{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T16:06:06Z","timestamp":1772640366018,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,2,25]],"date-time":"2023-02-25T00:00:00Z","timestamp":1677283200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,2,25]],"date-time":"2023-02-25T00:00:00Z","timestamp":1677283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100013058","name":"Jiangsu Provincial Key Research and Development Program","doi-asserted-by":"publisher","award":["BE2019710"],"award-info":[{"award-number":["BE2019710"]}],"id":[{"id":"10.13039\/501100013058","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010881","name":"Suzhou Municipal Science and Technology Bureau","doi-asserted-by":"publisher","award":["SYS2019008"],"award-info":[{"award-number":["SYS2019008"]}],"id":[{"id":"10.13039\/501100010881","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010229","name":"Natural Science Foundation of Tianjin Municipal Science and Technology Commission","doi-asserted-by":"crossref","award":["CE20195001"],"award-info":[{"award-number":["CE20195001"]}],"id":[{"id":"10.13039\/501100010229","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s11517-023-02777-3","type":"journal-article","created":{"date-parts":[[2023,2,25]],"date-time":"2023-02-25T20:02:17Z","timestamp":1677355337000},"page":"1631-1648","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["AI-assisted identification of intrapapillary capillary loops in magnification endoscopy for diagnosing early-stage esophageal squamous cell carcinoma: a preliminary study"],"prefix":"10.1007","volume":"61","author":[{"given":"Jinming","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qigang","family":"Long","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9259-3840","authenticated-orcid":false,"given":"Yadong","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8001-0828","authenticated-orcid":false,"given":"Peng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1952-3026","authenticated-orcid":false,"given":"Lingxiao","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,25]]},"reference":[{"issue":"8","key":"2777_CR1","doi-asserted-by":"publisher","first-page":"1941","DOI":"10.1002\/ijc.31937","volume":"144","author":"J Ferlay","year":"2019","unstructured":"Ferlay J, Colombet M, Soerjomataram I, Mathers C, Parkin D, Pi\u00f1eros M, Znaor A, Bray F (2019) Estimating the global cancer incidence and mortality in: 2018 Globocan sources and methods. Int J Cancer 144(8):1941\u20131953","journal-title":"Int J Cancer"},{"issue":"6","key":"2777_CR2","first-page":"394","volume":"68","author":"F Bray","year":"2018","unstructured":"Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer J Clin 68(6):394\u2013424","journal-title":"CA: A Cancer J Clin"},{"issue":"9","key":"2777_CR3","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s11912-018-0713-y","volume":"20","author":"M Naveed","year":"2018","unstructured":"Naveed M, Kubiliun N (2018) Endoscopic treatment of early-stage esophageal cancer. Curr Oncol Rep 20(9):71","journal-title":"Curr Oncol Rep"},{"issue":"1","key":"2777_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10388-014-0465-1","volume":"12","author":"H Kuwano","year":"2015","unstructured":"Kuwano H, Nishimura Y, Oyama T, Kato H, Kitagawa Y, Kusano M, Shimada H, Takiuchi H, Toh Y, Doki Y, Naomoto Y, Matsubara H, Miyazaki T, Muto M, Yanagisawa A (2015) Guidelines for diagnosis and treatment of carcinoma of the esophagus April 2012 edited by the Japan esophageal society. Esophagus 12(1):1\u201330","journal-title":"Esophagus"},{"key":"2777_CR5","doi-asserted-by":"crossref","unstructured":"Yada T, Yokoi C, Uemura N (2013) The current state of diagnosis and treatment for early gastric cancer. Diagnostic and therapeutic endoscopy","DOI":"10.1155\/2013\/241320"},{"key":"2777_CR6","doi-asserted-by":"crossref","unstructured":"Shinozaki S, Osawa H, Hayashi Y, Lefor AK, Yamamoto H (2019) Linked color imaging for the detection of early gastrointestinal neoplasms. Ther Adv Gastroenterol 12","DOI":"10.1177\/1756284819885246"},{"issue":"10","key":"2777_CR7","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.1016\/j.dld.2018.03.027","volume":"50","author":"W Diao","year":"2018","unstructured":"Diao W, Huang X, Shen L, Zeng Z (2018) Diagnostic ability of blue laser imaging combined with magnifying endoscopy for early esophageal cancer. Dig Liver Dis 50(10):1035\u20131040","journal-title":"Dig Liver Dis"},{"issue":"5","key":"2777_CR8","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1111\/j.1442-2050.2009.00942.x","volume":"22","author":"K Goda","year":"2009","unstructured":"Goda K, Tajiri H, Ikegami M, Yoshida Y, Yoshimura N, Kato M, Sumiyama K, Imazu H, Matsuda K, Kaise M, Kato T, Omar S (2009) Magnifying endoscopy with narrow band imaging for predicting the invasion depth of superficial esophageal squamous cell carcinoma. Dis Esophagus 22 (5):453\u2013460","journal-title":"Dis Esophagus"},{"issue":"2","key":"2777_CR9","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/s10388-016-0527-7","volume":"14","author":"T Oyama","year":"2017","unstructured":"Oyama T, Inoue H, Arima M, Momma K, Omori T, Ishihara R, Hirasawa D, Takeuchi M, Tomori A, Goda K (2017) Prediction of the invasion depth of superficial squamous cell carcinoma based on microvessel morphology: magnifying endoscopic classification of the Japan esophageal society. Esophagus 14(2):105\u2013112","journal-title":"Esophagus"},{"issue":"1","key":"2777_CR10","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1111\/den.13553","volume":"32","author":"K Goda","year":"2020","unstructured":"Goda K, Irisawa A (2020) Japan esophageal society classification for predicting the invasion depth of superficial esophageal squamous cell carcinoma: Should it be modified now? Dig Endosc 32(1):37\u201338","journal-title":"Dig Endosc"},{"issue":"12","key":"2777_CR11","doi-asserted-by":"publisher","first-page":"3448","DOI":"10.1007\/s10620-020-06643-2","volume":"65","author":"T Syed","year":"2020","unstructured":"Syed T, Doshi A, Guleria S, Syed S, Shah T (2020) Artificial intelligence and its role in identifying esophageal neoplasia. Dig Dis Sci 65(12):3448\u20133455","journal-title":"Dig Dis Sci"},{"issue":"35","key":"2777_CR12","doi-asserted-by":"publisher","first-page":"5256","DOI":"10.3748\/wjg.v26.i35.5256","volume":"26","author":"YH Zhang","year":"2020","unstructured":"Zhang YH, Guo LJ, Yuan XL, Hu B (2020) Artificial intelligence-assisted esophageal cancer management: Now and future. World J Gastroenterol 26(35):5256\u20135271. https:\/\/doi.org\/10.3748\/wjg.v26.i35.5256","journal-title":"World J Gastroenterol"},{"key":"2777_CR13","doi-asserted-by":"crossref","unstructured":"Laz\u0103r DC, Avram MF, Faur AC, Goldi\u015f A, Romo\u015fan I, T\u0103ban S, Cornianu M (2020) The impact of artificial intelligence in the endoscopic assessment of premalignant and malignant esophageal lesions: Present and future. Medicina 56(7)","DOI":"10.3390\/medicina56070364"},{"issue":"10","key":"2777_CR14","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","volume":"43","author":"J Wang","year":"2021","unstructured":"Wang J, Sun K, Cheng T, Jiang B, Deng C, Zhao Y, Liu D, Mu Y, Tan M, Wang X, Liu W, Xiao B (2021) Deep high-resolution representation learning for visual recognition. IEEE Trans Pattern Anal Mach Intell 43(10):3349\u20133364. https:\/\/doi.org\/10.1109\/TPAMI.2020.2983686","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2777_CR15","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28"},{"key":"2777_CR16","unstructured":"Sun K, Zhao Y, Jiang B, Cheng T, Xiao B, Liu D, Mu Y, Wang X, Liu W, Wang J (2019) High-resolution representations for labeling pixels and regions. arXiv:190404514"},{"key":"2777_CR17","doi-asserted-by":"crossref","unstructured":"Lin TY, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"2777_CR18","doi-asserted-by":"crossref","unstructured":"Liu H, Liu F, Fan X, Huang D (2021) Polarized self-attention: Towards high-quality pixel-wise regression. arXiv:210700782","DOI":"10.1016\/j.neucom.2022.07.054"},{"key":"2777_CR19","doi-asserted-by":"publisher","first-page":"AB581","DOI":"10.1016\/j.gie.2017.03.1354","volume":"85","author":"C Zhang","year":"2017","unstructured":"Zhang C, Ma L, Matsuura N, Tam P, Teoh A (2017) Tu1217 the use of convolutional neural artificial intelligence network to aid the diagnosis and classification of early esophageal neoplasia. A feasibility study. Gastroint Endosc 85:AB581\u2013AB582","journal-title":"Gastroint Endosc"},{"issue":"6","key":"2777_CR20","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1007\/s40846-016-0182-4","volume":"36","author":"DX Xue","year":"2016","unstructured":"Xue DX, Zhang R, Feng H, Wang YL (2016) CNN-SVM for microvascular morphological type recognition with data augmentation. J Med Biol Eng 36(6):755\u2013764","journal-title":"J Med Biol Eng"},{"issue":"4","key":"2777_CR21","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1055\/a-0756-8754","volume":"51","author":"Y Zhao","year":"2019","unstructured":"Zhao Y, Xue DX, Wang YL, Zhang R, Sun B, Cai YP, Feng H, Cai Y, Xu JM (2019) Computer-assisted diagnosis of early esophageal squamous cell carcinoma using narrow-band imaging magnifying endoscopy. Endoscopy 51(4):333\u2013341","journal-title":"Endoscopy"},{"issue":"2","key":"2777_CR22","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.gie.2019.09.034","volume":"91","author":"M Ohmori","year":"2020","unstructured":"Ohmori M, Ishihara R, Aoyama K, Nakagawa K, Iwagami H, Matsuura N, Shichijo S, Yamamoto K, Nagaike K, Nakahara M, Inoue T, Aoi K, Okada H, Tada T (2020) Endoscopic detection and differentiation of esophageal lesions using a deep neural network. Gastrointest Endosc 91(2):301\u2013309.e1","journal-title":"Gastrointest Endosc"},{"issue":"3","key":"2777_CR23","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/j.gie.2019.04.245","volume":"90","author":"K Nakagawa","year":"2019","unstructured":"Nakagawa K, Ishihara R, Aoyama K, Ohmori M, Nakahira H, Matsuura N, Shichijo S, Nishida T, Yamada T, Yamaguchi S, Ogiyama H, Egawa S, Kishida O, Tada T (2019) Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists. Gastrointest Endosc 90(3):407\u2013414","journal-title":"Gastrointest Endosc"},{"issue":"2","key":"2777_CR24","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1177\/2050640618821800","volume":"7","author":"M Everson","year":"2019","unstructured":"Everson M, Herrera L, Li W, Luengo I, Ahmad OF, Banks M, Magee CG, Alzoubaidi D, Hsu HM, Graham D, Vercauteren TKM, Lovat LB, Ourselin S, Kashin S, Wang H, Wang WL, Haidry RJ (2019) Artificial intelligence for the real-time classification of intrapapillary capillary loop patterns in the endoscopic diagnosis of early oesophageal squamous cell carcinoma: A proof-of-concept study. United Eur Gastroenterol J 7(2):297\u2013306","journal-title":"United Eur Gastroenterol J"},{"issue":"4","key":"2777_CR25","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1007\/s11548-020-02127-w","volume":"15","author":"LC Garc\u00eda-Peraza-Herrera","year":"2020","unstructured":"Garc\u00eda-Peraza-Herrera LC, Everson M, Lovat LB, Wang H, Wang WL, Haidry RJ, Stoyanov D, Ourselin S, Vercauteren TKM (2020) Intrapapillary capillary loop classification in magnification endoscopy: open dataset and baseline methodology. Int J Comput Assist Radiol Surg 15(4):651\u2013659","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"1","key":"2777_CR26","first-page":"41","volume":"91","author":"L Jie Guo","year":"2019","unstructured":"Jie Guo L, Xiao X, Wu C, Zeng X, Hang Zhang Y, Du J, Bai S, Xie J, Zhang Z, Li Y, Wang X, Cheung O, Sharma M, Liu J, Hu B (2019) Real-time automated diagnosis of precancerous lesion and early esophageal squamous cell carcinoma using a deep learning model (with videos). Gastrointest Endosc 91(1):41\u201351","journal-title":"Gastrointest Endosc"},{"key":"2777_CR27","unstructured":"Bochkovskiy A, Wang CY, Liao HYM (2020) YOLOV4: Optimal speed and accuracy of object detection. arXiv:200410934"},{"issue":"5814","key":"2777_CR28","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1126\/science.1136800","volume":"315","author":"BJ Frey","year":"2007","unstructured":"Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315 (5814):972\u2013976","journal-title":"Science"},{"key":"2777_CR29","doi-asserted-by":"crossref","unstructured":"Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. In: International conference on medical image computing and computer-assisted intervention, pp 130\u2013137","DOI":"10.1007\/BFb0056195"},{"issue":"11","key":"2777_CR30","doi-asserted-by":"publisher","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","volume":"30","author":"ZQ Zhao","year":"2019","unstructured":"Zhao ZQ, Zheng P, Xu ST, Wu X (2019) Object detection with deep learning : A review. IEEE Trans Neural Netw Learn Syst 30(11):3212\u20133232. https:\/\/doi.org\/10.1109\/TNNLS.2018.2876865","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2777_CR31","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:14091556"},{"key":"2777_CR32","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2777_CR33","doi-asserted-by":"crossref","unstructured":"Sun K, Xiao B, Liu D, Wang J (2019) Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5693\u20135703","DOI":"10.1109\/CVPR.2019.00584"},{"key":"2777_CR34","doi-asserted-by":"publisher","first-page":"104,117","DOI":"10.1016\/j.imavis.2021.104117","volume":"107","author":"R Solovyev","year":"2021","unstructured":"Solovyev R, Wang W, Gabruseva T (2021) Weighted boxes fusion: Ensembling boxes from different object detection models. Image Vision Comput 107:104,117","journal-title":"Image Vision Comput"},{"key":"2777_CR35","doi-asserted-by":"publisher","unstructured":"Neubeck A, Van Gool L (2006) Efficient non-maximum suppression. In: 18th international conference on pattern recognition (ICPR\u201906), vol 3, pp 850\u2013855, DOI https:\/\/doi.org\/10.1109\/ICPR.2006.479","DOI":"10.1109\/ICPR.2006.479"},{"key":"2777_CR36","doi-asserted-by":"publisher","unstructured":"Bodla N, Singh B, Chellappa R, Davis LS (2017) Soft-NMS\u2013improving object detection with one line of code. In: Proceedings of the IEEE international conference on computer vision, pp 5561\u20135569. https:\/\/doi.org\/10.1109\/ICCV.2017.593","DOI":"10.1109\/ICCV.2017.593"},{"key":"2777_CR37","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7263\u20137271","DOI":"10.1109\/CVPR.2017.690"},{"issue":"2","key":"2777_CR38","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (VOC) challenge. Int J Comput Vision 88(2):303\u2013338","journal-title":"Int J Comput Vision"},{"key":"2777_CR39","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) SSD: Single shot multibox detector. In: European conference on computer vision, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"2777_CR40","doi-asserted-by":"publisher","unstructured":"Lin TY, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2999\u20133007. https:\/\/doi.org\/10.1109\/ICCV.2017.324","DOI":"10.1109\/ICCV.2017.324"},{"key":"2777_CR41","doi-asserted-by":"crossref","unstructured":"Zhang H, Wang Y, Dayoub F, Sunderhauf N (2021) Varifocalnet: An iou-aware dense object detector. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8514\u20138523","DOI":"10.1109\/CVPR46437.2021.00841"},{"key":"2777_CR42","doi-asserted-by":"crossref","unstructured":"Pang J, Chen K, Shi J, Feng H, Ouyang W, Lin D (2019) Libra R-CNN: Towards balanced learning for object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 821\u2013830","DOI":"10.1109\/CVPR.2019.00091"},{"key":"2777_CR43","doi-asserted-by":"crossref","unstructured":"Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path aggregation network for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8759\u20138768","DOI":"10.1109\/CVPR.2018.00913"},{"key":"2777_CR44","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2777_CR45","unstructured":"Zhang H, Zu K, Lu J, Zou Y, Meng D (2021) EPSANet: An efficient pyramid squeeze attention block on convolutional neural network. arXiv:210514447"},{"key":"2777_CR46","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY, Kweon IS (2018) CBAM: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02777-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-02777-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02777-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T01:03:48Z","timestamp":1687136628000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-02777-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,25]]},"references-count":46,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["2777"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-02777-3","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,25]]},"assertion":[{"value":"21 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2023","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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of interest"}}]}}