{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T17:11:20Z","timestamp":1793380280660,"version":"4.5.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2025,6,14]],"date-time":"2025-06-14T00:00:00Z","timestamp":1749859200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,6,14]],"date-time":"2025-06-14T00:00:00Z","timestamp":1749859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Sebald Fund"},{"name":"Janssen Pharmaceuticals Ltd."},{"name":"KNAW Medical Sciences Fund, Royal Netherlands Academy of Arts & Sciences"},{"DOI":"10.13039\/501100005342","name":"Stichting Vrienden Beatrix Kinderziekenhuis","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005342","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Cardiovasc Imaging"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Purpose<\/jats:title>\n            <jats:p>Pulmonary arterial hypertension (PAH) significantly affects the pulmonary vasculature, requiring accurate estimation of mean pulmonary arterial pressure (mPAP) and pulmonary vascular resistance index (PVRi). Although cardiac catheterization is the gold standard for these measurements, it poses risks, especially in children. This pilot study explored how machine learning (ML) can predict pulmonary hemodynamics from non-invasive cardiac magnetic resonance (CMR) cine images in pediatric PAH patients.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>A retrospective analysis of 40 CMR studies from children with PAH using a four-fold stratified group cross-validation was conducted. The endpoints were severity profiles of mPAP and PVRi, categorised as \u2018low\u2019, \u2018high\u2019, and \u2018extreme\u2019. Deep learning (DL) and traditional ML models were optimized through hyperparameter tuning. Receiver operating characteristic curves and area under the curve (AUC) were used as the primary evaluation metrics.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>DL models utilizing CMR cine imaging showed the best potential for predicting mPAP and PVRi severity profiles on test folds (AUC<jats:sub>mPAP<\/jats:sub>=0.82 and AUC<jats:sub>PVRi<\/jats:sub>=0.73). True positive rates (TPR) for predicting low, high, and extreme mPAP were 5\/10, 11\/16, and 11\/14, respectively. TPR for predicting low, high, and extreme PVRi were 5\/13, 14\/15, and 7\/12, respectively. Optimal DL models only used spatial patterns from consecutive CMR cine frames to maximize prediction performance.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>This exploratory pilot study demonstrates the potential of DL leveraging CMR imaging for non-invasive prediction of mPAP and PVRi in pediatric PAH. While preliminary, these findings may lay the groundwork for future advancements in CMR imaging in pediatric PAH, offering a pathway to safer disease monitoring and reduced reliance on invasive cardiac catheterization.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s10554-025-03434-6","type":"journal-article","created":{"date-parts":[[2025,6,14]],"date-time":"2025-06-14T03:44:45Z","timestamp":1749872685000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Predicting pulmonary hemodynamics in pediatric pulmonary arterial hypertension using cardiac magnetic resonance imaging and machine learning: an exploratory pilot study"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9531-8980","authenticated-orcid":false,"given":"Hung","family":"Chu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4460-4992","authenticated-orcid":false,"given":"Rosaria J.","family":"Ferreira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9024-4142","authenticated-orcid":false,"given":"Chantal","family":"Lokhorst","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3146-5831","authenticated-orcid":false,"given":"Johannes M.","family":"Douwes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1223-6067","authenticated-orcid":false,"given":"Meindina G.","family":"Haarman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8851-0595","authenticated-orcid":false,"given":"Tineke P.","family":"Willems","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4385-5784","authenticated-orcid":false,"given":"Rolf M.F.","family":"Berger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3017-746X","authenticated-orcid":false,"given":"Mark-Jan","family":"Ploegstra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,14]]},"reference":[{"key":"3434_CR1","doi-asserted-by":"publisher","first-page":"2401095","DOI":"10.1183\/13993003.01095-2024","volume":"64","author":"C Guignabert","year":"2024","unstructured":"Guignabert C, Aman J, Bonnet S et al (2024) Pathology and pathobiology of pulmonary hypertension: current insights and future directions. Eur Respir J 64:2401095. https:\/\/doi.org\/10.1183\/13993003.01095-2024","journal-title":"Eur Respir J"},{"key":"3434_CR2","doi-asserted-by":"publisher","unstructured":"Haarman MG, Coenraad I, Hagdorn QAJ et al (2023) Cardiac magnetic resonance derived left ventricular eccentricity index and right ventricular mass measurements predict outcome in children with pulmonary arterial hypertension. Children 10. https:\/\/doi.org\/10.3390\/children10040756","DOI":"10.3390\/children10040756"},{"key":"3434_CR3","doi-asserted-by":"publisher","first-page":"2401345","DOI":"10.1183\/13993003.01345-2024","volume":"64","author":"D Ivy","year":"2024","unstructured":"Ivy D, Rosenzweig EB, Abman SH et al (2024) Embracing the challenges of neonatal and paediatric pulmonary hypertension. Eur Respir J 64:2401345. https:\/\/doi.org\/10.1183\/13993003.01345-2024","journal-title":"Eur Respir J"},{"key":"3434_CR4","doi-asserted-by":"publisher","first-page":"1102","DOI":"10.21037\/CDT-20-395","volume":"11","author":"M Kaestner","year":"2021","unstructured":"Kaestner M, Apitz C, Lammers AE (2021) Cardiac catheterization in pediatric pulmonary hypertension: a systematic and practical approach. Cardiovasc Diagn Ther 11:1102. https:\/\/doi.org\/10.21037\/CDT-20-395","journal-title":"Cardiovasc Diagn Ther"},{"key":"3434_CR5","doi-asserted-by":"publisher","unstructured":"Knight DS, Kotecha T, Martinez-Naharro A et al (2019) Cardiovascular magnetic resonance-guided right heart catheterization in a conventional CMR environment - predictors of procedure success and duration in pulmonary artery hypertension. J Cardiovasc Magn Reson 21. https:\/\/doi.org\/10.1186\/S12968-019-0569-9","DOI":"10.1186\/S12968-019-0569-9"},{"key":"3434_CR6","doi-asserted-by":"publisher","first-page":"2718","DOI":"10.1161\/CIRCULATIONAHA.107.723213","volume":"116","author":"DW Brown","year":"2007","unstructured":"Brown DW, Gauvreau K, Powell AJ et al (2007) Cardiac magnetic resonance versus routine cardiac catheterization before bidirectional Glenn anastomosis in infants with functional single ventricle: a prospective randomized trial. Circulation 116:2718\u20132725. https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.107.723213","journal-title":"Circulation"},{"key":"3434_CR7","doi-asserted-by":"crossref","unstructured":"Lindemann B, M\u00fcller T, Vietz H et al (2021) A survey on long short-term memory networks for time series prediction. In: Procedia CIRP. Elsevier B.V., pp 650\u2013655","DOI":"10.1016\/j.procir.2021.03.088"},{"key":"3434_CR8","doi-asserted-by":"publisher","unstructured":"Alzubaidi L, Zhang J, Humaidi AJ et al (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8. https:\/\/doi.org\/10.1186\/S40537-021-00444-8","DOI":"10.1186\/S40537-021-00444-8"},{"key":"3434_CR9","doi-asserted-by":"publisher","unstructured":"Brnabic A, Hess LM (2021) Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making. BMC Med Inf Decis Mak 21. https:\/\/doi.org\/10.1186\/S12911-021-01403-2","DOI":"10.1186\/S12911-021-01403-2"},{"key":"3434_CR10","doi-asserted-by":"publisher","unstructured":"Leha A, Hellenkamp K, Uns\u00f6ld B et al (2019) A machine learning approach for the prediction of pulmonary hypertension. PLoS ONE 14. https:\/\/doi.org\/10.1371\/JOURNAL.PONE.0224453","DOI":"10.1371\/JOURNAL.PONE.0224453"},{"key":"3434_CR11","doi-asserted-by":"publisher","unstructured":"Gazibarich GJ, Boland JE, Wang LW (2019) Analysis and interpretation of Fick and thermodilution cardiac output determinations. Interventional Cardiol Cardiac Catheterisation 221\u2013232. https:\/\/doi.org\/10.1201\/9781351060356-17","DOI":"10.1201\/9781351060356-17"},{"key":"3434_CR12","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1016\/J.AMJCARD.2013.10.049","volume":"113","author":"HG Freling","year":"2014","unstructured":"Freling HG, Willems TP, Van Melle JP et al (2014) Effect of right ventricular outflow tract obstruction on right ventricular volumes and exercise capacity in patients with repaired tetralogy of fallot. Am J Cardiol 113:719\u2013723. https:\/\/doi.org\/10.1016\/J.AMJCARD.2013.10.049","journal-title":"Am J Cardiol"},{"key":"3434_CR13","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1007\/S10554-012-0112-7","volume":"29","author":"HG Freling","year":"2013","unstructured":"Freling HG, Van Wijk K, Jaspers K et al (2013) Impact of right ventricular endocardial trabeculae on volumes and function assessed by CMR in patients with tetralogy of fallot. Int J Cardiovasc Imaging 29:625\u2013631. https:\/\/doi.org\/10.1007\/S10554-012-0112-7","journal-title":"Int J Cardiovasc Imaging"},{"key":"3434_CR14","doi-asserted-by":"publisher","unstructured":"Grynblat J, Malekzadeh-Milani S, Meot M et al (2023) Monitoring of hemodynamics with right heart catheterization in children with pulmonary arterial hypertension. J Am Heart Assoc 12. https:\/\/doi.org\/10.1161\/JAHA.122.029085","DOI":"10.1161\/JAHA.122.029085"},{"key":"3434_CR15","unstructured":"Hendrycks D, Mu N, Cubuk ED et al (2019) AugMix: a simple data processing method to improve robustness and uncertainty. In: The eighth international conference on learning representations"},{"key":"3434_CR16","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1117\/12.2513089","volume":"10949","author":"JC Reinhold","year":"2019","unstructured":"Reinhold JC, Dewey BE, Carass A, Prince JL (2019) Evaluating the impact of intensity normalization on MR image synthesis. Proc SPIE Int Soc Opt Eng 10949:126. https:\/\/doi.org\/10.1117\/12.2513089","journal-title":"Proc SPIE Int Soc Opt Eng"},{"key":"3434_CR17","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1213\/ANE.0000000000005247","volume":"132","author":"P Schober","year":"2021","unstructured":"Schober P, Vetter TR (2021) Logistic regression in medical research. Anesth Analg 132:365\u2013366. https:\/\/doi.org\/10.1213\/ANE.0000000000005247","journal-title":"Anesth Analg"},{"key":"3434_CR18","doi-asserted-by":"crossref","unstructured":"Blotwijk S, Raets C, Barbe K (2023) Exploratory study on Evolutionary Random Forests for Classification in Medical Datasets. In: 2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc","DOI":"10.1109\/MeMeA57477.2023.10171908"},{"key":"3434_CR19","doi-asserted-by":"publisher","unstructured":"Vlachas C, Damianos L, Gousetis N et al (2022) Random forest classification algorithm for medical industry data. SHS Web Conferences 139(03008). https:\/\/doi.org\/10.1051\/SHSCONF\/202213903008","DOI":"10.1051\/SHSCONF\/202213903008"},{"key":"3434_CR20","unstructured":"Bergstra J, Bardenet R, Bengio Y, K\u00e9gl B (2011) Algorithms for Hyper-Parameter Optimization. In: NIPS\u201911: Proceedings of the 24th International Conference on Neural Information Processing Systems. pp 2546\u20132554"},{"key":"3434_CR21","unstructured":"Falkner S, Klein A, Hutter F (2018) BOHB: robust and efficient hyperparameter optimization at scale. In: Proceedings of the 35th international conference on machine learning. Stockholm, Sweden"},{"key":"3434_CR22","unstructured":"Paszke A, Gross S, Massa F et al (2019) PyTorch: an imperative style. High-Performance Deep Learning Library. In: Advances in neural information processing systems. Curran Associates, Inc."},{"key":"3434_CR23","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"3434_CR24","doi-asserted-by":"publisher","unstructured":"Akiba T, Sano S, Yanase T et al (2019) Optuna: A Next-generation Hyperparameter Optimization Framework. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2623\u20132631. https:\/\/doi.org\/10.1145\/3292500.3330701","DOI":"10.1145\/3292500.3330701"},{"key":"3434_CR25","doi-asserted-by":"publisher","unstructured":"Cardoso MJ, Li W, Brown R et al (2022) MONAI: an open-source framework for deep learning in healthcare. https:\/\/doi.org\/10.48550\/arXiv.2211.02701","DOI":"10.48550\/arXiv.2211.02701"},{"key":"3434_CR26","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M et al (2018) MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition. IEEE, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"3434_CR27","unstructured":"Luo L, Xiong Y, Liu Y, Sun X (2019) Adaptive gradient methods with dynamic bound of learning rate. 7th International Conference on Learning Representations, ICLR 2019 1\u201319"},{"key":"3434_CR28","unstructured":"Zhuang J, Tang T, Ding Y et al (2020) AdaBelief optimizer: adapting stepsizes by the belief in observed gradients. In: Advances in neural information processing systems. Curran Associates, Inc., pp 18795\u201318806"},{"key":"3434_CR29","doi-asserted-by":"publisher","unstructured":"Loshchilov I, Hutter F (2016) SGDR: stochastic gradient descent with warm restarts. https:\/\/doi.org\/10.48550\/arXiv.1608.03983","DOI":"10.48550\/arXiv.1608.03983"},{"key":"3434_CR30","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1016\/J.IJCARD.2016.10.068","volume":"227","author":"Z Zhang","year":"2017","unstructured":"Zhang Z, Wang M, Yang Z et al (2017) Noninvasive prediction of pulmonary artery pressure and vascular resistance by using cardiac magnetic resonance indices. Int J Cardiol 227:915\u2013922. https:\/\/doi.org\/10.1016\/J.IJCARD.2016.10.068","journal-title":"Int J Cardiol"},{"key":"3434_CR31","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1097\/RTI.0000000000000079","volume":"29","author":"AJ Swift","year":"2014","unstructured":"Swift AJ, Wild JM, Nagle SK et al (2014) Quantitative magnetic resonance imaging of pulmonary hypertension: a practical approach to the current state of the Art. J Thorac Imaging 29:68\u201379. https:\/\/doi.org\/10.1097\/RTI.0000000000000079","journal-title":"J Thorac Imaging"},{"key":"3434_CR32","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.1016\/J.JCMG.2013.01.013","volume":"6","author":"AJ Swift","year":"2013","unstructured":"Swift AJ, Rajaram S, Hurdman J et al (2013) Noninvasive Estimation of PA pressure, flow, and resistance with CMR imaging: derivation and prospective validation study from the ASPIRE registry. JACC Cardiovasc Imaging 6:1036\u20131047. https:\/\/doi.org\/10.1016\/J.JCMG.2013.01.013","journal-title":"JACC Cardiovasc Imaging"},{"key":"3434_CR33","doi-asserted-by":"publisher","first-page":"1438","DOI":"10.1007\/S00330-017-5143-Y","volume":"28","author":"CS Johns","year":"2018","unstructured":"Johns CS, Rajaram S, Capener DA et al (2018) Non-invasive methods for estimating mPAP in COPD using cardiovascular magnetic resonance imaging. Eur Radiol 28:1438\u20131448. https:\/\/doi.org\/10.1007\/S00330-017-5143-Y","journal-title":"Eur Radiol"},{"key":"3434_CR34","doi-asserted-by":"publisher","unstructured":"Li Y, Weber J, Dang O et al (2024) Deep learning assessment of pulmonary artery pressure with CMR flow imaging. J Am Coll Cardiol 83:1376. https:\/\/doi.org\/10.1016\/S0735-1097(24)03366-7","DOI":"10.1016\/S0735-1097(24)03366-7"},{"key":"3434_CR35","doi-asserted-by":"publisher","unstructured":"Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. https:\/\/doi.org\/10.1007\/978-0-387-84858-7","DOI":"10.1007\/978-0-387-84858-7"},{"key":"3434_CR36","doi-asserted-by":"publisher","unstructured":"Monaghan TF, Rahman SN, Agudelo CW et al (2021) Foundational Statistical Principles in Medical Research: Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value. Medicina 2021, Vol 57, Page 503 57:503. https:\/\/doi.org\/10.3390\/MEDICINA57050503","DOI":"10.3390\/MEDICINA57050503"},{"key":"3434_CR37","doi-asserted-by":"publisher","unstructured":"Singh Y, Villaescusa JU, da Cruz EM et al (2020) Recommendations for hemodynamic monitoring for critically ill children\u2014expert consensus statement issued by the cardiovascular dynamics section of the European Society of Paediatric and Neonatal Intensive Care (ESPNIC). Crit Care 24. https:\/\/doi.org\/10.1186\/s13054-020-03326-2","DOI":"10.1186\/s13054-020-03326-2"},{"key":"3434_CR38","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/j.echo.2010.03.019","volume":"23","author":"L Lopez","year":"2010","unstructured":"Lopez L, Colan SD, Frommelt PC et al (2010) Recommendations for quantification methods during the performance of a pediatric echocardiogram: A report from the pediatric measurements writing group of the American society of echocardiography pediatric and congenital heart disease Council. J Am Soc Echocardiogr 23:465\u2013495","journal-title":"J Am Soc Echocardiogr"},{"key":"3434_CR39","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1148\/radiographics.22.3.g02ma11651","volume":"22","author":"J Lotz","year":"2002","unstructured":"Lotz J, Meier C, Leppert A, Galanski M (2002) Cardiovascular flow measurement with Phase-Contrast MR imaging: basic facts and implementation. Radiographics 22:651\u2013671. https:\/\/doi.org\/10.1148\/radiographics.22.3.g02ma11651","journal-title":"Radiographics"},{"key":"3434_CR40","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1016\/j.mric.2023.04.005","volume":"31","author":"A Maroun","year":"2023","unstructured":"Maroun A, Quinn S, Dushfunian D et al (2023) Clinical applications of Four-Dimensional flow MRI. Magn Reson Imaging Clin N Am 31:451\u2013460. https:\/\/doi.org\/10.1016\/j.mric.2023.04.005","journal-title":"Magn Reson Imaging Clin N Am"}],"container-title":["The International Journal of Cardiovascular Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10554-025-03434-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10554-025-03434-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10554-025-03434-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,14]],"date-time":"2025-06-14T03:44:52Z","timestamp":1749872692000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10554-025-03434-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,14]]},"references-count":40,"alternative-id":["3434"],"URL":"https:\/\/doi.org\/10.1007\/s10554-025-03434-6","relation":{},"ISSN":["1875-8312"],"issn-type":[{"value":"1875-8312","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,14]]},"assertion":[{"value":"14 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 June 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":"This is a retrospective study. The Medical Ethics Review Board of the UMCG (METc 2008.009) has approved the ongoing registry.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"There is no concern about identifying information.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}