{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T22:14:15Z","timestamp":1776204855048,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T00:00:00Z","timestamp":1715040000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T00:00:00Z","timestamp":1715040000000},"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":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s11517-024-03076-1","type":"journal-article","created":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T08:01:57Z","timestamp":1715068917000},"page":"2911-2938","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DEC-DRR: deep ensemble of classification model for diabetic retinopathy recognition"],"prefix":"10.1007","volume":"62","author":[{"given":"L.B.","family":"Lisha","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C.","family":"Helen Sulochana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,7]]},"reference":[{"key":"3076_CR1","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1007\/s10278-021-00418-5","volume":"34","author":"C Bhardwaj","year":"2021","unstructured":"Bhardwaj C, Jain S, Sood M (2021) Deep learning\u2013based diabetic retinopathy severity grading system employing quadrant ensemble model. J Digit Imaging 34:440\u2013457","journal-title":"J Digit Imaging"},{"key":"3076_CR2","doi-asserted-by":"publisher","first-page":"13999","DOI":"10.1007\/s00521-021-06042-2","volume":"33","author":"C Bhardwaj","year":"2021","unstructured":"Bhardwaj C, Jain S, Sood M (2021) Transfer learning based robust automatic detection system for diabetic retinopathy grading. Neural Comput & Applic 33:13999\u201314019","journal-title":"Neural Comput & Applic"},{"key":"3076_CR3","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1134\/S1054661819030180","volume":"29","author":"C Raja","year":"2019","unstructured":"Raja C, Balaji L (2019) An automatic detection of blood vessel in retinal images using convolution neural network for diabetic retinopathy detection. Pattern Recogn Image Anal 29:533\u2013545","journal-title":"Pattern Recogn Image Anal"},{"key":"3076_CR4","first-page":"473","volume":"12","author":"S Chakraborty","year":"2020","unstructured":"Chakraborty S (2020) Gopal Chandra Jana, Divya Kumari & Aleena Swetapadma, \"An improved method using supervised learning technique for diabetic retinopathy detection\". Int J Inf Technol 12:473\u2013477","journal-title":"Int J Inf Technol"},{"key":"3076_CR5","doi-asserted-by":"publisher","first-page":"9825","DOI":"10.1007\/s12652-020-02727-z","volume":"12","author":"JD Bodapati","year":"2021","unstructured":"Bodapati JD (2021) Nagur Shareef Shaik & Veeranjaneyulu Naralasetti, \"Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification\". J Ambient Intell Humaniz Comput 12:9825\u20139839","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"3076_CR6","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1007\/s42600-021-00177-w","volume":"37","author":"A Dutta","year":"2021","unstructured":"Dutta A, Agarwal P, Mittal A, Khandelwal S (2021) Detecting grades of diabetic retinopathy by extraction of retinal lesions using digital fundus images. Res Biomed Eng 37:641\u2013656","journal-title":"Res Biomed Eng"},{"key":"3076_CR7","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1007\/s00779-020-01519-8","volume":"27","author":"MH Mahmoud","year":"2021","unstructured":"Mahmoud MH, Salman Alamery H, Fouad AA, Youssef AE (2021) An automatic detection system of diabetic retinopathy using a hybrid inductive machine learning algorithm. Pers Ubiquit Comput 27:751\u2013765","journal-title":"Pers Ubiquit Comput"},{"key":"3076_CR8","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1007\/s11760-020-01798-x","volume":"15","author":"R Alaguselvi","year":"2021","unstructured":"Alaguselvi R, Murugan K (2021) Performance analysis of automated lesion detection of diabetic retinopathy using morphological operation. SIViP 15:797\u2013805","journal-title":"SIViP"},{"key":"3076_CR9","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.neucom.2019.08.079","volume":"3695","author":"X Li","year":"2019","unstructured":"Li X, Shen L, Shen M, Tan F, Qiu CS (2019) Deep learning based early stage diabetic retinopathy detection using optical coherence tomography. Neurocomputing 3695:134\u2013144","journal-title":"Neurocomputing"},{"key":"3076_CR10","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.compbiomed.2017.07.007","volume":"88","author":"E Manuel","year":"2017","unstructured":"Manuel E, Gegundez-Arias DM, Ponte B, Alvarez F, Bravo JM (2017) A tool for automated diabetic retinopathy pre-screening based on retinal image computer analysis. Comput Biol Med 88:100\u2013109","journal-title":"Comput Biol Med"},{"key":"3076_CR11","doi-asserted-by":"publisher","first-page":"103537","DOI":"10.1016\/j.compbiomed.2019.103537","volume":"116","author":"GT Zago","year":"2020","unstructured":"Zago GT, Andre\u00e3o RV, Dorizzi B, Salles EOT (January 2020) Diabetic retinopathy detection using red lesion localization and convolutional neural networks. Comput Biol Med 116:103537","journal-title":"Comput Biol Med"},{"key":"3076_CR12","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/j.ajo.2016.06.037","volume":"169","author":"TC Gr\u00e4sbeck","year":"2016","unstructured":"Gr\u00e4sbeck TC, Gr\u00e4sbeck SV, Miettinen PJ, Summanen PA (2016) Fundus photography as a screening method for diabetic retinopathy in children with type 1 diabetes: outcome of the initial photography. Am J Ophthalmol 169:227\u2013234","journal-title":"Am J Ophthalmol"},{"key":"3076_CR13","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.compmedimag.2016.08.005","volume":"55","author":"S Garima Gupta","year":"2017","unstructured":"Garima Gupta S (2017) Kulasekaran, Keerthi Ram, Niranjan Joshi, Rashmin Gandhi, \u201cLocal characterization of neovascularization and identification of proliferative diabetic retinopathy in retinal fundus images\u201d. Comput Med Imaging Graph 55:124\u2013132","journal-title":"Comput Med Imaging Graph"},{"key":"3076_CR14","doi-asserted-by":"publisher","first-page":"105815","DOI":"10.1016\/j.optlastec.2019.105815","volume":"121","author":"S Kumar","year":"2020","unstructured":"Kumar S, Adarsh A, Kumar B, Singh AK (January 2020) An automated early diabetic retinopathy detection through improved blood vessel and optic disc segmentation. Optics & Laser Technol 121:105815","journal-title":"Optics & Laser Technol"},{"key":"3076_CR15","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.compeleceng.2019.03.004","volume":"76","author":"T Shanthi","year":"2019","unstructured":"Shanthi T, Sabeenian RS (2019) Modified Alexnet architecture for classification of diabetic retinopathy images. Comput Electr Eng 76:56\u201364","journal-title":"Comput Electr Eng"},{"key":"3076_CR16","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1016\/j.compeleceng.2018.07.042","volume":"72","author":"S Wan","year":"2018","unstructured":"Wan S, Liang Y, Zhang Y (2018) Deep convolutional neural networks for diabetic retinopathy detection by image classification. Comput Electr Eng 72:274\u2013282","journal-title":"Comput Electr Eng"},{"key":"3076_CR17","doi-asserted-by":"crossref","unstructured":"Pinazo-Dur\u00e1n MD, Shoaie-Nia K, Sanz-Gonz\u00e1lez SM, Raga-Cervera J  Identification of new candidate genes for retinopathy in type 2 diabetics. Valencia Study on diabetic retinopathy (VSDR). Report number 3. Arch Soc Espa\u00f1ola de Oftalmol 93(5):211\u2013219","DOI":"10.1016\/j.oftale.2018.03.001"},{"key":"3076_CR18","doi-asserted-by":"publisher","first-page":"104292","DOI":"10.1109\/ACCESS.2020.2993937","volume":"8","author":"L Qiao","year":"2020","unstructured":"Qiao L, Zhu Y, Zhou H (2020) Diabetic retinopathy detection using prognosis of microaneurysm and early diagnosis system for non-proliferative diabetic retinopathy based on deep learning algorithms. IEEE Access 8:104292\u2013104302. https:\/\/doi.org\/10.1109\/ACCESS.2020.2993937","journal-title":"IEEE Access"},{"key":"3076_CR19","doi-asserted-by":"crossref","unstructured":"Peter L. Nesper, Brian T. Soetikno, Hao F. Zhang, Amani A. Fawzi, \u201cOCT angiography and visible-light OCT in diabetic retinopathy\u201d, Vis Res, vol. 139, pp. 191-203, October 2017.","DOI":"10.1016\/j.visres.2017.05.006"},{"key":"3076_CR20","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.diabres.2018.01.023","volume":"138","author":"Bruna Let\u00edcia da Silva Pereira","year":"2018","unstructured":"Bruna Let\u00edcia da Silva Pereira (2018) Evelise Regina Polina, Daisy Crispim, Renan Cesar Sbruzzi, K\u00e1tia Gon\u00e7alves dos Santos, \u201cInterleukin-10 \u22121082A\u2009>\u2009G (rs1800896) polymorphism is associated with diabetic retinopathy in type 2 diabetes\u201d. Diabetes Res Clin Pract 138:187\u2013192","journal-title":"Diabetes Res Clin Pract"},{"key":"3076_CR21","doi-asserted-by":"publisher","unstructured":"Xiao Z, Xing H, Qu R, Feng L, Luo S, Zhao B, Dai Y (2024) Densely knowledge-aware network for multivariate time series classification. IEEE Trans Syst Man Cybern Syst. https:\/\/doi.org\/10.1109\/TSMC.2023.3342640","DOI":"10.1109\/TSMC.2023.3342640"},{"issue":"1","key":"3076_CR22","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1109\/TETCI.2023.3304948","volume":"8","author":"Z Xiao","year":"2024","unstructured":"Xiao Z (2024) Deep contrastive representation learning with self-distillation. IEEE Transactions on Emerging Topics in Computational Intelligence 8(1):3\u201315. https:\/\/doi.org\/10.1109\/TETCI.2023.3304948","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"key":"3076_CR23","doi-asserted-by":"publisher","unstructured":"Xiao Z, Tong H, Qu R, Xing H, Luo S, Zhu Z (2023) CapMatch: Semi-supervised contrastive transformer capsule with feature-based knowledge distillation for human activity recognition. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2023.3344294","DOI":"10.1109\/TNNLS.2023.3344294"},{"key":"3076_CR24","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1016\/j.compbiomed.2017.10.030","volume":"91","author":"M Adam","year":"2017","unstructured":"Adam M, Ng EYK, Tan JH, Heng ML, Acharya UR (2017) Computer-aided diagnosis of diabetic foot using infrared thermography: a review. Comput Biol Med 91:326\u2013336","journal-title":"Comput Biol Med"},{"key":"3076_CR25","doi-asserted-by":"publisher","first-page":"22844","DOI":"10.1109\/ACCESS.2021.3054743","volume":"9","author":"MM Abdelsalam","year":"2021","unstructured":"Abdelsalam MM, Zahran MA (2021) A novel approach of diabetic retinopathy early detection based on multifractal geometry analysis for OCTA macular images using support vector machine. IEEE Access 9:22844\u201322858. https:\/\/doi.org\/10.1109\/ACCESS.2021.3054743","journal-title":"IEEE Access"},{"issue":"Supplement 1","key":"3076_CR26","doi-asserted-by":"publisher","first-page":"s451","DOI":"10.1016\/j.dsx.2017.03.034","volume":"11","author":"M Nalini","year":"2017","unstructured":"Nalini M, Raghavulu BV, Annapurna A, Avinash P, Wasim (2017) Correlation of various serum biomarkers with the severity of diabetic retinopathy. Diabetes Metab Syndr Clin Res Rev 11(Supplement 1):s451\u2013s454","journal-title":"Diabetes Metab Syndr Clin Res Rev"},{"key":"3076_CR27","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.cyto.2017.10.014","volume":"106","author":"A Blum","year":"2018","unstructured":"Blum A, Pastukh N, Socea D, Jabaly H (2018) Levels of adhesion molecules in peripheral blood correlate with stages of diabetic retinopathy and may serve as biomarkers for microvascular complications. Cytokine 106:76\u201379","journal-title":"Cytokine"},{"key":"3076_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cct.2016.11.007","volume":"53","author":"J Desai","year":"2017","unstructured":"Desai J, Taylor G, Vazquez-Benitez G, Vine S, O'Connor PJ (2017) Financial incentives for diabetes prevention in a Medicaid population: study design and baseline characteristics. Contemp Clin Trials 53:1\u201310","journal-title":"Contemp Clin Trials"},{"issue":"7","key":"3076_CR29","doi-asserted-by":"publisher","first-page":"2686","DOI":"10.1109\/JBHI.2020.3041848","volume":"25","author":"C-H Hua","year":"2021","unstructured":"Hua C-H, Kim K, Huynh T, In You J, Seung-Young Y, Le-Tien T, Bae S-H, Lee S (2021) Convolutional network with twofold feature augmentation for diabetic retinopathy recognition from multi-modal in. IEEE J Biomed Health Inform 25(7):2686\u20132697. https:\/\/doi.org\/10.1109\/JBHI.2020.3041848","journal-title":"IEEE J Biomed Health Inform"},{"issue":"2","key":"3076_CR30","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1109\/TASE.2020.2981637","volume":"18","author":"S Wang","year":"2021","unstructured":"Wang S, Wang X, Hu Y, Shen Y (2021) Diabetic retinopathy diagnosis using multichannel generative adversarial network with semisupervision. IEEE Trans Autom Sci Eng 18(2):574\u2013585. https:\/\/doi.org\/10.1109\/TASE.2020.2981637","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"3076_CR31","doi-asserted-by":"publisher","first-page":"86115","DOI":"10.1109\/ACCESS.2019.2918625","volume":"7","author":"Y Sun","year":"2019","unstructured":"Sun Y, Zhang D (2019) Diagnosis and analysis of diabetic retinopathy based on electronic health records. IEEE Access 7:86115\u201386120. https:\/\/doi.org\/10.1109\/ACCESS.2019.2918625","journal-title":"IEEE Access"},{"key":"3076_CR32","doi-asserted-by":"publisher","first-page":"114862","DOI":"10.1109\/ACCESS.2019.2935912","volume":"7","author":"A Imran","year":"2019","unstructured":"Imran A, Li J, Pei Y, Yang J-J, Wang Q (2019) Comparative analysis of vessel segmentation techniques in retinal images. IEEE Access 7:114862\u2013114887. https:\/\/doi.org\/10.1109\/ACCESS.2019.2935912","journal-title":"IEEE Access"},{"key":"3076_CR33","doi-asserted-by":"publisher","first-page":"101267","DOI":"10.1109\/ACCESS.2021.3094649","volume":"9","author":"O Bernab\u00e9","year":"2021","unstructured":"Bernab\u00e9 O, Acevedo E, Acevedo A, Carre\u00f1o R, G\u00f3mez S (2021) \"Classification of eye diseases in fundus images,\" IEEE. Access 9:101267\u2013101276. https:\/\/doi.org\/10.1109\/ACCESS.2021.3094649","journal-title":"Access"},{"key":"3076_CR34","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.knosys.2019.03.016","volume":"175","author":"W Zhang","year":"2019","unstructured":"Zhang W, Zhong J, Yang S, Gao Z, Hu J, Chen Y, Yi Z (2019) Automated identification and grading system of diabetic retinopathy using deep neural networks. Knowl-Based Syst 175:12\u201325","journal-title":"Knowl-Based Syst"},{"key":"3076_CR35","first-page":"1","volume":"2021","author":"M Bader Alazzam","year":"2021","unstructured":"Bader Alazzam M, Alassery F, Almulihi A (2021) Identification of diabetic retinopathy through machine learning. Mob Inf Syst 2021:1\u20138","journal-title":"Mob Inf Syst"},{"key":"3076_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/8864698","volume":"2020","author":"G Jinfeng","year":"2020","unstructured":"Jinfeng G, Qummar S, Junming Z, Ruxian Y, Khan FG (2020) Ensemble framework of deep CNNs for diabetic retinopathy detection. Comput Intell Neurosci 2020:1\u201311","journal-title":"Comput Intell Neurosci"},{"key":"3076_CR37","doi-asserted-by":"publisher","first-page":"925901","DOI":"10.3389\/fpubh.2022.925901","volume":"10","author":"MAA Albadr","year":"2022","unstructured":"Albadr MAA, Ayob M, Tiun S, Al-Dhief FT, Hasan MK (2022) Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection. Front Public Health 10:925901","journal-title":"Front Public Health"},{"issue":"5","key":"3076_CR38","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.3390\/diagnostics13051001","volume":"13","author":"S Sundaram","year":"2023","unstructured":"Sundaram S, Selvamani M, Raju SK, Ramaswamy S, Islam S, Cha JH et al (2023) Diabetic retinopathy and diabetic macular edema detection using ensemble based convolutional neural networks. Diagnostics 13(5):1001","journal-title":"Diagnostics"},{"key":"3076_CR39","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1016\/j.cam.2017.12.026","volume":"336","author":"F Li","year":"2018","unstructured":"Li F, Lv X-G, Deng Z (2018) Regularized iterative Weiner filter method for blind image deconvolution. J Comput Appl Math 336:425\u2013438","journal-title":"J Comput Appl Math"},{"key":"3076_CR40","doi-asserted-by":"publisher","unstructured":"Deng Q, Peirong L, Zhao S, Yuan N (2022) U-Net: A deeplearning method for improving summer precipitation forecasts in China. Atmos Ocean Sci Lett. https:\/\/doi.org\/10.1016\/j.aosl.2022.100322","DOI":"10.1016\/j.aosl.2022.100322"},{"key":"3076_CR41","first-page":"100038","volume":"5","author":"R Shinde","year":"2021","unstructured":"Shinde R (2021) Glaucoma detection in retinal fundus images using U-Net and supervised machine learning algorithms. Intell-Based Med 5:100038","journal-title":"Intell-Based Med"},{"key":"3076_CR42","doi-asserted-by":"publisher","unstructured":"Tong L, Ma H, Lin Q, He J, Peng L  A novel deep learning Bi-GRU-I model for real-time human activity recognition using inertial sensors. IEEE Sens J. https:\/\/doi.org\/10.1109\/JSEN.2022.3148431","DOI":"10.1109\/JSEN.2022.3148431"},{"key":"3076_CR43","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","volume":"77","author":"G Jiuxiang","year":"2018","unstructured":"Jiuxiang G, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J, Chen T (2018) Recent advances in convolutional neural networks. Pattern Recogn 77:354\u2013377","journal-title":"Pattern Recogn"},{"key":"3076_CR44","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1109\/ASRU.2013.6707745","volume-title":"IEEE Workshop on Automatic Speech Recognition and Understanding, 2013","author":"M Cai","year":"2013","unstructured":"Cai M, Shi Y, Liu J (2013) Deep maxout neural networks for speech recognition. In: IEEE Workshop on Automatic Speech Recognition and Understanding, 2013, pp 291\u2013296. https:\/\/doi.org\/10.1109\/ASRU.2013.6707745"},{"issue":"3","key":"3076_CR45","doi-asserted-by":"publisher","first-page":"855","DOI":"10.3390\/s22030855","volume":"22","author":"P Trojovsk\u00fd","year":"2022","unstructured":"Trojovsk\u00fd P, Dehghani M (2022) Pelican optimization algorithm: a novel nature-inspired algorithm for engineering applications. Sensors 22(3):855","journal-title":"Sensors"},{"key":"3076_CR46","doi-asserted-by":"publisher","first-page":"19599","DOI":"10.1109\/ACCESS.2022.3151641","volume":"10","author":"M Dehghani","year":"2022","unstructured":"Dehghani M, Hub\u00e1lovsk\u00fd \u0160, Trojovsk\u00fd P (2022) Tasmanian devil optimization: a new bio-inspired optimization algorithm for solving optimization algorithm. IEEE Access 10:19599\u201319620. https:\/\/doi.org\/10.1109\/ACCESS.2022.3151641","journal-title":"IEEE Access"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03076-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-024-03076-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03076-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T21:06:28Z","timestamp":1723928788000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-024-03076-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,7]]},"references-count":46,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["3076"],"URL":"https:\/\/doi.org\/10.1007\/s11517-024-03076-1","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,7]]},"assertion":[{"value":"4 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 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":"Not applicable","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}