.2016.11.018","volume":"57","author":"D Zhang","year":"2017","unstructured":"Zhang, D., Li, Q., Chen, Y., Cao, M., He, L., Zhang, B.: An efficient and reliable coarse-to-fine approach for asphalt pavement crack detection. Image Vis. Comput. 57, 130\u2013146 (2017)","journal-title":"Image Vis. Comput."},{"key":"1538_CR4","doi-asserted-by":"crossref","unstructured":"Sari, Y., Prakoso, P.B., Baskara, A.R.: Road crack detection using support vector machine (SVM) and OTSU Algorithm. In: 2019 6th International Conference on Electric Vehicular Technology (ICEVT), pp. 349\u2013354. IEEE, Bali, Indonesia (2019)","DOI":"10.1109\/ICEVT48285.2019.8993969"},{"issue":"1","key":"1538_CR5","doi-asserted-by":"publisher","DOI":"10.1117\/1.OE.55.1.011008","volume":"55","author":"Y Bao","year":"2015","unstructured":"Bao, Y., Chen, G.: Strain distribution and crack detection in thin unbonded concrete pavement overlays with fully distributed fiber optic sensors. Opt. Eng. 55(1), 011008 (2015)","journal-title":"Opt. Eng."},{"key":"1538_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2021.126162","volume":"321","author":"N Kheradmandi","year":"2022","unstructured":"Kheradmandi, N., Mehranfar, V.: A critical review and comparative study on image segmentation-based techniques for pavement crack detection. Constr. Build. Mater. 321, 126162 (2022)","journal-title":"Constr. Build. Mater."},{"issue":"6245","key":"1538_CR7","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1126\/science.aaa8415","volume":"349","author":"MI Jordan","year":"2015","unstructured":"Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255\u2013260 (2015)","journal-title":"Science"},{"key":"1538_CR8","unstructured":"Ahmadi, a., Khalesi, S., Bagheri, M.: Automatic road crack detection and classification using image processing techniques, machine learning and integrated models in urban areas: A novel image binarization technique. Journal of Industrial and Systems Engineering 11(Special issue: 14th International Industrial Engineering Conference), 85\u201397 (2018)"},{"key":"1538_CR9","doi-asserted-by":"crossref","unstructured":"Sari, Y., Prakoso, P.B., Baskara, A.R.: Road Crack Detection using Support Vector Machine (SVM) and OTSU Algorithm. In: 2019 6th International Conference on Electric Vehicular Technology (ICEVT), pp. 349\u2013354. IEEE, Bali, Indonesia (2019)","DOI":"10.1109\/ICEVT48285.2019.8993969"},{"key":"1538_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2023.104767","volume":"148","author":"H Nhat-Duc","year":"2023","unstructured":"Nhat-Duc, H., Van-Duc, T.: Comparison of histogram-based gradient boosting classification machine, random Forest, and deep convolutional neural network for pavement raveling severity classification. Autom. Constr. 148, 104767 (2023)","journal-title":"Autom. Constr."},{"issue":"3","key":"1538_CR11","doi-asserted-by":"publisher","first-page":"2927","DOI":"10.1007\/s00521-022-07736-x","volume":"35","author":"E Raslan","year":"2023","unstructured":"Raslan, E., Alrahmawy, M.F., Mohammed, Y.A., Tolba, A.S.: IoT for measuring road network quality index. Neural Comput. Appl. 35(3), 2927\u20132944 (2023)","journal-title":"Neural Comput. Appl."},{"issue":"6","key":"1538_CR12","doi-asserted-by":"publisher","first-page":"4549","DOI":"10.1007\/s00521-022-07929-4","volume":"35","author":"S Jafarzadeh Ghoushchi","year":"2023","unstructured":"Jafarzadeh Ghoushchi, S., Shaffiee Haghshenas, S., Memarpour Ghiaci, A., Guido, G., Vitale, A.: Road safety assessment and risks prioritization using an integrated SWARA and MARCOS approach under spherical fuzzy environment. Neural Comput. Appl. 35(6), 4549\u20134567 (2023)","journal-title":"Neural Comput. Appl."},{"issue":"4","key":"1538_CR13","doi-asserted-by":"publisher","first-page":"3324","DOI":"10.1109\/TITS.2020.3035663","volume":"23","author":"Y Yu","year":"2022","unstructured":"Yu, Y., Guan, H., Li, D., Zhang, Y., Jin, S., Yu, C.: CCapFPN: a context-augmented capsule feature pyramid network for pavement crack detection. IEEE Trans. Intell. Transp. Syst. 23(4), 3324\u20133335 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"11","key":"1538_CR14","doi-asserted-by":"publisher","first-page":"12686","DOI":"10.1109\/TITS.2023.3287533","volume":"24","author":"L Yang","year":"2023","unstructured":"Yang, L., Huang, H., Kong, S., Liu, Y., Yu, H.: PAF-Net: a progressive and adaptive fusion network for pavement crack segmentation. IEEE Trans. Intell. Transport. Syst. 24 (11), 12686\u201312700 (2023). https:\/\/doi.org\/10.1109\/TITS.2023.3287533","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"1538_CR15","unstructured":"Jiang, L., Xie, Y., Ren, T.: A deep neural networks approach for pixel-level runway pavement crack segmentation using drone-captured images. arXiv preprint arXiv:2001.03257 (2020)"},{"key":"1538_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2022.104689","volume":"146","author":"Z Liu","year":"2023","unstructured":"Liu, Z., Yeoh, J.K.W., Gu, X., Dong, Q., Chen, Y., Wu, W., Wang, L., Wang, D.: Automatic pixel-level detection of vertical cracks in asphalt pavement based on GPR investigation and improved mask R-CNN. Autom. Constr. 146, 104689 (2023)","journal-title":"Autom. Constr."},{"key":"1538_CR17","doi-asserted-by":"crossref","unstructured":"Fan, R., Bocus, M.J., Zhu, Y., Jiao, J., Wang, L., Ma, F., Cheng, S., Liu, M.: Road crack detection using deep convolutional neural network and adaptive thresholding. In: 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 474\u2013479 (2019). IEEE","DOI":"10.1109\/IVS.2019.8814000"},{"key":"1538_CR18","doi-asserted-by":"crossref","unstructured":"Arya, D., Maeda, H., Ghosh, S.K., Toshniwal, D., Sekimoto, Y.: Rdd2022: A multi-national image dataset for automatic road damage detection. arXiv preprint arXiv:2209.08538 (2022)","DOI":"10.1016\/j.dib.2021.107133"},{"key":"1538_CR19","unstructured":"Li, D., Li, L., Chen, Z., Li, J.: Shift-ConvNets: Small Convolutional Kernel with Large Kernel Effects. arXiv (2024)"},{"key":"1538_CR20","doi-asserted-by":"crossref","unstructured":"Dai, X., Chen, Y., Xiao, B., Chen, D., Liu, M., Yuan, L., Zhang, L.: Dynamic head: unifying object detection heads with attentions. arXiv (2021)","DOI":"10.1109\/CVPR46437.2021.00729"},{"key":"1538_CR21","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 936\u2013944. IEEE, Honolulu, HI (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"1538_CR22","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8759\u20138768. IEEE, Salt Lake City, UT (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"1538_CR23","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Lin, T.-Y., Le, Q.V.: NAS-FPN: learning scalable feature pyramid architecture for object detection. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7029\u20137038. IEEE, Long Beach, CA, USA (2019)","DOI":"10.1109\/CVPR.2019.00720"},{"key":"1538_CR24","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., Le, Q.V.: Efficientdet: scalable and efficient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781\u201310790 (2020)","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"1538_CR25","doi-asserted-by":"crossref","unstructured":"Lv, W., Zhao, Y., Xu, S., Wei, J., Wang, G., Cui, C., Du, Y., Dang, Q., Liu, Y.: DETRs beat YOLOs on real-time object detection. arXiv (2023)","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"1538_CR26","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779\u2013788. IEEE, Las Vegas, NV, USA (2016)","DOI":"10.1109\/CVPR.2016.91"},{"issue":"9","key":"1538_CR27","doi-asserted-by":"publisher","first-page":"3274","DOI":"10.1080\/10298436.2021.1888092","volume":"23","author":"C Chen","year":"2022","unstructured":"Chen, C., Seo, H., Jun, C.H., Zhao, Y.: Pavement crack detection and classification based on fusion feature of LBP and PCA with SVM. Int. J. Pavement Eng. 23(9), 3274\u20133283 (2022)","journal-title":"Int. J. Pavement Eng."},{"key":"1538_CR28","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.aei.2019.04.004","volume":"40","author":"N-D Hoang","year":"2019","unstructured":"Hoang, N.-D.: Image processing based automatic recognition of asphalt pavement patch using a metaheuristic optimized machine learning approach. Adv. Eng. Inform. 40, 110\u2013120 (2019)","journal-title":"Adv. Eng. Inform."},{"issue":"10","key":"1538_CR29","doi-asserted-by":"publisher","first-page":"2718","DOI":"10.1109\/TITS.2015.2477675","volume":"17","author":"R Amhaz","year":"2016","unstructured":"Amhaz, R., Chambon, S., Idier, J., Baltazart, V.: Automatic crack detection on two-dimensional pavement images: an algorithm based on minimal path selection. IEEE Trans. Intell. Transp. Syst. 17(10), 2718\u20132729 (2016)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"2","key":"1538_CR30","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/s00530-024-01282-3","volume":"30","author":"M Xue","year":"2024","unstructured":"Xue, M., Xu, Z., Qiao, S., Zheng, J., Li, T., Wang, Y., Peng, D.: Driver intention prediction based on multi-dimensional cross-modality information interaction. Multimedia Syst. 30(2), 83 (2024)","journal-title":"Multimedia Syst."},{"issue":"2","key":"1538_CR31","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1007\/s00530-022-00944-4","volume":"29","author":"F Liu","year":"2023","unstructured":"Liu, F., Wang, J., Chen, D., Shen, C., Xu, F.: Asymmetric exponential loss function for crack segmentation. Multimedia Syst. 29(2), 539\u2013552 (2023)","journal-title":"Multimedia Syst."},{"issue":"10","key":"1538_CR32","doi-asserted-by":"publisher","first-page":"18392","DOI":"10.1109\/TITS.2022.3158670","volume":"23","author":"X Sun","year":"2022","unstructured":"Sun, X., Xie, Y., Jiang, L., Cao, Y., Liu, B.: DMA-Net: DeepLab with multi-scale attention for pavement crack segmentation. IEEE Trans. Intell. Transp. Syst. 23(10), 18392\u201318403 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"9","key":"1538_CR33","doi-asserted-by":"publisher","first-page":"10099","DOI":"10.1109\/TITS.2023.3267433","volume":"24","author":"R Ren","year":"2023","unstructured":"Ren, R., Shi, P., Jia, P., Xu, X.: A semi-supervised learning approach for pixel-level pavement anomaly detection. IEEE Trans. Intell. Transp. Syst. 24(9), 10099\u201310107 (2023)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"9","key":"1538_CR34","doi-asserted-by":"publisher","first-page":"16038","DOI":"10.1109\/TITS.2022.3147669","volume":"23","author":"Z Qu","year":"2022","unstructured":"Qu, Z., Wang, C.-Y., Wang, S.-Y., Ju, F.-R.: A method of hierarchical feature fusion and connected attention architecture for pavement crack detection. IEEE Trans. Intell. Transp. Syst. 23(9), 16038\u201316047 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1538_CR35","unstructured":"Guo, M.-H., Lu, C.-Z., Liu, Z.-N., Cheng, M.-M., Hu, S.-M.: Visual Attention Network. arXiv (2022)"},{"key":"1538_CR36","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv (2015)"},{"key":"1538_CR37","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E.: Squeeze-and-excitation networks. arXiv (2019)","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"6","key":"1538_CR38","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1538_CR39","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv (2018)"},{"key":"1538_CR40","unstructured":"Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)"},{"key":"1538_CR41","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: YOLOX: exceeding YOLO series in 2021. arXiv (2021)"},{"key":"1538_CR42","doi-asserted-by":"crossref","unstructured":"Hegde, V., Trivedi, D., Alfarrarjeh, A., Deepak, A., Ho\u00a0Kim, S., Shahabi, C.: Yet another deep learning approach for road damage detection using ensemble learning, 5553\u20135558 (2020)","DOI":"10.1109\/BigData50022.2020.9377833"},{"key":"1538_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2024.114443","volume":"229","author":"H Hu","year":"2024","unstructured":"Hu, H., Li, Z., He, Z., Wang, L., Cao, S., Du, W.: Road surface crack detection method based on improved yolov5 and vehicle-mounted images. Measurement 229, 114443 (2024)","journal-title":"Measurement"},{"issue":"10","key":"1538_CR44","doi-asserted-by":"publisher","first-page":"2377","DOI":"10.3390\/math11102377","volume":"11","author":"G Yu","year":"2023","unstructured":"Yu, G., Zhou, X.: An improved yolov5 crack detection method combined with a bottleneck transformer. Mathematics. 11(10), 2377 (2023). https:\/\/doi.org\/10.3390\/math11102377","journal-title":"Mathematics"},{"issue":"2","key":"1538_CR45","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1007\/s11554-023-01405-5","volume":"21","author":"M Zhao","year":"2024","unstructured":"Zhao, M., Su, Y., Wang, J., Liu, X., Wang, K., Liu, Z., Liu, M., Guo, Z.: Med-yolov8s: a new real-time road crack, pothole, and patch detection model. J. Real-Time Image Proc. 21(2), 26 (2024)","journal-title":"J. Real-Time Image Proc."},{"key":"1538_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2023.105062","volume":"155","author":"J Li","year":"2023","unstructured":"Li, J., Yuan, C., Wang, X.: Real-time instance-level detection of asphalt pavement distress combining space-to-depth (spd) yolo and omni-scale network (osnet). Autom. Constr. 155, 105062 (2023)","journal-title":"Autom. Constr."},{"issue":"1","key":"1538_CR47","first-page":"8879622","volume":"2023","author":"Z Diao","year":"2023","unstructured":"Diao, Z., Huang, X., Liu, H., Liu, Z.: Le-yolov5: a lightweight and efficient road damage detection algorithm based on improved yolov5. Int. J. Intell. Syst. 2023(1), 8879622 (2023)","journal-title":"Int. J. Intell. Syst."},{"issue":"1","key":"1538_CR48","doi-asserted-by":"publisher","first-page":"16758","DOI":"10.1038\/s41598-024-67953-3","volume":"14","author":"J Wang","year":"2024","unstructured":"Wang, J., Meng, R., Huang, Y., Zhou, L., Huo, L., Qiao, Z., Niu, C.: Road defect detection based on improved yolov8s model. Sci. Rep. 14(1), 16758 (2024)","journal-title":"Sci. Rep."},{"key":"1538_CR49","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Yeh, I.-H., Liao, H.-Y.M.: Yolov9: learning what you want to learn using programmable gradient information. arXiv preprint arXiv:2402.13616 (2024)","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"1538_CR50","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., Ding, G.: Yolov10: Real-time end-to-end object detection. arXiv preprint arXiv:2405.14458 (2024)"},{"issue":"12","key":"1538_CR51","doi-asserted-by":"publisher","first-page":"15105","DOI":"10.1109\/TITS.2023.3300312","volume":"24","author":"T Zhang","year":"2023","unstructured":"Zhang, T., Wang, D., Lu, Y.: ECSNet: an accelerated real-time image segmentation CNN architecture for pavement crack detection. IEEE Trans. Intell. Transport. Syst. 24(12), 15105\u201315112 (2023). https:\/\/doi.org\/10.1109\/TITS.2023.3300312","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"issue":"10","key":"1538_CR52","doi-asserted-by":"publisher","first-page":"14164","DOI":"10.1109\/TNNLS.2023.3274926","volume":"35","author":"H Liu","year":"2024","unstructured":"Liu, H., Jin, F., Zeng, H., Pu, H., Fan, B.: Image enhancement guided object detection in visually degraded scenes. IEEE Trans. Neural Netw. Learn. Syst. 35(10), 14164\u201314177 (2024). https:\/\/doi.org\/10.1109\/TNNLS.2023.3274926","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01538-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-024-01538-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01538-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T04:16:35Z","timestamp":1734322595000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-024-01538-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,20]]},"references-count":52,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["1538"],"URL":"https:\/\/doi.org\/10.1007\/s00530-024-01538-y","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4350707\/v1","asserted-by":"object"}]},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,20]]},"assertion":[{"value":"30 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 November 2024","order":3,"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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"351"}}