{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T01:54:19Z","timestamp":1780451659581,"version":"3.54.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,4,11]],"date-time":"2022-04-11T00:00:00Z","timestamp":1649635200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,11]],"date-time":"2022-04-11T00:00:00Z","timestamp":1649635200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s10278-022-00625-8","type":"journal-article","created":{"date-parts":[[2022,4,11]],"date-time":"2022-04-11T19:08:21Z","timestamp":1649704101000},"page":"1091-1100","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Machine Learning\u2013Aided Chronic Kidney Disease Diagnosis Based on Ultrasound Imaging Integrated with Computer-Extracted Measurable Features"],"prefix":"10.1007","volume":"35","author":[{"given":"Sangmi","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Myeongkyun","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keunho","family":"Byeon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang Eun","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"In Ho","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young Ah","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shin-Wook","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jung Tak","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,4,11]]},"reference":[{"issue":"6","key":"625_CR1","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1007\/s00134-017-4809-x","volume":"43","author":"LG Forni","year":"2017","unstructured":"Forni LG, Darmon M, Ostermann M, Oudemans-van Straaten HM, Pettil\u00e4 V, Prowle JR, Schetz M, Joannidis M: Renal recovery after acute kidney injury. Intensive Care Med 43(6):855-866, 2017. https:\/\/doi.org\/10.1007\/s00134-017-4809-x","journal-title":"Intensive Care Med"},{"issue":"13","key":"625_CR2","doi-asserted-by":"publisher","first-page":"1294","DOI":"10.1001\/jama.2019.14745","volume":"322","author":"TK Chen","year":"2019","unstructured":"Chen TK, Knicely DH, Grams ME: Chronic Kidney Disease Diagnosis and Management: A Review. Jama 322(13):1294-1304, 2019. https:\/\/doi.org\/10.1001\/jama.2019.14745","journal-title":"Jama"},{"key":"625_CR3","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1159\/000445469","volume":"188","author":"M Meola","year":"2016","unstructured":"Meola M, Samoni S, Petrucci I: Imaging in Chronic Kidney Disease. Contrib Nephrol 188:69-80, 2016. https:\/\/doi.org\/10.1159\/000445469","journal-title":"Contrib Nephrol"},{"issue":"4","key":"625_CR4","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1053\/j.ajkd.2018.12.029","volume":"73","author":"JG Fried","year":"2019","unstructured":"Fried JG, Morgan MA: Renal Imaging: Core Curriculum 2019. Am J Kidney Dis 73(4):552-565, 2019. https:\/\/doi.org\/10.1053\/j.ajkd.2018.12.029","journal-title":"Am J Kidney Dis"},{"issue":"3","key":"625_CR5","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.4241","volume":"11","author":"S Ahmed","year":"2019","unstructured":"Ahmed S, Bughio S, Hassan M, Lal S, Ali M: Role of Ultrasound in the Diagnosis of Chronic Kidney Disease and its Correlation with Serum Creatinine Level. Cureus 11(3):e4241, 2019. https:\/\/doi.org\/10.7759\/cureus.4241","journal-title":"Cureus"},{"issue":"4","key":"625_CR6","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1097\/01.wnq.0000186666.61037.f6","volume":"21","author":"NJ Khati","year":"2005","unstructured":"Khati NJ, Hill MC, Kimmel PL: The role of ultrasound in renal insufficiency: the essentials. Ultrasound Q 21(4):227-244, 2005. https:\/\/doi.org\/10.1097\/01.wnq.0000186666.61037.f6","journal-title":"Ultrasound Q"},{"issue":"2","key":"625_CR7","doi-asserted-by":"publisher","first-page":"299","DOI":"10.7863\/ultra.34.2.299","volume":"34","author":"G Lucisano","year":"2015","unstructured":"Lucisano G, Comi N, Pelagi E, Cianfrone P, Fuiano L, Fuiano G: Can renal sonography be a reliable diagnostic tool in the assessment of chronic kidney disease? J Ultrasound Med 34(2):299-306, 2015. https:\/\/doi.org\/10.7863\/ultra.34.2.299","journal-title":"J Ultrasound Med"},{"issue":"7","key":"625_CR8","first-page":"494","volume":"87","author":"BK Crownover","year":"2013","unstructured":"Crownover BK, Bepko JL: Appropriate and safe use of diagnostic imaging. Am Fam Physician 87(7):494-501, 2013.","journal-title":"Am Fam Physician"},{"key":"625_CR9","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.clinimag.2018.02.012","volume":"51","author":"M Gulati","year":"2018","unstructured":"Gulati M, Cheng J, Loo JT, Skalski M, Malhi H, Duddalwar V: Pictorial review: Renal ultrasound. Clin Imaging 51:133-154, 2018. https:\/\/doi.org\/10.1016\/j.clinimag.2018.02.012","journal-title":"Clin Imaging"},{"issue":"4","key":"625_CR10","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1007\/s00261-018-1517-0","volume":"43","author":"LJ Brattain","year":"2018","unstructured":"Brattain LJ, Telfer BA, Dhyani M, Grajo JR, Samir AE: Machine learning for medical ultrasound: status, methods, and future opportunities. Abdom Radiol (NY) 43(4):786-799, 2018. https:\/\/doi.org\/10.1007\/s00261-018-1517-0","journal-title":"Abdom Radiol (NY)"},{"key":"625_CR11","doi-asserted-by":"publisher","unstructured":"Park SH: Artificial intelligence for ultrasonography: unique opportunities and challenges. Ultrasonography 40(1):3\u20136, 2021. https:\/\/doi.org\/10.14366\/usg.20078","DOI":"10.14366\/usg.20078"},{"key":"625_CR12","doi-asserted-by":"publisher","unstructured":"Kim YH: Artificial intelligence in medical ultrasonography: driving on an unpaved road. Ultrasonography 40(3):313\u2013317, 2021. https:\/\/doi.org\/10.14366\/usg.21031","DOI":"10.14366\/usg.21031"},{"issue":"10","key":"625_CR13","doi-asserted-by":"publisher","first-page":"5322","DOI":"10.1007\/s00330-019-06183-y","volume":"29","author":"F Tatsugami","year":"2019","unstructured":"Tatsugami F, Higaki T, Nakamura Y, Yu Z, Zhou J, Lu Y, Fujioka C, Kitagawa T, Kihara Y, Iida M, Awai K: Deep learning-based image restoration algorithm for coronary CT angiography. Eur Radiol 29(10):5322-5329, 2019. https:\/\/doi.org\/10.1007\/s00330-019-06183-y","journal-title":"Eur Radiol"},{"issue":"5","key":"625_CR14","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1016\/j.jcct.2019.04.007","volume":"13","author":"M Kolossvary","year":"2019","unstructured":"Kolossvary M, De Cecco CN, Feuchtner G, Maurovich-Horvat P: Advanced atherosclerosis imaging by CT: Radiomics, machine learning and deep learning. J Cardiovasc Comput Tomogr 13(5):274-280, 2019. https:\/\/doi.org\/10.1016\/j.jcct.2019.04.007","journal-title":"J Cardiovasc Comput Tomogr"},{"key":"625_CR15","doi-asserted-by":"publisher","first-page":"8314740","DOI":"10.1155\/2017\/8314740","volume":"2017","author":"Q Song","year":"2017","unstructured":"Song Q, Zhao L, Luo X, Dou X: Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images. J Healthc Eng 2017:8314740, 2017. https:\/\/doi.org\/10.1155\/2017\/8314740","journal-title":"J Healthc Eng"},{"issue":"1","key":"625_CR16","doi-asserted-by":"publisher","first-page":"9286","DOI":"10.1038\/s41598-018-27569-w","volume":"8","author":"JL Causey","year":"2018","unstructured":"Causey JL, Zhang J, Ma S, Jiang B, Qualls JA, Politte DG, Prior F, Zhang S, Huang X: Highly accurate model for prediction of lung nodule malignancy with CT scans. Sci Rep 8(1):9286, 2018. https:\/\/doi.org\/10.1038\/s41598-018-27569-w","journal-title":"Sci Rep"},{"issue":"2","key":"625_CR17","doi-asserted-by":"publisher","first-page":"W123","DOI":"10.2214\/ajr.17.19298","volume":"211","author":"J Uhlig","year":"2018","unstructured":"Uhlig J, Uhlig A, Kunze M, Beissbarth T, Fischer U, Lotz J, Wienbeck S: Novel Breast Imaging and Machine Learning: Predicting Breast Lesion Malignancy at Cone-Beam CT Using Machine Learning Techniques. AJR Am J Roentgenol 211(2):W123-w131, 2018. https:\/\/doi.org\/10.2214\/ajr.17.19298","journal-title":"AJR Am J Roentgenol"},{"issue":"1","key":"625_CR18","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s41747-019-0131-4","volume":"4","author":"NC D'Amico","year":"2020","unstructured":"D'Amico NC, Grossi E, Valbusa G, Rigiroli F, Colombo B, Buscema M, Fazzini D, Ali M, Malasevschi A, Cornalba G, Papa S: A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI. Eur Radiol Exp 4(1):5, 2020. https:\/\/doi.org\/10.1186\/s41747-019-0131-4","journal-title":"Eur Radiol Exp"},{"issue":"10","key":"625_CR19","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/aae255","volume":"39","author":"B Kim","year":"2018","unstructured":"Kim B, Kim KC, Park Y, Kwon JY, Jang J, Seo JK: Machine-learning-based automatic identification of fetal abdominal circumference from ultrasound images. Physiol Meas 39(10):105007, 2018. https:\/\/doi.org\/10.1088\/1361-6579\/aae255","journal-title":"Physiol Meas"},{"issue":"1","key":"625_CR20","doi-asserted-by":"publisher","first-page":"75.e71","DOI":"10.1016\/j.jpurol.2018.10.020","volume":"15","author":"Q Zheng","year":"2019","unstructured":"Zheng Q, Furth SL, Tasian GE, Fan Y: Computer-aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features. J Pediatr Urol 15(1):75.e71-75.e77, 2019. https:\/\/doi.org\/10.1016\/j.jpurol.2018.10.020","journal-title":"J Pediatr Urol"},{"issue":"3","key":"625_CR21","doi-asserted-by":"publisher","first-page":"369","DOI":"10.3348\/kjr.2019.0581","volume":"21","author":"SR Chung","year":"2020","unstructured":"Chung SR, Baek JH, Lee MK, Ahn Y, Choi YJ, Sung TY, Song DE, Kim TY, Lee JH: Computer-Aided Diagnosis System for the Evaluation of Thyroid Nodules on Ultrasonography: Prospective Non-Inferiority Study according to the Experience Level of Radiologists. Korean J Radiol 21(3):369-376, 2020. https:\/\/doi.org\/10.3348\/kjr.2019.0581","journal-title":"Korean J Radiol"},{"key":"625_CR22","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.557169","volume":"10","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Wu Q, Chen Y, Wang Y: A Clinical Assessment of an Ultrasound Computer-Aided Diagnosis System in Differentiating Thyroid Nodules With Radiologists of Different Diagnostic Experience. Front Oncol 10:557169, 2020. https:\/\/doi.org\/10.3389\/fonc.2020.557169","journal-title":"Front Oncol"},{"issue":"1","key":"625_CR23","first-page":"1","volume":"2","author":"KDIGO CKD Work Group","year":"2013","unstructured":"KDIGO CKD Work Group: KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl 2(1):1-150, 2013.","journal-title":"Kidney Int Suppl"},{"issue":"5","key":"625_CR24","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1053\/j.ajkd.2014.01.416","volume":"63","author":"LA Inker","year":"2014","unstructured":"Inker LA, Astor BC, Fox CH, Isakova T, Lash JP, Peralta CA, Kurella Tamura M, Feldman HI: KDOQI US commentary on the 2012 KDIGO clinical practice guideline for the evaluation and management of CKD. Am J Kidney Dis 63(5):713-735, 2014. https:\/\/doi.org\/10.1053\/j.ajkd.2014.01.416","journal-title":"Am J Kidney Dis"},{"issue":"9","key":"625_CR25","doi-asserted-by":"publisher","first-page":"859","DOI":"10.3109\/0886022x.2011.605533","volume":"33","author":"X Du","year":"2011","unstructured":"Du X, Hu B, Jiang L, Wan X, Fan L, Wang F, Cao C: Implication of CKD-EPI equation to estimate glomerular filtration rate in Chinese patients with chronic kidney disease. Ren Fail 33(9):859-865, 2011. https:\/\/doi.org\/10.3109\/0886022x.2011.605533","journal-title":"Ren Fail"},{"key":"625_CR26","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R, editors: Mask r-cnn. Proceedings of the IEEE international conference on computer vision; 2017","DOI":"10.1109\/ICCV.2017.322"},{"issue":"6","key":"625_CR27","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren S, He K, Girshick R, Sun J: Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence 39(6):1137-1149, 2016.","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"625_CR28","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T, editors: U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention; 2015: Springer,","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"625_CR29","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T, editors: Fully convolutional networks for semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition; 2015","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"625_CR30","doi-asserted-by":"crossref","unstructured":"Golan D, Donner Y, Mansi C, Jaremko J, Ramachandran M: Fully automating Graf\u2019s method for DDH diagnosis using deep convolutional neural networks. Deep Learning and Data Labeling for Medical Applications. Springer, 2016. p. 130\u2013141","DOI":"10.1007\/978-3-319-46976-8_14"},{"key":"625_CR31","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K, editors: Aggregated residual transformations for deep neural networks. Proceedings of the IEEE conference on computer vision and pattern recognition; 2017","DOI":"10.1109\/CVPR.2017.634"},{"key":"625_CR32","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S, editors: Feature pyramid networks for object detection. Proceedings of the IEEE conference on computer vision and pattern recognition; 2017","DOI":"10.1109\/CVPR.2017.106"},{"issue":"4","key":"625_CR33","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1016\/s0272-6386(01)80118-9","volume":"37","author":"JA Manley","year":"2001","unstructured":"Manley JA, O'Neill WC: How echogenic is echogenic? Quantitative acoustics of the renal cortex. Am J Kidney Dis 37(4):706-711, 2001. https:\/\/doi.org\/10.1016\/s0272-6386(01)80118-9","journal-title":"Am J Kidney Dis"},{"key":"625_CR34","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J, editors: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition; 2016","DOI":"10.1109\/CVPR.2016.90"},{"key":"625_CR35","unstructured":"Kingma DP, Ba J: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014."},{"key":"625_CR36","unstructured":"Loshchilov I, Hutter F: Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016."},{"key":"625_CR37","doi-asserted-by":"publisher","unstructured":"Chougrad H, Zouaki H, Alheyane O: Deep Convolutional Neural Networks for breast cancer screening. Comput Methods Programs Biomed 157:19-30, 2018. https:\/\/doi.org\/10.1016\/j.cmpb.2018.01.011","DOI":"10.1016\/j.cmpb.2018.01.011"},{"key":"625_CR38","unstructured":"Saman Sarraf GT. Classification of Alzheimer\u2019s Disease Using fMRI Data and Deep Learning Convolutional Neural Networks.\u00a02016.\u00a0arXiv:1603.08631"},{"issue":"1","key":"625_CR39","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1186\/s41747-018-0061-6","volume":"2","author":"F Pesapane","year":"2018","unstructured":"Pesapane F, Codari M, Sardanelli F: Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp 2(1):35, 2018. https:\/\/doi.org\/10.1186\/s41747-018-0061-6","journal-title":"Eur Radiol Exp"},{"key":"625_CR40","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak J, van Ginneken B, S\u00e1nchez CI: A survey on deep learning in medical image analysis. Med Image Anal 42:60-88, 2017. https:\/\/doi.org\/10.1016\/j.media.2017.07.005","journal-title":"Med Image Anal"},{"issue":"15","key":"625_CR41","doi-asserted-by":"publisher","first-page":"4057","DOI":"10.1016\/j.ijleo.2014.01.114","volume":"125","author":"XC Kaizhi Wu","year":"2014","unstructured":"Kaizhi Wu XC, Mingyue Ding: Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound. Optik 125(15):4057-4063, 2014. https:\/\/doi.org\/10.1016\/j.ijleo.2014.01.114","journal-title":"Optik"},{"key":"625_CR42","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1038\/s41746-019-0104-2","volume":"2","author":"CC Kuo","year":"2019","unstructured":"Kuo CC, Chang CM, Liu KT, Lin WK, Chiang HY, Chung CW, Ho MR, Sun PR, Yang RL, Chen KT: Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning. NPJ Digit Med 2:29, 2019. https:\/\/doi.org\/10.1038\/s41746-019-0104-2","journal-title":"NPJ Digit Med"},{"issue":"6","key":"625_CR43","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1016\/j.rcl.2017.06.004","volume":"55","author":"BL Niell","year":"2017","unstructured":"Niell BL, Freer PE, Weinfurtner RJ, Arleo EK, Drukteinis JS: Screening for Breast Cancer. Radiol Clin North Am 55(6):1145-1162, 2017. https:\/\/doi.org\/10.1016\/j.rcl.2017.06.004","journal-title":"Radiol Clin North Am"},{"key":"625_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2020.108992","volume":"127","author":"H Zhou","year":"2020","unstructured":"Zhou H, Jin Y, Dai L, Zhang M, Qiu Y, Wang K, Tian J, Zheng J: Differential Diagnosis of Benign and Malignant Thyroid Nodules Using Deep Learning Radiomics of Thyroid Ultrasound Images. Eur J Radiol 127:108992, 2020. https:\/\/doi.org\/10.1016\/j.ejrad.2020.108992","journal-title":"Eur J Radiol"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-022-00625-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-022-00625-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-022-00625-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T18:09:29Z","timestamp":1666202969000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-022-00625-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,11]]},"references-count":44,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["625"],"URL":"https:\/\/doi.org\/10.1007\/s10278-022-00625-8","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,11]]},"assertion":[{"value":"16 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 March 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 April 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Yonsei University Health System Clinical Trial Center (1\u20132018-0039).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"The need for informed consent was waived by the institutional review board owing to the study\u2019s retrospective design.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}