{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:19:01Z","timestamp":1791523141294,"version":"4.3.4"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T00:00:00Z","timestamp":1744675200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T00:00:00Z","timestamp":1744675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100009367","name":"Mansoura University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100009367","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>There are an estimated 1.3 million cases of cancer globally each year, making it one of the most serious types of urinary tract cancer. The methods used today for diagnosing and monitoring bladder cancer are intrusive, costly, and time-consuming. In clinical practice, invasive biopsy followed by histological examination continues to be the gold standard for diagnosis. Bladder cancer biomarkers have been used in expensive diagnostic tests created recently, however their reliability is limited by their high rates of false positives and false negatives. The potential and use of artificial intelligence in urological diseases have been the subject of several research, as interest in artificial intelligence in medicine has grown recently. In this paper, a new bladder cancer model called Ensemble Deep Learning (EDL) will be provided to accurately diagnose patients. Outlier rejection is used to filter data using the interquartile range (IQR) then the image diagnosis. The proposed EDL consists of three deep learning algorithms, which are; Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), and a new deep learning method called Explainable Deep Learning (XDL) that depends on Guided Gradient Weighted Class Activation Map (Guided Grad-CAM). In fact, Guided Grad-CAM enables doctor to understand the diagnose. A new voting mechanism will be used to integrate the results of all three methods to produce the final result to accurately diagnose bladder cancer cases. In fact, the used voting method depends on using majority voting based on two different scenarios according to the results of CNN, GAN, and XDL. If these three methods give the same class category, then the final diagnosis will be this class category. On the other hand, if the three methods give different class category, then the final result will be followed by the accuracy of each class. The proposed EDL model was tested after several trials. The results have proved that EDL model is more efficient and more accurate to diagnose bladder cancer disease. It introduced the highest accuracy results and the lowest error results as well as execution time.<\/jats:p>","DOI":"10.1038\/s41598-025-95002-0","type":"journal-article","created":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T02:01:42Z","timestamp":1744682502000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Accurate bladder cancer diagnosis using ensemble deep leaning"],"prefix":"10.1038","volume":"15","author":[{"given":"Rana A.","family":"El-Atier","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. S.","family":"Saraya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed I.","family":"Saleh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asmaa H.","family":"Rabie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,15]]},"reference":[{"key":"95002_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209\u2013249 (2021).","journal-title":"CA Cancer J. Clin."},{"key":"95002_CR2","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1111\/bju.15483","volume":"128","author":"S Khadhouri","year":"2021","unstructured":"Khadhouri, S. et al. The IDENTIFY study: The investigation and detection of urological neoplasia in patients referred with suspected urinary tract cancer-a multicentre observational study. BJU Int. 128, 440\u2013450 (2021).","journal-title":"BJU Int."},{"key":"95002_CR3","doi-asserted-by":"publisher","first-page":"404","DOI":"10.3322\/caac.21631","volume":"70","author":"V Patel","year":"2020","unstructured":"Patel, V., Oh, W. & Galsky, M. Treatment of muscle-invasive and advanced bladder cancer in 2020. CA Cancer J. Clin. 70, 404\u2013423 (2020).","journal-title":"CA Cancer J. Clin."},{"key":"95002_CR4","doi-asserted-by":"publisher","first-page":"1673","DOI":"10.1016\/j.euf.2022.06.001","volume":"8","author":"S Khadhouri","year":"2022","unstructured":"Khadhouri, S. et al. Developing a diagnostic multivariable prediction model for urinary tract cancer in patients referred with haematuria: Results from the IDENTIFY collaborative study. Eur. Urol. Focus 8, 1673\u20131682 (2022).","journal-title":"Eur. Urol. Focus"},{"key":"95002_CR5","doi-asserted-by":"publisher","first-page":"E51","DOI":"10.1111\/bju.12215","volume":"112","author":"P Zehnder","year":"2013","unstructured":"Zehnder, P. et al. Unaltered oncological outcomes of radical cystectomy with extended lymphadenectomy over three decades. BJU Int. 112, E51\u2013E58 (2013).","journal-title":"BJU Int."},{"key":"95002_CR6","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/1741-7015-11-13","volume":"11","author":"G Cheung","year":"2013","unstructured":"Cheung, G. et al. Recent advances in the diagnosis and treatment of bladder cancer. BMC Med. 11, 13 (2013).","journal-title":"BMC Med."},{"issue":"5","key":"95002_CR7","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1111\/j.1365-2559.2005.02096.x","volume":"46","author":"R Ball","year":"2005","unstructured":"Ball, R. Pathology and genetics of tumours of the urinary system and male genital organs. Histopathology 46(5), 586\u2013586 (2005).","journal-title":"Histopathology"},{"issue":"1","key":"95002_CR8","first-page":"1","volume":"3","author":"O Sanli","year":"2017","unstructured":"Sanli, O. et al. Bladder cancer. Nat. Rev. Dis. Prim. 3(1), 1\u201319 (2017).","journal-title":"Nat. Rev. Dis. Prim."},{"issue":"6","key":"95002_CR9","first-page":"317","volume":"119","author":"S Woldu","year":"2017","unstructured":"Woldu, S., Bagrodia, A. & Lotan, Y. Guideline for the management of nonmuscle invasive bladder cancer: 2007 update. J. Urol. 119(6), 317\u2013380 (2017).","journal-title":"J. Urol."},{"key":"95002_CR10","first-page":"339","volume":"184","author":"S Stasi","year":"2015","unstructured":"Stasi, S. et al. Hexaminolevulinate guided fluorescence cystoscopy reduces recurrence in patients with nonmuscle invasive bladder cancer. J. Urol. 184, 339\u2013350 (2015).","journal-title":"J. Urol."},{"key":"95002_CR11","doi-asserted-by":"publisher","first-page":"105112","DOI":"10.1016\/j.compbiomed.2021.105112","volume":"140","author":"A Rabie","year":"2022","unstructured":"Rabie, A., Saleh, A. & Mansour, N. A Covid-19\u2019s integrated herd immunity (CIHI) based on classifying people vulnerability. Comput. Biol. Med. 140, 105112 (2022).","journal-title":"Comput. Biol. Med."},{"key":"95002_CR12","doi-asserted-by":"publisher","first-page":"108693","DOI":"10.1016\/j.patcog.2022.108693","volume":"128","author":"A Rabie","year":"2022","unstructured":"Rabie, A. et al. Expecting individuals\u2019 body reaction to Covid-19 based on statistical Na\u00efve Bayes technique. Pattern Recognit. 128, 108693 (2022).","journal-title":"Pattern Recognit."},{"key":"95002_CR13","doi-asserted-by":"publisher","first-page":"108669","DOI":"10.1016\/j.jneumeth.2020.108669","volume":"337","author":"G Uysala","year":"2020","unstructured":"Uysala, G. & Ozturk, M. Hippocampal atrophy based Alzheimer\u2019s disease diagnosis via machine. J. Neurosci. Methods 337, 108669 (2020).","journal-title":"J. Neurosci. Methods"},{"key":"95002_CR14","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.artmed.2019.07.007","volume":"98","author":"A Woldaregay","year":"2019","unstructured":"Woldaregay, A. et al. Data-driven modeling and prediction of blood glucose dynamics: machine learning applications in type 1 diabetes. Artif. Intell. Med. 98, 109\u2013134 (2019).","journal-title":"Artif. Intell. Med."},{"key":"95002_CR15","first-page":"1","volume":"14","author":"V Bhatta","year":"2023","unstructured":"Bhatta, V. & Palb, V. An intelligent system for diagnosing thyroid disease in pregnant ladies through artificial neural network. J. Ambient Intell. Hum. Comput. 14, 1\u201310 (2023).","journal-title":"J. Ambient Intell. Hum. Comput."},{"key":"95002_CR16","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.compbiomed.2018.09.009","volume":"102","author":"\u00d6 Y\u0131ld\u0131r\u0131m","year":"2018","unstructured":"Y\u0131ld\u0131r\u0131m, \u00d6. et al. Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput. Biol. Med. 102, 411\u2013420 (2018).","journal-title":"Comput. Biol. Med."},{"key":"95002_CR17","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s00530-017-0582-5","volume":"25","author":"X Xi","year":"2018","unstructured":"Xi, X. et al. Automated segmentation of choroidal neovascularization in optical coherence tomography images using multi-scale convolutional neural networks with structure prior. Multimed. Syst. 25, 95\u2013102 (2018).","journal-title":"Multimed. Syst."},{"key":"95002_CR18","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1007\/s00530-020-00694-1","volume":"27","author":"C Bhatt","year":"2021","unstructured":"Bhatt, C., Kumar, I. & Vijayakumar, V. The state of the art of deep learning models in medical science and their challenges. Multimed. Syst. 27, 599\u2013613 (2021).","journal-title":"Multimed. Syst."},{"key":"95002_CR19","doi-asserted-by":"publisher","first-page":"37681","DOI":"10.1007\/s11042-022-13506-7","volume":"81","author":"S Fouladi","year":"2022","unstructured":"Fouladi, S., Safaei, A., Arshad, N., Ebadi, M. & Ahmadian, A. The use of artificial neural networks to diagnose Alzheimer\u2019s disease from brain images. Multimed. Tools Appl. 81, 37681\u201337721 (2022).","journal-title":"Multimed. Tools Appl."},{"key":"95002_CR20","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O. et al. Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211\u2013252 (2015).","journal-title":"Int. J. Comput. Vis."},{"issue":"6","key":"95002_CR21","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I. & Hinton, G. Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017).","journal-title":"Commun. ACM"},{"key":"95002_CR22","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","volume":"25","author":"A Esteva","year":"2019","unstructured":"Esteva, A. et al. A guide to deep learning in healthcare. Nat. Med. 25, 24\u201329 (2019).","journal-title":"Nat. Med."},{"key":"95002_CR23","first-page":"1689","volume":"40","author":"E Laukhtina","year":"2022","unstructured":"Laukhtina, E. et al. Urethrectomy at the time of radical cystectomy for non-metastatic urothelial carcinoma of the bladder: A collaborative multicenter study. J. Urol. 40, 1689\u20131696 (2022).","journal-title":"J. Urol."},{"key":"95002_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-58347-1","volume-title":"Domain Adaptation in Computer Vision Applications","author":"G Csurka","year":"2017","unstructured":"Csurka, G. A comprehensive survey on domain adaptation for visual applications. In Domain Adaptation in Computer Vision Applications 1\u201335 (Springer, 2017)."},{"key":"95002_CR25","doi-asserted-by":"crossref","unstructured":"Kim, P. MATLAB Deep Learning: With Machine Learning, Neural Networks, and Artificial Intelligence (2017).","DOI":"10.1007\/978-1-4842-2845-6_1"},{"key":"95002_CR26","unstructured":"Agarap, A. Deep learning using rectified linear units (ReLU) 1\u201311 (2018)."},{"key":"95002_CR27","doi-asserted-by":"crossref","unstructured":"Khan, S., Rahmani, H., Shah, S. et al. A guide to convolutional neural networks for computer vision 1\u2013207 (2018).","DOI":"10.1007\/978-3-031-01821-3_1"},{"key":"95002_CR28","doi-asserted-by":"crossref","unstructured":"Munjal, P., Paul, A. & Krishnan, N. Implicit discriminator in variation auto encoder. In Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK 1\u20138 (2020).","DOI":"10.1109\/IJCNN48605.2020.9207307"},{"key":"95002_CR29","unstructured":"Hendrycks, D, Mazeika, M, Kadavath, S. et al. Using self-supervised learning can improve model robustness and uncertainty. In 33rd Conference on Neural Information Processing Systems Vol. 32, 1\u201313 (2019)."},{"key":"95002_CR30","doi-asserted-by":"crossref","unstructured":"Goyal, P., Mahajan, D. & Gupta, A.et al. Scaling and benchmarking self-supervised visual representation learning. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea 6391\u20136400 (2019).","DOI":"10.1109\/ICCV.2019.00649"},{"key":"95002_CR31","first-page":"507","volume":"96","author":"KC DeGeorge","year":"2017","unstructured":"DeGeorge, K. C. et al. Bladder cancer: Diagnosis and treatment. Amer. Fam. Physician 96, 507\u2013514 (2017).","journal-title":"Amer. Fam. Physician"},{"key":"95002_CR32","doi-asserted-by":"publisher","first-page":"2137","DOI":"10.1002\/ima.22750","volume":"32","author":"M Nawaz","year":"2022","unstructured":"Nawaz, M. et al. Melanoma segmentation: A framework of improved DenseNet77 and UNET convolutional neural network. Int. J. Imaging Syst. Technol 32, 2137\u20132153 (2022).","journal-title":"Int. J. Imaging Syst. Technol"},{"key":"95002_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-019-1466-3","volume":"43","author":"M Khan","year":"2019","unstructured":"Khan, M., Sharif, M., Akram, T., Yasmin, M. & Nayak, R. Stomach deformities recognition using rank-based deep features selection. J. Med. Syst. 43, 1\u201315 (2019).","journal-title":"J. Med. Syst."},{"key":"95002_CR34","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.2174\/1573405616666200406110547","volume":"16","author":"S Zahoor","year":"2020","unstructured":"Zahoor, S., Lali, I., Khan, M., Javed, K. & Mehmood, W. Breast cancer detection and classification using traditional computer vision techniques: A comprehensive review. Curr. Med. Imaging 16, 1187\u20131200 (2020).","journal-title":"Curr. Med. Imaging"},{"key":"95002_CR35","doi-asserted-by":"publisher","first-page":"57","DOI":"10.3390\/cancers17010057","volume":"17","author":"W Hwang","year":"2025","unstructured":"Hwang, W. et al. Artificial intelligence-based classification and segmentation of bladder cancer in cystoscope images. Cancers 17, 57 (2025).","journal-title":"Cancers"},{"key":"95002_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/cells14010014","volume":"14","author":"M Petrov","year":"2024","unstructured":"Petrov, M. et al. Detection of human bladder epithelial cancerous cells with atomic force microscopy and machine learning. Cells 14, 1\u201316 (2024).","journal-title":"Cells"},{"key":"95002_CR37","first-page":"1","volume":"16","author":"K Gumus","year":"2024","unstructured":"Gumus, K. et al. Deep learning algorithms for bladder cancer segmentation on multi-parametric MRI. MDPI 16, 1\u201310 (2024).","journal-title":"MDPI"},{"key":"95002_CR38","doi-asserted-by":"publisher","first-page":"7873","DOI":"10.3390\/s24247873","volume":"24","author":"F Tortora","year":"2024","unstructured":"Tortora, F. et al. New challenges in bladder cancer diagnosis: How biosensing tools can lead to population screening opportunities. Sensors 24, 7873 (2024).","journal-title":"Sensors"},{"key":"95002_CR39","first-page":"1","volume":"2","author":"F Jassim","year":"2013","unstructured":"Jassim, F. Image Denoising using interquartile range filter with local averaging. Int. J. Soft Comput. Eng. IJSCE 2, 1\u20135 (2013).","journal-title":"Int. J. Soft Comput. Eng. IJSCE"},{"key":"95002_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.imu.2019.100231","volume":"16","author":"S Dabeer","year":"2019","unstructured":"Dabeer, S., Khan, M. & Islam, S. Cancer diagnosis in histopathological image: CNN based approach. Inform. Med. Unlocked 16, 1\u201311 (2019).","journal-title":"Inform. Med. Unlocked"},{"key":"95002_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cmpb.2020.105568","volume":"195","author":"Z Qin","year":"2020","unstructured":"Qin, Z., Liu, Z., Zhu, P. & Xue, Y. A GAN-based image synthesis method for skin lesion classification. Comput. Methods Programs Biomed. 195, 1\u201341 (2020).","journal-title":"Comput. Methods Programs Biomed."},{"key":"95002_CR42","doi-asserted-by":"publisher","first-page":"5528622","DOI":"10.1155\/2021\/5528622","volume":"2021","author":"S Alanazi","year":"2021","unstructured":"Alanazi, S. et al. Boosting breast cancer detection using convolutional neural network. J. Healthc. Eng. 2021, 5528622 (2021).","journal-title":"J. Healthc. Eng."},{"key":"95002_CR43","doi-asserted-by":"crossref","unstructured":"Desai, S. D., Giraddi, S., Verma, N., Gupta, P. & Ramya, S. Breast cancer detection using GAN for limited labeled dataset 34\u201339 (2020).","DOI":"10.1109\/CICN49253.2020.9242551"},{"key":"95002_CR44","doi-asserted-by":"crossref","unstructured":"Patricio, C., Neves, J. & Neves, T. Explainable deep learning methods in medical diagnosis: A survey. In Electrical Engineering and Systems Science, Image and Video Processing Vol. 1, 1\u201340 (2023).","DOI":"10.1145\/3625287"},{"key":"95002_CR45","first-page":"1","volume":"147","author":"N Freitas","year":"2024","unstructured":"Freitas, N. et al. Multiple mask and boundary scoring R-CNN with cGAN data augmentation for bladder tumor segmentation in WLC videos. Artif. Intell In Medicine, ElSEVIER 147, 1\u20139 (2024).","journal-title":"Artif. Intell In Medicine, ElSEVIER"},{"key":"95002_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jpi.2022.100155","volume":"14","author":"A Mundhada","year":"2023","unstructured":"Mundhada, A. et al. Differentiation of urothelial carcinoma in histopathology images using deep learning and visualization. J. Pathol. Inform. 14, 1\u20139 (2023).","journal-title":"J. Pathol. Inform."},{"key":"95002_CR47","doi-asserted-by":"crossref","unstructured":"Fu\u2019adah, Y., Pratiwi1, N., Pramudito, M. et.al. Convolutional neural network (CNN) for automatic skin cancer classification system. In IOP Conference Series: Materials Science and Engineering, International Conference in Engineering, Technology and Innovative Researches (ICETIR 2020) Vol. 982, 012005 (2020).","DOI":"10.1088\/1757-899X\/982\/1\/012005"},{"key":"95002_CR48","doi-asserted-by":"publisher","first-page":"4372","DOI":"10.3390\/cancers15174372","volume":"15","author":"D Sun","year":"2023","unstructured":"Sun, D. et al. Survival Prediction of Patients with Bladder Cancer after Cystectomy Based on Clinical, Radiomics, and Deep-Learning Descriptors. Cancers 15, 4372 (2023).","journal-title":"Cancers"},{"key":"95002_CR49","doi-asserted-by":"publisher","first-page":"1673","DOI":"10.3390\/cancers15061673","volume":"15","author":"S Sarkar","year":"2023","unstructured":"Sarkar, S. et al. Performing automatic identification and staging of urothelial carcinoma in bladder cancer patients using a hybrid deep-machine learning approach. Cancers 15, 1673 (2023).","journal-title":"Cancers"},{"key":"95002_CR50","doi-asserted-by":"crossref","unstructured":"Ribeiro, M., Singh, S. & Guestrin, C. Why should I trust you?: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA 1135\u20131144 (2016).","DOI":"10.1145\/2939672.2939778"},{"key":"95002_CR51","first-page":"123","volume":"57","author":"J Smith","year":"2020","unstructured":"Smith, J. & Jones, A. Explainable deep learning: Enhancing learning performance and human understanding in artificial intelligence. J. Artif. Intell. Res. 57, 123\u2013140 (2020).","journal-title":"J. Artif. Intell. Res."},{"key":"95002_CR52","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"R Selvaraju","year":"2019","unstructured":"Selvaraju, R. et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vis. 128, 336\u2013359 (2019).","journal-title":"Int. J. Comput. Vis."},{"key":"95002_CR53","unstructured":"Quellec, G. Automatic image analysis method for automatically recognising at least one rare characteristic. U.S. Patent Application No. 17\/610,098 (2020)."},{"key":"95002_CR54","first-page":"1","volume":"98","author":"Z Tao","year":"2021","unstructured":"Tao, Z. et al. The ensemble deep learning model for novel COVID-19 on CT images. Appl. Soft Comput. 98, 1\u20139 (2021).","journal-title":"Appl. Soft Comput."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-95002-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-95002-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-95002-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,19]],"date-time":"2025-04-19T14:01:31Z","timestamp":1745071291000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-95002-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,15]]},"references-count":54,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["95002"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-95002-0","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,15]]},"assertion":[{"value":"13 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2025","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":"Competing interests"}}],"article-number":"12880"}}