{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T19:47:15Z","timestamp":1771703235439,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,5,2]],"date-time":"2023-05-02T00:00:00Z","timestamp":1682985600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,2]],"date-time":"2023-05-02T00:00:00Z","timestamp":1682985600000},"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":["J Digit Imaging"],"DOI":"10.1007\/s10278-023-00827-8","type":"journal-article","created":{"date-parts":[[2023,5,2]],"date-time":"2023-05-02T16:01:51Z","timestamp":1683043311000},"page":"1675-1686","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Automated Urine Cell Image Classification Model Using Chaotic Mixer Deep Feature Extraction"],"prefix":"10.1007","volume":"36","author":[{"given":"Mehmet","family":"Erten","sequence":"first","affiliation":[]},{"given":"Ilknur","family":"Tuncer","sequence":"additional","affiliation":[]},{"given":"Prabal D.","family":"Barua","sequence":"additional","affiliation":[]},{"given":"Kubra","family":"Yildirim","sequence":"additional","affiliation":[]},{"given":"Sengul","family":"Dogan","sequence":"additional","affiliation":[]},{"given":"Turker","family":"Tuncer","sequence":"additional","affiliation":[]},{"given":"Ru-San","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Hamido","family":"Fujita","sequence":"additional","affiliation":[]},{"given":"U. Rajendra","family":"Acharya","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,2]]},"reference":[{"key":"827_CR1","doi-asserted-by":"publisher","first-page":"15","DOI":"10.3343\/alm.2019.39.1.15","volume":"39","author":"M Oyaert","year":"2019","unstructured":"M. Oyaert, J. Delanghe, Progress in automated urinalysis, Annals of laboratory medicine, 39 (2019) 15-22.","journal-title":"Annals of laboratory medicine"},{"key":"827_CR2","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1053\/j.ajkd.2018.07.012","volume":"73","author":"C Cavanaugh","year":"2019","unstructured":"C. Cavanaugh, M.A. Perazella, Urine sediment examination in the diagnosis and management of kidney disease: core curriculum 2019, American Journal of Kidney Diseases, 73 (2019) 258-272.","journal-title":"American Journal of Kidney Diseases"},{"key":"827_CR3","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1053\/j.ajkd.2015.02.342","volume":"66","author":"MA Perazella","year":"2015","unstructured":"M.A. Perazella, The urine sediment as a biomarker of kidney disease, American journal of kidney diseases, 66 (2015) 748-755.","journal-title":"American journal of kidney diseases"},{"key":"827_CR4","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1080\/10408363.2020.1828811","volume":"58","author":"S De Bruyne","year":"2021","unstructured":"S. De Bruyne, M.M. Speeckaert, W. Van Biesen, J.R. Delanghe, Recent evolutions of machine learning applications in clinical laboratory medicine, Critical Reviews in Clinical Laboratory Sciences, 58 (2021) 131-152.","journal-title":"Critical Reviews in Clinical Laboratory Sciences"},{"key":"827_CR5","doi-asserted-by":"crossref","unstructured":"M. D'Alessandro, L. Poli, Q. Lai, A. Gaeta, C. Nazzari, M. Garofalo, F. Nudo, F. Della Pietra, A. Bachetoni, V. Sargentini, Automated Intelligent Microscopy for the Recognition of Decoy Cells in Urine Samples of Kidney Transplant Patients, Transplantation Proceedings, Elsevier, 2019, pp. 157\u2013159.","DOI":"10.1016\/j.transproceed.2018.05.030"},{"key":"827_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-018-1014-6","volume":"42","author":"Y Liang","year":"2018","unstructured":"Y. Liang, R. Kang, C. Lian, Y. Mao, An end-to-end system for automatic urinary particle recognition with convolutional neural network, Journal of medical systems, 42 (2018) 1-14.","journal-title":"Journal of medical systems"},{"key":"827_CR7","first-page":"661","volume":"38","author":"Y Liang","year":"2018","unstructured":"Y. Liang, Z. Tang, M. Yan, J. Liu, Object detection based on deep learning for urine sediment examination, Biocybernetics and Biomedical Engineering, 38 (2018) 661-670.","journal-title":"Engineering"},{"key":"827_CR8","doi-asserted-by":"crossref","unstructured":"M. Yan, Q. Liu, Z. Yin, D. Wang, Y. Liang, A Bidirectional Context Propagation Network for Urine Sediment Particle Detection in Microscopic Images, ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2020, pp. 981\u2013985.","DOI":"10.1109\/ICASSP40776.2020.9054367"},{"key":"827_CR9","doi-asserted-by":"publisher","first-page":"2937","DOI":"10.1002\/mp.14118","volume":"47","author":"Q Li","year":"2020","unstructured":"Q. Li, Z. Yu, T. Qi, L. Zheng, S. Qi, Z. He, S. Li, H. Guan, Inspection of visible components in urine based on deep learning, Medical Physics, 47 (2020) 2937-2949.","journal-title":"Medical Physics"},{"key":"827_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-019-1457-4","volume":"43","author":"X Zhang","year":"2019","unstructured":"X. Zhang, L. Jiang, D. Yang, J. Yan, X. Lu, Urine sediment recognition method based on multi-view deep residual learning in microscopic image, Journal of medical systems, 43 (2019) 1-10.","journal-title":"Journal of medical systems"},{"key":"827_CR11","doi-asserted-by":"crossref","unstructured":"J. Pan, C. Jiang, T. Zhu, Classification of urine sediment based on convolution neural network, AIP Conference Proceedings, AIP Publishing LLC, 2018, pp. 040176.","DOI":"10.1063\/1.5033840"},{"key":"827_CR12","first-page":"109","volume":"8","author":"T Li","year":"2020","unstructured":"T. Li, D. Jin, C. Du, X. Cao, H. Chen, J. Yan, N. Chen, Z. Chen, Z. Feng, S. Liu, The image-based analysis and classification of urine sediments using a LeNet-5 neural network, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 8 (2020) 109-114.","journal-title":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization"},{"key":"827_CR13","doi-asserted-by":"crossref","unstructured":"N. O\u2019Mahony, S. Campbell, A. Carvalho, S. Harapanahalli, G.V. Hernandez, L. Krpalkova, D. Riordan, J. Walsh, Deep learning vs. traditional computer vision, Science and information conference, Springer, 2019, pp. 128\u2013144.","DOI":"10.1007\/978-3-030-17795-9_10"},{"key":"827_CR14","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1109\/MCE.2016.2640698","volume":"6","author":"J Lemley","year":"2017","unstructured":"J. Lemley, S. Bazrafkan, P. Corcoran, Deep Learning for Consumer Devices and Services: Pushing the limits for machine learning, artificial intelligence, and computer vision, IEEE Consumer Electronics Magazine, 6 (2017) 48-56.","journal-title":"IEEE Consumer Electronics Magazine"},{"key":"827_CR15","unstructured":"I. Zafar, G. Tzanidou, R. Burton, N. Patel, L. Araujo, Hands-on convolutional neural networks with TensorFlow: Solve computer vision problems with modeling in TensorFlow and Python, Packt Publishing Ltd, 2018."},{"key":"827_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103638","volume":"113","author":"\u015e \u00d6zt\u00fcrk","year":"2021","unstructured":"\u015e. \u00d6zt\u00fcrk, U. \u00d6zkaya, Residual LSTM layered CNN for classification of gastrointestinal tract diseases, Journal of Biomedical Informatics, 113 (2021) 103638.","journal-title":"Journal of Biomedical Informatics"},{"key":"827_CR17","doi-asserted-by":"crossref","unstructured":"P. Carcagn\u00ec, M. Leo, G. Celeste, C. Distante, A. Cuna, A systematic investigation on deep architectures for automatic skin lesions classification, 2020 25th International Conference on Pattern Recognition (ICPR), IEEE, 2021, pp. 8639\u20138646.","DOI":"10.1109\/ICPR48806.2021.9412789"},{"key":"827_CR18","unstructured":"A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, An image is worth 16x16 words: Transformers for image recognition at scale, arXiv preprint arXiv:2010.11929, (2020)."},{"key":"827_CR19","unstructured":"I.O. Tolstikhin, N. Houlsby, A. Kolesnikov, L. Beyer, X. Zhai, T. Unterthiner, J. Yung, A. Steiner, D. Keysers, J. Uszkoreit, Mlp-mixer: An all-mlp architecture for vision, Advances in Neural Information Processing Systems, 34 (2021)."},{"key":"827_CR20","doi-asserted-by":"crossref","unstructured":"Z. Liu, J. Ning, Y. Cao, Y. Wei, Z. Zhang, S. Lin, H. Hu, Video swin transformer, Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 3202\u20133211.","DOI":"10.1109\/CVPR52688.2022.00320"},{"key":"827_CR21","unstructured":"A. Trockman, J.Z. Kolter, Patches are all you need?, arXiv preprint arXiv:2201.09792, (2022)."},{"key":"827_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2022.102274","volume":"127","author":"M Baygin","year":"2022","unstructured":"M. Baygin, O. Yaman, P.D. Barua, S. Dogan, T. Tuncer, U.R. Acharya, Exemplar Darknet19 feature generation technique for automated kidney stone detection with coronal CT images, Artificial Intelligence in Medicine, 127 (2022) 102274.","journal-title":"Artificial Intelligence in Medicine"},{"key":"827_CR23","doi-asserted-by":"crossref","unstructured":"Z. Tu, H. Talebi, H. Zhang, F. Yang, P. Milanfar, A. Bovik, Y. Li, Maxim: Multi-axis mlp for image processing, Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 5769\u20135780.","DOI":"10.1109\/CVPR52688.2022.00568"},{"key":"827_CR24","unstructured":"V.I. Arnold, A. Avez, Ergodic problems of classical mechanics, Benjamin, 1968."},{"key":"827_CR25","doi-asserted-by":"publisher","first-page":"1365","DOI":"10.1007\/s11071-012-0539-3","volume":"70","author":"J Bao","year":"2012","unstructured":"J. Bao, Q. Yang, Period of the discrete Arnold cat map and general cat map, Nonlinear Dynamics, 70 (2012) 1365-1375.","journal-title":"Nonlinear Dynamics"},{"key":"827_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109792","volume":"258","author":"H Zhang","year":"2022","unstructured":"H. Zhang, Z. Dong, B. Li, S. He, Multi-Scale MLP-Mixer for image classification, Knowledge-Based Systems, 258 (2022) 109792.","journal-title":"Knowledge-Based Systems"},{"key":"827_CR27","doi-asserted-by":"crossref","unstructured":"Z. Zhou, M.T. Islam, L. Xing, Multibranch CNN With MLP-Mixer-Based Feature Exploration for High-Performance Disease Diagnosis, IEEE Transactions on Neural Networks and Learning Systems, (2023).","DOI":"10.1109\/TNNLS.2023.3250490"},{"key":"827_CR28","doi-asserted-by":"crossref","unstructured":"G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, Densely connected convolutional networks, Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"key":"827_CR29","first-page":"248","volume":"2009","author":"J Deng","year":"2009","unstructured":"J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei, Imagenet: A large-scale hierarchical image database, 2009 IEEE conference on computer vision and pattern recognition, Ieee, 2009, pp. 248-255.","journal-title":"Ieee"},{"key":"827_CR30","doi-asserted-by":"publisher","first-page":"84532","DOI":"10.1109\/ACCESS.2020.2992641","volume":"8","author":"T Tuncer","year":"2020","unstructured":"T. Tuncer, S. Dogan, F. \u00d6zyurt, S.B. Belhaouari, H. Bensmail, Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice, IEEE Access, 8 (2020) 84532-84540.","journal-title":"IEEE Access"},{"key":"827_CR31","doi-asserted-by":"publisher","first-page":"1883","DOI":"10.4249\/scholarpedia.1883","volume":"4","author":"LE Peterson","year":"2009","unstructured":"L.E. Peterson, K-nearest neighbor, Scholarpedia, 4 (2009) 1883.","journal-title":"Scholarpedia"},{"key":"827_CR32","doi-asserted-by":"crossref","unstructured":"H. Tora, E. Gokcay, M. Turan, M. Buker, A generalized Arnold\u2019s Cat Map transformation for image scrambling, Multimedia Tools and Applications, (2022) 1\u201314.","DOI":"10.1007\/s11042-022-11985-2"},{"key":"827_CR33","first-page":"513","volume":"17","author":"J Goldberger","year":"2004","unstructured":"J. Goldberger, G.E. Hinton, S. Roweis, R.R. Salakhutdinov, Neighbourhood components analysis, Advances in neural information processing systems, 17 (2004) 513-520.","journal-title":"Advances in neural information processing systems"},{"key":"827_CR34","doi-asserted-by":"crossref","unstructured":"H.W. Loh, C.P. Ooi, S. Seoni, P.D. Barua, F. Molinari, U.R. Acharya, Application of Explainable Artificial Intelligence for Healthcare: A Systematic Review of the Last Decade (2011\u20132022), Computer Methods and Programs in Biomedicine, (2022) 107161.","DOI":"10.1016\/j.cmpb.2022.107161"},{"key":"827_CR35","doi-asserted-by":"crossref","unstructured":"R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-cam: Visual explanations from deep networks via gradient-based localization, Proceedings of the IEEE international conference on computer vision, 2017, pp. 618\u2013626.","DOI":"10.1109\/ICCV.2017.74"},{"key":"827_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105550","volume":"146","author":"V Jahmunah","year":"2022","unstructured":"V. Jahmunah, E.Y.K. Ng, R.-S. Tan, S.L. Oh, U.R. Acharya, Explainable detection of myocardial infarction using deep learning models with Grad-CAM technique on ECG signals, Computers in Biology and Medicine, 146 (2022) 105550.","journal-title":"Computers in Biology and Medicine"},{"key":"827_CR37","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"827_CR38","doi-asserted-by":"crossref","unstructured":"M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L.-C. Chen, Mobilenetv2: Inverted residuals and linear bottlenecks, Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 4510\u20134520.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"827_CR39","doi-asserted-by":"crossref","unstructured":"J. Redmon, A. Farhadi, YOLO9000: better, faster, stronger, Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 7263\u20137271.","DOI":"10.1109\/CVPR.2017.690"},{"key":"827_CR40","doi-asserted-by":"crossref","unstructured":"F. Chollet, Xception: Deep learning with depthwise separable convolutions, Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1251\u20131258.","DOI":"10.1109\/CVPR.2017.195"},{"key":"827_CR41","unstructured":"M. Tan, Q. Le, Efficientnet: Rethinking model scaling for convolutional neural networks, International Conference on Machine Learning, PMLR, 2019, pp. 6105\u20136114."},{"key":"827_CR42","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1093\/mnras\/225.1.155","volume":"225","author":"G Fasano","year":"1987","unstructured":"G. Fasano, A. Franceschini, A multidimensional version of the Kolmogorov\u2013Smirnov test, Monthly Notices of the Royal Astronomical Society, 225 (1987) 155-170.","journal-title":"Monthly Notices of the Royal Astronomical Society"},{"key":"827_CR43","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1175\/MWR3326.1","volume":"135","author":"DJ Steinskog","year":"2007","unstructured":"D.J. Steinskog, D.B. Tj\u00f8stheim, N.G. Kvamst\u00f8, A cautionary note on the use of the Kolmogorov\u2013Smirnov test for normality, Monthly Weather Review, 135 (2007) 1151-1157.","journal-title":"Monthly Weather Review"},{"key":"827_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00145-021-09414-y","volume":"35","author":"M S\u00fds","year":"2022","unstructured":"M. S\u00fds, L. Obr\u00e1til, V. Maty\u00e1\u0161, D. Klinec, A Bad Day to Die Hard: Correcting the Dieharder Battery, Journal of Cryptology, 35 (2022) 1-20.","journal-title":"Journal of Cryptology"},{"key":"827_CR45","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1111\/bju.15518","volume":"130","author":"M Kaneko","year":"2022","unstructured":"M. Kaneko, K. Tsuji, K. Masuda, K. Ueno, K. Henmi, S. Nakagawa, R. Fujita, K. Suzuki, Y. Inoue, S. Teramukai, Urine cell image recognition using a deep\u2010learning model for an automated slide evaluation system, BJU international, 130 (2022) 235-243.","journal-title":"BJU international"},{"key":"827_CR46","doi-asserted-by":"crossref","unstructured":"X. Zhao, J. Xiang, Q. Ji, Urine red blood cell classification based on Siamese Network, Journal of Physics: Conference Series, IOP Publishing, 2021, pp. 012089.","DOI":"10.1088\/1742-6596\/1873\/1\/012089"},{"key":"827_CR47","unstructured":"E. Fernandez, M. Barlis, K. Dematera, G. LLas, R. Paeste, D. Taveso, J. Velasco, Four-class urine microscopic recognition system through image processing using artificial neural network, J. Telecommun. Electron. Comput. Eng.(JTEC), (2018) 214\u2013218."},{"key":"827_CR48","doi-asserted-by":"publisher","first-page":"2114","DOI":"10.1049\/ipr2.12476","volume":"16","author":"X Li","year":"2022","unstructured":"X. Li, M. Li, Y. Wu, X. Zhou, L. Zhang, X. Ping, X. Zhang, W. Zheng, Multi\u2010instance inflated 3D CNN for classifying urine red blood cells from multi\u2010focus videos, IET Image Processing, 16 (2022) 2114-2123.","journal-title":"IET Image Processing"},{"key":"827_CR49","doi-asserted-by":"crossref","unstructured":"E.O. Fernandez, M. Nilo, J.O. Aquino, J.M.P. Bravo, S. Julie-Anne, C.V.B. Gaddi, C.A. Simbran, Microcontroller-based automated microscope for image recognition of four urine constituents, TENCON 2018\u20132018 IEEE Region 10 Conference, IEEE, 2018, pp. 1689\u20131694.","DOI":"10.1109\/TENCON.2018.8650102"},{"key":"827_CR50","doi-asserted-by":"publisher","first-page":"4176","DOI":"10.3390\/electronics11244176","volume":"11","author":"F Hao","year":"2022","unstructured":"F. Hao, X. Li, M. Li, Y. Wu, W. Zheng, An Accurate Urine Red Blood Cell Detection Method Based on Multi-Focus Video Fusion and Deep Learning with Application to Diabetic Nephropathy Diagnosis, Electronics, 11 (2022) 4176.","journal-title":"Electronics"},{"key":"827_CR51","first-page":"1706","volume":"12","author":"A Africa","year":"2017","unstructured":"A. Africa, J. Velasco, Development of a urine strip analyzer using artificial neural network using an android phone, ARPN Journal of Engineering and Applied Sciences, 12 (2017) 1706-1712.","journal-title":"ARPN Journal of Engineering and Applied Sciences"},{"key":"827_CR52","unstructured":"J.S. Velasco, M.K. Cabatuan, E.P. Dadios, Urine sediment classification using deep learning, Lecture Notes on Advanced Research in Electrical and Electronic Engineering Technology, (2019) 180\u2013185."}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-023-00827-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-023-00827-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-023-00827-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T17:13:51Z","timestamp":1691428431000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-023-00827-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,2]]},"references-count":52,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["827"],"URL":"https:\/\/doi.org\/10.1007\/s10278-023-00827-8","relation":{},"ISSN":["1618-727X"],"issn-type":[{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,2]]},"assertion":[{"value":"9 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 March 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2023","order":4,"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":"Ethical Approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}