{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T12:40:27Z","timestamp":1791549627769,"version":"4.3.4"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,12,10]],"date-time":"2021-12-10T00:00:00Z","timestamp":1639094400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:p>\n                    <jats:bold>Background:<\/jats:bold>\n                    Bone cement leakage is a common complication of percutaneous vertebroplasty and it could be life-threatening to some extent. The aim of this study was to develop a machine learning model for predicting the risk of cement leakage in patients with osteoporotic vertebral compression fractures undergoing percutaneous vertebroplasty. Furthermore, we developed an online calculator for clinical application.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods:<\/jats:bold>\n                    This was a retrospective study including 385 patients, who had osteoporotic vertebral compression fracture disease and underwent surgery at the Department of Spine Surgery, Liuzhou People's Hospital from June 2016 to June 2018. Combing the patient's clinical characteristics variables, we applied six machine learning (ML) algorithms to develop the predictive models, including logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision Tree (DT) and Multilayer perceptron (MLP), which could predict the risk of bone cement leakage. We tested the results with ten-fold cross-validation, which calculated the Area Under Curve (AUC) of the six models and selected the model with the highest AUC as the excellent performing model to build the web calculator.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results:<\/jats:bold>\n                    The results showed that Injection volume of bone cement, Surgery time and Multiple vertebral fracture were all independent predictors of bone cement leakage by using multivariate logistic regression analysis in the 385 observation subjects. Furthermore, Heatmap revealed the relative proportions of the 15 clinical variables. In bone cement leakage prediction, the AUC of the six ML algorithms ranged from 0.633 to 0.898, while the RF model had an AUC of 0.898 and was used as the best performing ML Web calculator (\n                    <jats:ext-link>https:\/\/share.streamlit.io\/liuwencai0\/pvp_leakage\/main\/pvp_leakage<\/jats:ext-link>\n                    ) was developed to estimate the risk of bone cement leakage that each patient undergoing vertebroplasty.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusion:<\/jats:bold>\n                    It achieved a good prediction for the occurrence of bone cement leakage with our ML model. The Web calculator concluded based on RF model can help orthopedist to make more individual and rational clinical strategies.\n                  <\/jats:p>","DOI":"10.3389\/fpubh.2021.812023","type":"journal-article","created":{"date-parts":[[2021,12,10]],"date-time":"2021-12-10T00:43:20Z","timestamp":1639097000000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":55,"title":["Machine Learning Applications for the Prediction of Bone Cement Leakage in Percutaneous Vertebroplasty"],"prefix":"10.3389","volume":"9","author":[{"given":"Wenle","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaming","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wencai","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanying","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shibin","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaohui","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruogu","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengliang","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,12,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1055\/s-2003-812630","article-title":"Vertebroplasty: cement leakage into the disc increases the risk of new fracture of adjacent vertebral body","volume":"25","author":"Lin","year":"2004","journal-title":"Am J Neuroradiol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1097\/01.BRS.0000131211.87594.B0","article-title":"Acute respiratory distress syndrome associated with pulmonary cement embolism following percutaneous vertebroplasty with polymethylmethacrylate","volume":"29","author":"Yoo","year":"2004","journal-title":"Spine."},{"key":"B3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TCYB.2020.2977267","article-title":"Nonparametric Bayesian Prior Inducing Deep Network for Automatic Detection of Cognitive Status","volume":"51","author":"Wu","year":"2020","journal-title":"IEEE Trans Cybern."},{"key":"B4","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.comcom.2020.07.032","article-title":"N-sanitization: a semantic privacy-preserving framework for unstructured medical datasets","volume":"161","author":"Iwendi","year":"2020","journal-title":"Comput Commun."},{"key":"B5","doi-asserted-by":"publisher","first-page":"577537","DOI":"10.3389\/fendo.2020.577537","article-title":"Machine learning algorithms for the prediction of central lymph node metastasis in patients with papillary thyroid cancer","volume":"11","author":"Wu","year":"2020","journal-title":"Front Endocrinol."},{"key":"B6","doi-asserted-by":"publisher","first-page":"357","DOI":"10.3389\/fpubh.2020.00357","article-title":"COVID-19 patient health prediction using boosted random forest algorithm","volume":"8","author":"Iwendi","year":"2020","journal-title":"Front Public Health."},{"key":"B7","doi-asserted-by":"publisher","first-page":"9255","DOI":"10.12659\/MSM.915312","article-title":"Risk factors of secondary vertebral compression fracture after percutaneous vertebroplasty or kyphoplasty: a retrospective study of 650 patients","volume":"25","author":"Chen","year":"2019","journal-title":"Med Sci Monit."},{"key":"B8","doi-asserted-by":"publisher","first-page":"34","DOI":"10.3389\/fmed.2019.00034","article-title":"From big data to precision medicine","volume":"6","author":"Hulsen","year":"2019","journal-title":"Front Med."},{"key":"B9","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.neucom.2020.04.012","article-title":"Fully memristive spiking-neuron learning framework and its applications on pattern recognition and edge detection","volume":"403","author":"Tang","year":"2020","journal-title":"Neurocomputing."},{"key":"B10","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1186\/s41747-018-0061-6","article-title":"Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine","volume":"2","author":"Pesapane","year":"2018","journal-title":"Eur Radiol."},{"key":"B11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1049\/cp.2012.0610","article-title":"Enhanced security technique for wireless sensor network nodes","author":"Iwendi","year":"2012","journal-title":"IET Conf Wireless Sensor Systems."},{"key":"B12","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.spinee.2019.09.007","article-title":"Development of an unsupervised machine learning algorithm for the prognostication of walking ability in spinal cord injury patients","volume":"20","author":"Devries","year":"2019","journal-title":"Spine J."},{"key":"B13","doi-asserted-by":"publisher","first-page":"e1002699","DOI":"10.1371\/journal.pmed.1002699","article-title":"Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet","volume":"15","author":"Bien","year":"2018","journal-title":"PLoS Med."},{"key":"B14","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1109\/TCDS.2019.2963476","article-title":"Detecting fatigue status of pilots based on deep learning network using eeg signals","volume":"13","author":"Wu","year":"2020","journal-title":"IEEE Trans Cogn Dev Syst."},{"key":"B15","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1109\/CSNT48778.2020.9115741","article-title":"Improved advance encryption standard with a privacy database structure for IoT nodes","volume":"13","author":"Anajemba","year":"2020","journal-title":"2020 IEEE 9th Int Conf Commun Syst Netw Technol."},{"key":"B16","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.neucom.2018.12.049","article-title":"A hardware friendly unsupervised memristive neural network with weight sharing mechanism","volume":"332","author":"Tang","year":"2019","journal-title":"Neurocomputing."},{"key":"B17","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1109\/TCDS.2020.3003377","article-title":"A multilayer neural network merging image preprocessing and pattern recognition by integrating diffusion and drift memristors","volume":"13","author":"Tang","year":"2020","journal-title":"IEEE Trans Cogn Dev Syst."},{"key":"B18","doi-asserted-by":"publisher","first-page":"334","DOI":"10.14245\/kjs.2012.9.4.334","article-title":"Clinical effects and radiological results of vertebroplasty: over a 2-year follow-up period","volume":"9","author":"Nam","year":"2012","journal-title":"Korean J Spine."},{"key":"B19","doi-asserted-by":"publisher","first-page":"1272","DOI":"10.1007\/s00586-009-1037-2","article-title":"Clinical investigations of polymethylmethacrylate cement viscosity during vertebroplasty and related in vitro measurements","volume":"18","author":"Boger","year":"2009","journal-title":"Eur Spine J."},{"key":"B20","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1016\/j.spinee.2011.07.027","article-title":"Cement leakage in percutaneous vertebroplasty for osteoporotic vertebral compression fractures: identification of risk factors","volume":"11","author":"Nieuwenhuijse","year":"2011","journal-title":"Spine J."},{"key":"B21","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.pjnns.2014.11.005","article-title":"Percutaneous balloon kyphoplasty in the treatment of painful vertebral compression fractures: effect on local kyphosis and one-year outcomes in pain and disability","volume":"49","author":"Zapalowicz","year":"2015","journal-title":"Neurol Neurochir Pol."},{"key":"B22","doi-asserted-by":"publisher","first-page":"331","DOI":"10.3238\/arztebl.2011.0331","article-title":"Percutaneous vertebroplasty in vertebral compression fractures of benign or malignant origin: a prospective study of 1188 patients with follow-up of 12 months","volume":"108","author":"Mpotsaris","year":"2011","journal-title":"Dtsch Arzteblt Int."},{"key":"B23","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.spinee.2010.11.011","article-title":"Vertebroplasty versus conservative treatment in acute osteoporotic vertebral compression fractures (Vertos II): an open-label randomised trial.","volume":"11","author":"Klazen","year":"2011","journal-title":"Spine J."},{"key":"B24","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1177\/0004563214528448","article-title":"Romosozumab in postmenopausal women with low bone mineral density","volume":"354","author":"Clung","year":"2014","journal-title":"Dig World Core Med J."},{"key":"B25","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1007\/s00264-015-3102-2","article-title":"Risk factors for bone cement leakage in vertebral augmentation","volume":"40","author":"Guo","year":"2019","journal-title":"Orthop J China."},{"key":"B26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00586-021-07064-z","article-title":"Establishment and validation of a nomogram and web calculator for the risk of new vertebral compression fractures and cement leakage after percutaneous vertebroplasty in patients with osteoporotic vertebral compression fractures","volume":"30","author":"Li","year":"2021","journal-title":"European Spine J."},{"key":"B27","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1177\/2192568218797090","article-title":"Predictive factors for discharge destination following posterior lumbar spinal fusion: a Canadian spine outcome and research network (CSORN) study","volume":"9","author":"Morcos","year":"2018","journal-title":"Glob Spine J."}],"container-title":["Frontiers in Public Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2021.812023\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,10]],"date-time":"2021-12-10T00:43:23Z","timestamp":1639097003000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2021.812023\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,10]]},"references-count":27,"alternative-id":["10.3389\/fpubh.2021.812023"],"URL":"https:\/\/doi.org\/10.3389\/fpubh.2021.812023","relation":{},"ISSN":["2296-2565"],"issn-type":[{"value":"2296-2565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,10]]},"article-number":"812023"}}