{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T23:58:26Z","timestamp":1780617506321,"version":"3.54.1"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"28","license":[{"start":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T00:00:00Z","timestamp":1689984000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T00:00:00Z","timestamp":1689984000000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s00521-023-08859-5","type":"journal-article","created":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T07:01:26Z","timestamp":1690009286000},"page":"20631-20662","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["An advanced diagnostic ColoRectalCADx utilises CNN and unsupervised visual explanations to discover malignancies"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3237-2859","authenticated-orcid":false,"given":"Akella S. Narasimha","family":"Raju","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kayalvizhi","family":"Jayavel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"T.","family":"Rajalakshmi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,22]]},"reference":[{"issue":"69","key":"8859_CR1","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.mam.2019.06.005","volume":"1","author":"N Murphy","year":"2019","unstructured":"Murphy N, Moreno V, Hughes DJ, Vodicka L, Vodicka P, Aglago EK, Gunter MJ, Jenab M (2019) Lifestyle and dietary environmental factors in colorectal cancer susceptibility. Mol Aspec Med. 1(69):2\u20139","journal-title":"Mol Aspec Med."},{"key":"8859_CR2","unstructured":"Americal cancer Society, ACS. [Online]. [Accessed 15 November 2022]"},{"key":"8859_CR3","unstructured":"Colorectal Cancer Facts & Figures 2020\u20132022, (2022) American Cancer Society"},{"key":"8859_CR4","unstructured":"World Health Organization (WHO, 2018. [Online]. Available: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/cancer. [Accessed 1 December 2022]"},{"key":"8859_CR5","unstructured":"American Cancer Society, 2021. [Online]. Available: https:\/\/www.cancer.org\/cancer\/colon-rectal-cancer\/detection-diagnosis-staging\/survival-rates.html. [Accessed 2 December 2022]"},{"key":"8859_CR6","unstructured":"Cancer Facts & Figures (2021) American cancer society"},{"key":"8859_CR7","unstructured":"Colorectal Cancer: Risk Factors and Prevention, ASCO, May 2022. [Online]. Available: https:\/\/www.cancer.net\/cancer-types\/colorectal-cancer\/risk-factors-and-prevention. [Accessed 20 November 2022]"},{"issue":"3","key":"8859_CR8","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1109\/TITB.2012.2185807","volume":"16","author":"B Li","year":"2012","unstructured":"Li B, Meng MQ (2012) Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection. IEEE Trans Inform Technol Biomed. 16(3):323\u2013329","journal-title":"IEEE Trans Inform Technol Biomed."},{"issue":"1","key":"8859_CR9","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1080\/10408363.2019.1670614","volume":"57","author":"E Hissong","year":"2020","unstructured":"Hissong E, Pittman ME (2020) Colorectal carcinoma screening: established methods and emerging technology. Crit Rev Clin Lab Sci 57(1):22\u201336","journal-title":"Crit Rev Clin Lab Sci"},{"issue":"28","key":"8859_CR10","doi-asserted-by":"publisher","first-page":"5086","DOI":"10.3748\/wjg.v23.i28.5086","volume":"23","author":"IA Issa","year":"2017","unstructured":"Issa IA, Noureddine M (2017) Colorectal cancer screening: an updated review of the available options. World J Gastroenterol 23(28):5086","journal-title":"World J Gastroenterol"},{"issue":"1","key":"8859_CR11","doi-asserted-by":"publisher","first-page":"44","DOI":"10.3322\/canjclin.53.1.44","volume":"53","author":"B Levin","year":"2003","unstructured":"Levin B, Brooks D, Smith RA, Stone A (2003) Emerging technologies in screening for colorectal cancer: CT Colonography, immunochemical fecal occult blood tests, and stool screening using molecular markers. CA A Cancer J Clin. 53(1):44\u201355","journal-title":"CA A Cancer J Clin."},{"key":"8859_CR12","doi-asserted-by":"crossref","unstructured":"Taha B, Werghi N, Dias J (2017) Automatic polyp detection in endoscopy videos: A survey, In: Proceedings of the lASTED international conference biomedical engineering (BioMed 2017), Innsbruck, Aus t r i a","DOI":"10.2316\/P.2017.852-031"},{"issue":"1","key":"8859_CR13","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1038\/s41597-020-00622-y","volume":"7","author":"H Borgli","year":"2020","unstructured":"Borgli H, Thambawita V, Smedsrud PH, Hicks S, Jha D, Eskeland SL, Randel KR, Pogorelov K, Lux M, Nguyen DT, Johansen D (2020) HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy. Sci Data. 7(1):283","journal-title":"Sci Data."},{"key":"8859_CR14","doi-asserted-by":"publisher","first-page":"93145","DOI":"10.1109\/ACCESS.2020.2993887","volume":"8","author":"H Tang","year":"2020","unstructured":"Tang H, Hu Z (2020) Research on medical image classification based on machine learning. IEEE Access. 8:93145\u201393154","journal-title":"IEEE Access."},{"issue":"15","key":"8859_CR15","doi-asserted-by":"publisher","first-page":"3707","DOI":"10.3390\/cancers14153707","volume":"14","author":"MS Kavitha","year":"2022","unstructured":"Kavitha MS, Gangadaran P, Jackson A, Venmathi Maran BA, Kurita T, Ahn BC (2022) Deep neural network models for colon cancer screening. Cancers 14(15):3707","journal-title":"Cancers"},{"issue":"70","key":"8859_CR16","first-page":"1","volume":"10","author":"D Banik","year":"2020","unstructured":"Banik D, Roy K, Bhattacharjee D, Nasipuri M, Krejcar O (2020) Polyp-net: a multimodel fusion network for polyp segmentation. IEEE Trans Instrum Measur. 10(70):1\u20132","journal-title":"IEEE Trans Instrum Measur."},{"key":"8859_CR17","doi-asserted-by":"crossref","unstructured":"Cao Z, Pan X, Yu H, Hua S, Wang D, Chen DZ, Zhou M, Wu5 J, (2022) A deep learning approach for detecting colorectal cancer via Raman spectra, BMEF","DOI":"10.34133\/2022\/9872028"},{"key":"8859_CR18","unstructured":"Siva Naga Raju M, Rao BS, (2022) Colorectal cancer disease classification and segmentation using a novel deep learning approach, Int J Intell Eng Syst, 4, 29"},{"key":"8859_CR19","doi-asserted-by":"publisher","first-page":"103638","DOI":"10.1016\/j.jbi.2020.103638","volume":"113","author":"S Ozturk","year":"2021","unstructured":"Ozturk S, ozakaya U (2021) Residual LSTM layered CNN for classification of gastrointestinal tract diseases. J Biomed Inform 113:103638","journal-title":"J Biomed Inform"},{"key":"8859_CR20","first-page":"3","volume":"2021","author":"Y Wang","year":"2021","unstructured":"Wang Y, Feng Z, Song L, Liu X, Liu S (2021) Multiclassification of endoscopic colonoscopy images based on deep transfer learning. Comput Math Methods Med 2021:3","journal-title":"Comput Math Methods Med"},{"key":"8859_CR21","doi-asserted-by":"publisher","first-page":"47124","DOI":"10.1109\/ACCESS.2022.3171238","volume":"10","author":"M Souaidi","year":"2022","unstructured":"Souaidi M, El Ansari M (2022) A new automated polyp detection network MP-FSSD in WCE and colonoscopy images based fusion single shot multibox detector and transfer learning. IEEE Access. 10:47124\u201347140","journal-title":"IEEE Access."},{"key":"8859_CR22","doi-asserted-by":"publisher","first-page":"52971","DOI":"10.1109\/ACCESS.2022.3175858","volume":"10","author":"Q Guo","year":"2022","unstructured":"Guo Q, Fang X, Wang L, Zhang E (2022) Polyp segmentation of colonoscopy images by exploring the uncertain areas. IEEE Access. 10:52971\u201352981","journal-title":"IEEE Access."},{"issue":"20","key":"8859_CR23","doi-asserted-by":"publisher","first-page":"1496","DOI":"10.1056\/NEJM199911113412003","volume":"341","author":"HM Fenlon","year":"1999","unstructured":"Fenlon HM, Nunes DP, Schroy PC, Barish MA, Clarke PD, Ferrucci JT (1999) A comparison of virtual and conventional colonoscopy for the detection of colorectal polyps. New Engl J Med. 341(20):1496\u20131503","journal-title":"New Engl J Med."},{"issue":"17","key":"8859_CR24","doi-asserted-by":"publisher","first-page":"1609","DOI":"10.1056\/NEJMe2211595","volume":"387","author":"JA Dominitz","year":"2022","unstructured":"Dominitz JA, Robertson DJ (2022) Understanding the results of a randomized trial of screening colonoscopy. N Engl J Med 387(17):1609\u20131611","journal-title":"N Engl J Med"},{"issue":"3","key":"8859_CR25","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1109\/JBHI.2018.2885214","volume":"23","author":"F Milletari","year":"2018","unstructured":"Milletari F, Frei J, Aboulatta M, Vivar G, Ahmadi SA (2018) Cloud deployment of high-resolution medical image analysis with TOMAAT. IEEE J Biomed Health Inform 23(3):969\u2013977","journal-title":"IEEE J Biomed Health Inform"},{"key":"8859_CR26","doi-asserted-by":"crossref","unstructured":"Prashanth B, Mendu M, Thallapalli R, (2021) Cloud based machine learning with advanced predictive analytics using google colaboratory, Materials Today: Proceedings","DOI":"10.1016\/j.matpr.2021.01.800"},{"key":"8859_CR27","unstructured":"https:\/\/www.kaggle.com\/balraj98\/cvcclinicdb (2015) [Online]. [Accessed 25 May 2021]"},{"key":"8859_CR28","unstructured":"https:\/\/datasets.simula.no\/kvasir\/, (2016) [Online]. [Accessed 3 July 2021]"},{"key":"8859_CR29","unstructured":"https:\/\/datasets.simula.no\/hyper-kvasir\/, (2020) [Online]. [Accessed 3 July 2021]"},{"issue":"5","key":"8859_CR30","doi-asserted-by":"publisher","first-page":"141","DOI":"10.3390\/jimaging8050141","volume":"8","author":"P Oza","year":"2022","unstructured":"Oza P, Sharma P, Patel S, Adedoyin F, Bruno A (2022) Image augmentation techniques for mammogram analysis. J Imag 8(5):141","journal-title":"J Imag"},{"key":"8859_CR31","doi-asserted-by":"publisher","first-page":"91725","DOI":"10.1109\/ACCESS.2020.2994388","volume":"8","author":"A Basher","year":"2020","unstructured":"Basher A, Kim BC, Lee KH, Jung HY (2020) Automatic localization and discrete volume measurements of hippocampi from MRI data using a convolutional neural network. IEEE Access. 8:91725\u201391739","journal-title":"IEEE Access."},{"issue":"1","key":"8859_CR32","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.bbe.2018.10.004","volume":"39","author":"AK Anaraki","year":"2019","unstructured":"Anaraki AK, Ayati M, Kazemi F (2019) Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocybern Biomed Eng. 39(1):63\u201374","journal-title":"Biocybern Biomed Eng."},{"issue":"6","key":"8859_CR33","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.3390\/s19061265","volume":"19","author":"H Alaskar","year":"2019","unstructured":"Alaskar H, Hussain A, Al-Aseem N, Liatsis P, Al-Jumeily D (2019) Application of convolutional neural networks for automated ulcer detection in wireless capsule endoscopy images. Sensors 19(6):1265","journal-title":"Sensors"},{"key":"8859_CR34","doi-asserted-by":"publisher","first-page":"169537","DOI":"10.1109\/ACCESS.2019.2954675","volume":"7","author":"HA Qadir","year":"2019","unstructured":"Qadir HA, Solhusvik J, Bergsland J, Aabakken L, Balasingham I (2019) A framework with a fully convolutional neural network for semi-automatic colon polyp annotation. IEEE Access. 7:169537\u2013169547","journal-title":"IEEE Access."},{"key":"8859_CR35","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition, In computer vision and pattern recognition (cs.CV)","DOI":"10.1109\/CVPR.2016.90"},{"key":"8859_CR36","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van der Maaten L (2018) Densely connected convolutional networks, computer vision and pattern recognition (cs.CV)","DOI":"10.1109\/CVPR.2017.243"},{"key":"8859_CR37","doi-asserted-by":"publisher","first-page":"84374","DOI":"10.1109\/ACCESS.2019.2925585","volume":"7","author":"N Ghatwary","year":"2019","unstructured":"Ghatwary N, Ye X, Zolgharni M (2019) Esophageal abnormality detection using DenseNet based faster R-CNN with Gabor features. IEEE Access. 7:84374\u201384385","journal-title":"IEEE Access."},{"key":"8859_CR38","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition, Comput Vis Pattern Recognit (cs.CV),arXiv:1409.1556v6"},{"key":"8859_CR39","doi-asserted-by":"publisher","first-page":"178839","DOI":"10.1109\/ACCESS.2019.2958124","volume":"7","author":"T Yang","year":"2019","unstructured":"Yang T, Liang N, Li J, Yang Y, Li Y, Huang Q, Li R, He X, Zhang H (2019) Intelligent imaging technology in diagnosis of colorectal cancer using deep learning. IEEE Access. 7:178839\u2013178847","journal-title":"IEEE Access."},{"key":"8859_CR40","doi-asserted-by":"crossref","unstructured":"Li Q, Yang G, Chen Z, Bin H, Chen L, Xu D, Zhou X, Zhong S, Zhang H, Wang T (2018) Colorectal polyp segmentation using a fully convolutional neural network, In: 10th International Congress on Image and signal Processing , Biomedical Engineering and informatics (CISP-BMWI2017), Shanghai, China, 14\u201316","DOI":"10.1109\/CISP-BMEI.2017.8301980"},{"key":"8859_CR41","doi-asserted-by":"crossref","unstructured":"Zoph B, Vasudevan V, Shlens J, Le QV (2018) Learning transferable architectures for scalable image recognition, In: Computer vision and pattern recognition (cs.CV); Machine Learning (cs.LG); Machine Learning (stat.ML),arXiv:1707.07012v4","DOI":"10.1109\/CVPR.2018.00907"},{"key":"8859_CR42","unstructured":"Agrawal T, Gupta R, Sahu S, Espy-Wilson C, (2017) SCL-UMD at the medico task-mediaeval 2017: Transfer learning based classification of medical images, In: Medico challenge, MediaEval, Dublin, Ireland"},{"issue":"39\u201340","key":"8859_CR43","doi-asserted-by":"publisher","first-page":"28825","DOI":"10.1007\/s11042-020-09468-3","volume":"79","author":"\u015e \u00d6zt\u00fcrk","year":"2020","unstructured":"\u00d6zt\u00fcrk \u015e, \u00d6zkaya U (2020) Gastrointestinal tract classification using improved LSTM based CNN. Multimed Tools Appl. 79(39\u201340):28825\u201328840","journal-title":"Multimed Tools Appl."},{"key":"8859_CR44","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.jelekin.2018.07.005","volume":"42","author":"H Wu","year":"2018","unstructured":"Wu H, Huang Q, Wang D, Gao L (2018) A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals. J Electromyogr Kinesiol 42:136\u2013142","journal-title":"J Electromyogr Kinesiol"},{"key":"8859_CR45","doi-asserted-by":"crossref","unstructured":"Wu W, Li D, Du J, Gao X, Gu W, Zhao F, Feng X, Yan H (2020) An intelligent diagnosis method of brain MRI tumor segmentation using deep convolutional neural network and SVM Algorithm, Computational and mathematical methods in medicine","DOI":"10.1155\/2020\/6789306"},{"key":"8859_CR46","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.cogsys.2018.12.007","volume":"54","author":"M Talo","year":"2019","unstructured":"Talo M, Baloglu UB, Y\u0131ld\u0131r\u0131m \u00d6, Acharya UR (2019) Application of deep transfer learning for automated brain abnormality classification using MR images. Cognit Syst Res. 54:176\u2013188","journal-title":"Cognit Syst Res."},{"issue":"8","key":"8859_CR47","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1016\/j.compbiomed.2011.05.017","volume":"41","author":"WB Sampaio","year":"2011","unstructured":"Sampaio WB, Diniz EM, Silva AC, De Paiva AC, Gattass M (2011) Detection of masses in mammogram images using CNN, geostatistic functions and SVM. Comput Biol Med 41(8):653\u2013664","journal-title":"Comput Biol Med"},{"key":"8859_CR48","doi-asserted-by":"crossref","unstructured":"Okamoto T, Koide T, Yoshida Hiroshi Mieno S, Toishi Takayuki H, Sugawara Masayuki T, Odagawa Nobuo Tamba M, Tamaki T, Raytchev B, Kaneda K, Tanaka S (2019) Implementation of computer-aided diagnosis system on custamizable DSP core for colorectal endoscopic images with CNN features and SVM, In: Proceedings TENCON 2018\u20132018 IEEE Region 10 Conference, Jeju, Korea","DOI":"10.1109\/TENCON.2018.8650331"},{"issue":"5","key":"8859_CR49","first-page":"32","volume":"4","author":"LH Thai","year":"2012","unstructured":"Thai LH, Hai TS, Thuy NT (2012) Image classification using support vector machine and artificial neural network. Int J Inform Technol Comput Sci. 4(5):32\u201338","journal-title":"Int J Inform Technol Comput Sci."},{"key":"8859_CR50","doi-asserted-by":"publisher","first-page":"8055","DOI":"10.1109\/TIP.2020.3011269","volume":"29","author":"W Kim","year":"2020","unstructured":"Kim W, Kanezaki A, Tanaka M (2020) Unsupervised learning of image segmentation based on differentiable feature clustering. IEEE Trans Image Process 29:8055\u20138068","journal-title":"IEEE Trans Image Process"},{"key":"8859_CR51","doi-asserted-by":"crossref","unstructured":"Moriya T, Roth HR, Nakamura S, Oda H, Nagara K, Oda M, Mori K (2018) Unsupervised pathology image segmentation using representation learning with spherical k-means, Medical Imaging, Digital Pathology. 10581","DOI":"10.1117\/12.2292172"},{"key":"8859_CR52","doi-asserted-by":"crossref","unstructured":"Liu L, Kuang L, Ji Y (2020) Multimodal MRI brain tumor image segmentation using sparse subspace clustering algorithm, Computational and Mathematical Methods in Medicine, vol 2020, p 13","DOI":"10.1155\/2020\/8620403"},{"key":"8859_CR53","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1016\/j.procs.2015.06.090","volume":"54","author":"N Dhanachandra","year":"2015","unstructured":"Dhanachandra N, Manglem K, Chanu YJ (2015) Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Proced Comput Sci. 54:764\u2013771","journal-title":"Proced Comput Sci."},{"key":"8859_CR54","doi-asserted-by":"crossref","unstructured":"Liew WS, Tang TB, Lu CK, (2022) Computer-aided diagnostic tool for classification of colonic polyp assessment. In: International conference on artificial intelligence for smart community","DOI":"10.1007\/978-981-16-2183-3_71"},{"key":"8859_CR55","doi-asserted-by":"publisher","first-page":"844391","DOI":"10.3389\/fgene.2022.844391","volume":"13","author":"P Sharma","year":"2022","unstructured":"Sharma P, Balabantaray BK, Bora K, Mallik S, Kasugai K, Zhao Z (2022) An ensemble-based deep convolutional neural network for computer-aided polyps identification from colonoscopy. Front Genet 13:844391","journal-title":"Front Genet"},{"key":"8859_CR56","doi-asserted-by":"publisher","first-page":"103465","DOI":"10.1016\/j.bspc.2021.103465","volume":"73","author":"JS Nisha","year":"2022","unstructured":"Nisha JS, Gopi VP, Palanisamy P (2022) Automated colorectal polyp detection based on image enhancement and dual-path CNN architecture. Biomed Signal Process Control 73:103465","journal-title":"Biomed Signal Process Control"},{"issue":"7","key":"8859_CR57","doi-asserted-by":"publisher","first-page":"e423","DOI":"10.7717\/peerj-cs.423","volume":"10","author":"O Attallah","year":"2021","unstructured":"Attallah O, Sharkas M (2021) GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases. Peer J Comput Sci. 10(7):e423","journal-title":"Peer J Comput Sci."}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08859-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08859-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08859-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T00:17:22Z","timestamp":1693354642000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08859-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,22]]},"references-count":57,"journal-issue":{"issue":"28","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["8859"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08859-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,22]]},"assertion":[{"value":"1 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 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":"There are no conflicts of interest between authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}