{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T03:00:49Z","timestamp":1774321249642,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T00:00:00Z","timestamp":1719360000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T00:00:00Z","timestamp":1719360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100006280","name":"Ministerio de Ciencia y Tecnolog\u00eda","doi-asserted-by":"publisher","award":["PID2019-110686RB-I00"],"award-info":[{"award-number":["PID2019-110686RB-I00"]}],"id":[{"id":"10.13039\/501100006280","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012818","name":"Comunidad de Madrid","doi-asserted-by":"publisher","award":["PEJ-2021-AI\/TIC-23268"],"award-info":[{"award-number":["PEJ-2021-AI\/TIC-23268"]}],"id":[{"id":"10.13039\/100012818","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Universidad Nacional de Educacion Distancia"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Breast cancer is the most common cancer in women. Ultrasound is one of the most used techniques for diagnosis, but an expert in the field is necessary to interpret the test. Computer-aided diagnosis (CAD) systems aim to help physicians during this process. Experts use the Breast Imaging-Reporting and Data System (BI-RADS) to describe tumors according to several features (shape, margin, orientation...) and estimate their malignancy, with a common language. To aid in tumor diagnosis with BI-RADS explanations, this paper presents a deep neural network for tumor detection, description, and classification. An expert radiologist described with BI-RADS terms 749 nodules taken from public datasets. The YOLO detection algorithm is used to obtain Regions of Interest (ROIs), and then a model, based on a multi-class classification architecture, receives as input each ROI and outputs the BI-RADS descriptors, the BI-RADS classification (with 6 categories), and a Boolean classification of malignancy. Six hundred of the nodules were used for 10-fold cross-validation (CV) and 149 for testing. The accuracy of this model was compared with state-of-the-art CNNs for the same task. This model outperforms plain classifiers in the agreement with the expert (Cohen\u2019s kappa), with a mean over the descriptors of 0.58 in CV and 0.64 in testing, while the second best model yielded kappas of 0.55 and 0.59, respectively. Adding YOLO to the model significantly enhances the performance (0.16 in CV and 0.09 in testing). More importantly, training the model with BI-RADS descriptors enables the explainability of the Boolean malignancy classification without reducing accuracy.<\/jats:p>","DOI":"10.1007\/s10278-024-01155-1","type":"journal-article","created":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T18:14:53Z","timestamp":1719425693000},"page":"2940-2954","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Deep Learning for Describing Breast Ultrasound Images with BI-RADS Terms"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4746-098X","authenticated-orcid":false,"given":"Mikel","family":"Carrilero-Mardones","sequence":"first","affiliation":[]},{"given":"Manuela","family":"Parras-Jurado","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4951-8102","authenticated-orcid":false,"given":"Alberto","family":"Nogales","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3588-7233","authenticated-orcid":false,"given":"Jorge","family":"P\u00e9rez-Mart\u00edn","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9855-9248","authenticated-orcid":false,"given":"Francisco Javier","family":"D\u00edez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,26]]},"reference":[{"key":"1155_CR1","doi-asserted-by":"crossref","unstructured":"F.\u00a0Bray, M.\u00a0Laversanne, H.\u00a0Sung, J.\u00a0Ferlay, R.\u00a0L. Siegel, I.\u00a0Soerjomataram, A.\u00a0Jemal, Global cancer statistics 2022: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA: a cancer journal for clinicians (2021) 229\u2013263.","DOI":"10.3322\/caac.21834"},{"key":"1155_CR2","first-page":"e1203","volume":"8","author":"V McCormack","year":"2020","unstructured":"V.\u00a0McCormack, F.\u00a0McKenzie, M.\u00a0Foerster, A.\u00a0Zietsman, M.\u00a0Galukande, C.\u00a0Adisa, A.\u00a0Anele, G.\u00a0Parham, L.\u00a0F. Pinder, H.\u00a0Cubasch, et\u00a0al., Breast cancer survival and survival gap apportionment in sub-Saharan Africa (ABC-DO): a prospective cohort study, The Lancet Global Health 8 (2020) e1203\u2013e1212.","journal-title":"Global Health"},{"key":"1155_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/bcr970","volume":"7","author":"CM Ronckers","year":"2004","unstructured":"C.\u00a0M. Ronckers, C.\u00a0A. Erdmann, C.\u00a0E. Land, Radiation and breast cancer: a review of current evidence, Breast Cancer Research 7 (2004) 1\u201312.","journal-title":"Breast Cancer Research"},{"key":"1155_CR4","doi-asserted-by":"crossref","unstructured":"H.\u00a0Qi, N.\u00a0A. Diakides, Thermal infrared imaging in early breast cancer detection-a survey of recent research, in: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No. 03CH37439), Vol.\u00a02, 2003, pp. 1109\u20131112.","DOI":"10.1109\/IEMBS.2003.1279442"},{"key":"1155_CR5","doi-asserted-by":"crossref","unstructured":"S.\u00a0T. Kakileti, G.\u00a0Manjunath, H.\u00a0Madhu, H.\u00a0V. Ramprakash, Advances in breast thermography, in: New Perspectives in Breast Imaging, 2017, pp. 91\u2013108.","DOI":"10.5772\/intechopen.69198"},{"key":"1155_CR6","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1016\/j.clinimag.2012.09.024","volume":"37","author":"A Jalalian","year":"2013","unstructured":"A.\u00a0Jalalian, S.\u00a0B. Mashohor, H.\u00a0R. Mahmud, M.\u00a0I.\u00a0B. Saripan, A.\u00a0R.\u00a0B. Ramli, B.\u00a0Karasfi, Computer-aided detection\/diagnosis of breast cancer in mammography and ultrasound: A review, Clinical Imaging 37 (2013) 420\u2013426.","journal-title":"Clinical Imaging"},{"key":"1155_CR7","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"Y.\u00a0LeCun, Y.\u00a0Bengio, G.\u00a0Hinton, Deep learning, Nature 521 (2015) 436\u2013444.","journal-title":"Nature"},{"key":"1155_CR8","unstructured":"A.\u00a0Krizhevsky, I.\u00a0Sutskever, G.\u00a0E. Hinton, ImageNet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems 25 (2012)."},{"key":"1155_CR9","doi-asserted-by":"crossref","unstructured":"G.\u00a0Litjens, T.\u00a0Kooi, B.\u00a0E. Bejnordi, A.\u00a0A.\u00a0A. Setio, F.\u00a0Ciompi, M.\u00a0Ghafoorian, J.\u00a0A. Van Der\u00a0Laak, B.\u00a0Van\u00a0Ginneken, C.\u00a0I. S\u00e1nchez, A survey on deep learning in medical image analysis, Medical Image Analysis 42 (2017) 60\u201388.","DOI":"10.1016\/j.media.2017.07.005"},{"key":"1155_CR10","doi-asserted-by":"crossref","unstructured":"K.\u00a0He, X.\u00a0Zhang, S.\u00a0Ren, J.\u00a0Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1155_CR11","unstructured":"K.\u00a0Simonyan, A.\u00a0Zisserman, Very deep convolutional networks for large-scale image recognition, in: International Conference on Learning Representations, 2015, pp. 1\u201314."},{"key":"1155_CR12","unstructured":"Data protection in EU, https:\/\/commission.europa.eu\/law\/law-topic\/data-protection\/data-protection-eu_en, accessed on May 2024 (2020)."},{"key":"1155_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102470","volume":"79","author":"BH van der Velden","year":"2022","unstructured":"B.\u00a0H. van der Velden, H.\u00a0J. Kuijf, K.\u00a0G. Gilhuijs, M.\u00a0A. Viergever, Explainable artificial intelligence (XAI) in deep learning-based medical image analysis, Medical Image Analysis 79 (2022) 102470.","journal-title":"Medical Image Analysis"},{"key":"1155_CR14","doi-asserted-by":"crossref","unstructured":"F.\u00a0Yang, Z.\u00a0Huang, J.\u00a0Scholtz, D.\u00a0L. Arendt, How do visual explanations foster end users\u2019 appropriate trust in machine learning?, in: Proceedings of the 25th International Conference on Intelligent User Interfaces, 2020, pp. 189\u2013201.","DOI":"10.1145\/3377325.3377480"},{"key":"1155_CR15","doi-asserted-by":"crossref","unstructured":"H.\u00a0Liu, G.\u00a0Cui, Y.\u00a0Luo, Y.\u00a0Guo, L.\u00a0Zhao, Y.\u00a0Wang, A.\u00a0Subasi, S.\u00a0Dogan, T.\u00a0Tuncer, Artificial intelligence-based breast cancer diagnosis using ultrasound images and grid-based deep feature generator, International Journal of General Medicine (2022) 2271\u20132282.","DOI":"10.2147\/IJGM.S347491"},{"issue":"4","key":"1155_CR16","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1109\/TKDE.2019.2891622","volume":"32","author":"Q Huang","year":"2019","unstructured":"Q.\u00a0Huang, Y.\u00a0Chen, L.\u00a0Liu, D.\u00a0Tao, X.\u00a0Li, On combining biclustering mining and AdaBoost for breast tumor classification, IEEE Transactions on Knowledge and Data Engineering 32\u00a0(4) (2019) 728\u2013738.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"1155_CR17","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1016\/j.ins.2019.06.054","volume":"502","author":"Q Huang","year":"2019","unstructured":"Q.\u00a0Huang, B.\u00a0Hu, F.\u00a0Zhang, Evolutionary optimized fuzzy reasoning with mined diagnostic patterns for classification of breast tumors in ultrasound, Information Sciences 502 (2019) 525\u2013536.","journal-title":"Information Sciences"},{"key":"1155_CR18","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1007\/s10278-013-9593-8","volume":"26","author":"WK Moon","year":"2013","unstructured":"W.\u00a0K. Moon, C.-M. Lo, J.\u00a0M. Chang, C.-S. Huang, J.-H. Chen, R.-F. Chang, Quantitative ultrasound analysis for classification of BI-RADS category 3 breast masses, Journal of Digital Imaging 26 (2013) 1091\u20131098.","journal-title":"Journal of Digital Imaging"},{"key":"1155_CR19","doi-asserted-by":"publisher","first-page":"980","DOI":"10.1016\/j.ultrasmedbio.2015.11.016","volume":"42","author":"J Shan","year":"2016","unstructured":"J.\u00a0Shan, S.\u00a0K. Alam, B.\u00a0Garra, Y.\u00a0Zhang, T.\u00a0Ahmed, Computer-aided diagnosis for breast ultrasound using computerized BI-RADS features and machine learning methods, Ultrasound in Medicine & Biology 42 (2016) 980\u2013988.","journal-title":"Ultrasound in Medicine & Biology"},{"key":"1155_CR20","doi-asserted-by":"publisher","first-page":"3","DOI":"10.14366\/usg.16012","volume":"36","author":"K Kim","year":"2017","unstructured":"K.\u00a0Kim, M.\u00a0K. Song, E.-K. Kim, J.\u00a0H. Yoon, Clinical application of S-Detect to breast masses on ultrasonography: A study evaluating the diagnostic performance and agreement with a dedicated breast radiologist, Ultrasonography 36 (2017) 3\u20139.","journal-title":"Ultrasonography"},{"key":"1155_CR21","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1109\/TUFFC.2021.3132933","volume":"69","author":"Q Huang","year":"2021","unstructured":"Q.\u00a0Huang, L.\u00a0Ye, multi-task\/single-task joint learning of ultrasound BI-RADS features, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 69 (2021) 691\u2013701.","journal-title":"IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control"},{"key":"1155_CR22","doi-asserted-by":"crossref","unstructured":"B.\u00a0Zhang, A.\u00a0Vakanski, M.\u00a0Xian, BI-RADS-Net: An explainable multitask learning approach for cancer diagnosis in breast ultrasound images, in: 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), 2021, pp. 1\u20136.","DOI":"10.1109\/MLSP52302.2021.9596314"},{"key":"1155_CR23","doi-asserted-by":"crossref","unstructured":"M.\u00a0Karimzadeh, A.\u00a0Vakanski, M.\u00a0Xian, B.\u00a0Zhang, Post-hoc explainability of bi-rads descriptors in a multi-task framework for breast cancer detection and segmentation, in: 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), 2023, pp. 1\u20136.","DOI":"10.1109\/MLSP55844.2023.10286006"},{"key":"1155_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.medengphy.2022.103895","volume":"108","author":"E Kaplan","year":"2022","unstructured":"E.\u00a0Kaplan, W.\u00a0Y. Chan, S.\u00a0Dogan, P.\u00a0D. Barua, H.\u00a0T. Bulut, T.\u00a0Tuncer, M.\u00a0Cizik, R.-S. Tan, U.\u00a0R. Acharya, Automated BI-RADS classification of lesions using pyramid triple deep feature generator technique on breast ultrasound images, Medical Engineering & Physics 108 (2022) 103895.","journal-title":"Medical Engineering & Physics"},{"key":"1155_CR25","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1109\/TBME.2015.2496264","volume":"63","author":"FA Spanhol","year":"2015","unstructured":"F.\u00a0A. Spanhol, L.\u00a0S. Oliveira, C.\u00a0Petitjean, L.\u00a0Heutte, A dataset for breast cancer histopathological image classification, IEEE Transactions on Biomedical Engineering 63 (2015) 1455\u20131462.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"1155_CR26","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.1109\/JBHI.2017.2731873","volume":"22","author":"MH Yap","year":"2017","unstructured":"M.\u00a0H. Yap, et\u00a0al., Automated breast ultrasound lesions detection using convolutional neural networks, IEEE Journal of Biomedical and Health Informatics 22 (2017) 1218\u20131226.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"1155_CR27","doi-asserted-by":"publisher","first-page":"729","DOI":"10.3390\/healthcare10040729","volume":"10","author":"Y Zhang","year":"2022","unstructured":"Y.\u00a0Zhang, M.\u00a0Xian, H.-D. Cheng, B.\u00a0Shareef, J.\u00a0Ding, F.\u00a0Xu, K.\u00a0Huang, B.\u00a0Zhang, C.\u00a0Ning, Y.\u00a0Wang, BUSIS: a benchmark for breast ultrasound image segmentation, Healthcare 10 (2022) 729.","journal-title":"Healthcare"},{"key":"1155_CR28","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"D.\u00a0G. Lowe, Distinctive image features from scale-invariant keypoints, International Journal of Computer VFision 60 (2004) 91\u2013110.","journal-title":"International Journal of Computer VFision"},{"key":"1155_CR29","doi-asserted-by":"crossref","unstructured":"S.\u00a0Lapuschkin, S.\u00a0W\u00e4dchen, A.\u00a0Binder, G.\u00a0Montavon, W.\u00a0Samek, K.-R. M\u00fcller, Unmasking Clever Hans predictors and assessing what machines really learn, Nature Communications 10 (2019) 1\u20138.","DOI":"10.1038\/s41467-019-08987-4"},{"key":"1155_CR30","doi-asserted-by":"crossref","unstructured":"J.\u00a0Redmon, S.\u00a0Divvala, R.\u00a0Girshick, A.\u00a0Farhadi, You only look once: Unified, real-time object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 779\u2013788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"1155_CR31","doi-asserted-by":"crossref","unstructured":"M.\u00a0Hashemi, Enlarging smaller images before inputting into convolutional neural network: Zero-padding vs. interpolation, Journal of Big Data 6 (2019) 1\u201313.","DOI":"10.1186\/s40537-019-0263-7"},{"key":"1155_CR32","unstructured":"D.\u00a0Hendrycks, K.\u00a0Gimpel, Bridging nonlinearities and stochastic regularizers with gaussian error linear units, CoRR (2016)."},{"key":"1155_CR33","unstructured":"K.\u00a0Xu, et\u00a0al., Show, attend and tell: Neural image caption generation with visual attention, in: International conference on machine learning, PMLR, 2015, pp. 2048\u20132057."},{"key":"1155_CR34","doi-asserted-by":"crossref","unstructured":"D.\u00a0Spak, J.\u00a0Plaxco, L.\u00a0Santiago, M.\u00a0Dryden, B.\u00a0Dogan, Bi-rads\u00ae fifth edition: A summary of changes, Diagnostic and Interventional Imaging 98 (2017) 179\u2013190.","DOI":"10.1016\/j.diii.2017.01.001"},{"key":"1155_CR35","doi-asserted-by":"crossref","unstructured":"G.\u00a0Huang, Z.\u00a0Liu, L.\u00a0V.\u00a0D. Maaten, K.\u00a0Q. Weinberger, Densely connected convolutional networks, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Los Alamitos, CA, USA, 2017, pp. 2261\u20132269.","DOI":"10.1109\/CVPR.2017.243"},{"key":"1155_CR36","unstructured":"A.\u00a0G. Howard, M.\u00a0Zhu, B.\u00a0Chen, D.\u00a0Kalenichenko, W.\u00a0Wang, T.\u00a0Weyand, M.\u00a0Andreetto, H.\u00a0Adam, Mobilenets: Efficient convolutional neural networks for mobile vision applications, CoRR (2017)."},{"key":"1155_CR37","unstructured":"F.\u00a0Wang, et\u00a0al., Residual attention network for image classification, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 3156\u20133164."},{"key":"1155_CR38","doi-asserted-by":"crossref","unstructured":"X.\u00a0Mei, Z.\u00a0Liu, P.\u00a0M. Robson, B.\u00a0Marinelli, M.\u00a0Huang, A.\u00a0Doshi, A.\u00a0Jacobi, C.\u00a0Cao, K.\u00a0E. Link, T.\u00a0Yang, et\u00a0al., RadimageNet: an open radiologic deep learning research dataset for effective transfer learning, Radiology: Artificial Intelligence 4 (2022) e210315.","DOI":"10.1148\/ryai.210315"},{"key":"1155_CR39","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1148\/radiol.2392042127","volume":"239","author":"E Lazarus","year":"2006","unstructured":"E.\u00a0Lazarus, M.\u00a0B. Mainiero, B.\u00a0Schepps, S.\u00a0L. Koelliker, L.\u00a0S. Livingston, BI-RADS lexicon for US and mammography: Interobserver variability and positive predictive value, Radiology 239 (2006) 385\u2013391.","journal-title":"Radiology"},{"key":"1155_CR40","doi-asserted-by":"crossref","unstructured":"C.\u00a0S. Park, J.\u00a0H. Lee, H.\u00a0W. Yim, B.\u00a0J. Kang, H.\u00a0S. Kim, J.\u00a0Im\u00a0Jung, N.\u00a0Y. Jung, S.\u00a0H. Kim, Observer agreement using the ACR breast imaging reporting and data system (BI-RADS)-ultrasound, (2003), Korean Journal of Radiology 8 (2007) 397\u2013402.","DOI":"10.3348\/kjr.2007.8.5.397"},{"key":"1155_CR41","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/j.ejrad.2007.04.008","volume":"65","author":"H-J Lee","year":"2008","unstructured":"H.-J. Lee, E.-K. Kim, M.\u00a0J. Kim, J.\u00a0H. Youk, J.\u00a0Y. Lee, D.\u00a0R. Kang, K.\u00a0K. Oh, observer variability of breast imaging reporting and data system (BI-RADS) for breast ultrasound, European Journal of Radiology 65 (2008) 293\u2013298.","journal-title":"European Journal of Radiology"},{"key":"1155_CR42","doi-asserted-by":"crossref","unstructured":"S.\u00a0DasGupta, Pillai\u2019s trace test, Encyclopedia of biostatistics 6 (2005).","DOI":"10.1002\/0471667196.ess1965.pub2"},{"key":"1155_CR43","first-page":"897","volume":"2","author":"H Abdi","year":"2010","unstructured":"H.\u00a0Abdi, L.\u00a0J. Williams, Newman-keuls test and Tukey test, Encyclopedia of research design 2 (2010) 897\u2013902.","journal-title":"Encyclopedia of research design"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01155-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01155-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01155-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T19:08:02Z","timestamp":1733166482000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-024-01155-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,26]]},"references-count":43,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1155"],"URL":"https:\/\/doi.org\/10.1007\/s10278-024-01155-1","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,26]]},"assertion":[{"value":"16 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 June 2024","order":4,"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":"Conflict of interest"}},{"value":"All the data have been obtained from public datasets, so ethics approval was not required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}}]}}