{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T21:40:29Z","timestamp":1793482829841,"version":"4.5.0"},"reference-count":105,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,6,29]],"date-time":"2024-06-29T00:00:00Z","timestamp":1719619200000},"content-version":"am","delay-in-days":302,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003593","name":"CNPq","doi-asserted-by":"publisher","award":["307471\/2021-7"],"award-info":[{"award-number":["307471\/2021-7"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01 DE031832"],"award-info":[{"award-number":["R01 DE031832"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"FAPESP","doi-asserted-by":"publisher","award":["2020\/05231-4"],"award-info":[{"award-number":["2020\/05231-4"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Tribology International"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1016\/j.triboint.2023.108735","type":"journal-article","created":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T20:53:37Z","timestamp":1687812817000},"page":"108735","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":18,"special_numbering":"C","title":["Early predicting tribocorrosion rate of dental implant titanium materials using random forest machine learning models"],"prefix":"10.1016","volume":"187","author":[{"given":"Remya","family":"Ampadi Ramachandran","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valentim A.R.","family":"Bar\u00e3o","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Didem","family":"Ozevin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cortino","family":"Sukotjo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Srinivasa","family":"Pai .P","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mathew","family":"Mathew","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.triboint.2023.108735_bib1","unstructured":"Artificial Intelligence (AI) vs Machine Learning, ScienceLogic. \u3008https:\/\/sciencelogic.com\/glossary\/artificial-intelligence-ai-vs-machine-learning\u3009 (accessed Feb. 07, 2022)."},{"key":"10.1016\/j.triboint.2023.108735_bib2","unstructured":"AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What\u2019s the Difference?, Jan. 19, 2022. \u3008https:\/\/www.ibm.com\/cloud\/blog\/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks\u3009 (accessed Feb. 07, 2022)."},{"issue":"1","key":"10.1016\/j.triboint.2023.108735_bib3","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s11517-018-1819-y","article-title":"A novel fused convolutional neural network for biomedical image classification","volume":"vol. 57","author":"Pang","year":"2019","journal-title":"Med Biol Eng Comput"},{"key":"10.1016\/j.triboint.2023.108735_bib4","unstructured":"H. Barzekar and Z. Yu, C-Net: A Reliable Convolutional Neural Network for Biomedical Image Classification, ArXiv201100081 Cs Eess, Oct. 2020, Accessed: Jun. 25, 2021. [Online]. Available: http:\/\/arxiv.org\/abs\/2011.00081."},{"key":"10.1016\/j.triboint.2023.108735_bib5","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.compmedimag.2016.07.004","article-title":"Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data","volume":"vol. 57","author":"Sun","year":"2017","journal-title":"Comput Med Imaging Graph"},{"key":"10.1016\/j.triboint.2023.108735_bib6","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.jbi.2017.05.002","article-title":"Character-level neural network for biomedical named entity recognition","volume":"vol. 70","author":"Gridach","year":"2017","journal-title":"J Biomed Inform"},{"issue":"5","key":"10.1016\/j.triboint.2023.108735_bib7","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1186\/s12920-017-0316-8","article-title":"Disease named entity recognition from biomedical literature using a novel convolutional neural network","volume":"vol. 10","author":"Zhao","year":"2017","journal-title":"BMC Med Genom"},{"key":"10.1016\/j.triboint.2023.108735_bib8","series-title":"Proceedings of the 6th ACM Conference on Bioinformatics","first-page":"258","article-title":"Convolutional neural networks for biomedical text classification: application in indexing biomedical articles","author":"Rios","year":"2015"},{"key":"10.1016\/j.triboint.2023.108735_bib9","doi-asserted-by":"crossref","unstructured":"J. Wang, H. Li, Y. An, H. Lin, and Z. Yang, Biomedical event trigger detection based on convolutional neural network, Int. J. Data Min. Bioinforma., Jun. 2016, Accessed: Jun. 25, 2021. [Online]. Available: https:\/\/www.inderscienceonline.com\/doi\/abs\/10.1504\/IJDMB.2016.077067.","DOI":"10.1504\/IJDMB.2016.077067"},{"issue":"3","key":"10.1016\/j.triboint.2023.108735_bib10","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1109\/TCBB.2018.2806438","article-title":"A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data","volume":"vol. 16","author":"Sun","year":"2019","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"21","key":"10.1016\/j.triboint.2023.108735_bib11","doi-asserted-by":"crossref","first-page":"15481","DOI":"10.1007\/s11042-019-7525-4","article-title":"Breast cancer multi-classification through deep neural network and hierarchical classification approach","volume":"vol. 79","author":"Murtaza","year":"2020","journal-title":"Multimed Tools Appl"},{"key":"10.1016\/j.triboint.2023.108735_bib12","doi-asserted-by":"crossref","first-page":"46278","DOI":"10.1109\/ACCESS.2019.2902252","article-title":"Brain MRI image classification for cancer detection using deep wavelet autoencoder-based deep neural network","volume":"vol. 7","author":"Kumar Mallick","year":"2019","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.triboint.2023.108735_bib13","doi-asserted-by":"crossref","first-page":"123","DOI":"10.17148\/IJARCCE.2017.6125","article-title":"Brain cancer classification based on features and artificial neural network","volume":"vol. 6","author":"El Abbadi","year":"2017","journal-title":"IJARCCE"},{"key":"10.1016\/j.triboint.2023.108735_bib14","unstructured":"H. Chang, Skin cancer reorganization and classification with deep neural network, p. 6."},{"key":"10.1016\/j.triboint.2023.108735_bib15","article-title":"Artificial neural network based detection for instance, skin cancer","author":"Jaleel","year":"2012","journal-title":"Int J Adv Res Electr Electron Instrum Eng"},{"key":"10.1016\/j.triboint.2023.108735_bib16","article-title":"Images data practices for semantic segmentation of breast cancer using deep neural network","author":"Ahmed","year":"2020","journal-title":"J Ambient Intell Humaniz Comput, Jan"},{"issue":"6","key":"10.1016\/j.triboint.2023.108735_bib17","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1007\/s00521-012-1134-8","article-title":"Probabilistic neural network for breast cancer classification","volume":"vol. 23","author":"Azar","year":"2013","journal-title":"Neural Comput Appl"},{"key":"10.1016\/j.triboint.2023.108735_bib18","doi-asserted-by":"crossref","first-page":"002570","DOI":"10.1109\/SMC.2016.7844626","article-title":"Combining deep neural network and traditional image features to improve survival prediction accuracy for lung cancer patients from diagnostic CT","author":"Paul","year":"2016","journal-title":"2016 IEEE Int Conf Syst, Man, Cybern (SMC)"},{"issue":"17","key":"10.1016\/j.triboint.2023.108735_bib19","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ab326a","article-title":"Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features","volume":"vol. 64","author":"Li","year":"2019","journal-title":"Phys Med Biol"},{"issue":"6","key":"10.1016\/j.triboint.2023.108735_bib20","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1055\/a-0855-3532","article-title":"A deep neural network improves endoscopic detection of early gastric cancer without blind spots","volume":"vol. 51","author":"Wu","year":"2019","journal-title":"Endoscopy"},{"issue":"1","key":"10.1016\/j.triboint.2023.108735_bib21","first-page":"15","article-title":"Classification and Prediction of Gastric Cancer from Saliva Diagnosis using Artificial Neural Network","volume":"vol. 29","author":"Aslam","year":"2021","journal-title":"Eng. Lett."},{"issue":"10","key":"10.1016\/j.triboint.2023.108735_bib22","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1038\/sj.bdj.4808932","article-title":"The use of artificial intelligence to identify people at risk of oral cancer and precancer","volume":"vol. 179","author":"Speight","year":"1995","journal-title":"Br Dent J"},{"issue":"2","key":"10.1016\/j.triboint.2023.108735_bib23","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1177\/0272989X9601600207","article-title":"Performance of a neural network trained to make third-molar treatment-planning decisions","volume":"vol. 16","author":"Brickley","year":"1996","journal-title":"Med Decis Mak"},{"key":"10.1016\/j.triboint.2023.108735_bib24","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.compbiomed.2016.11.003","article-title":"Classification of teeth in cone-beam CT using deep convolutional neural network","volume":"vol. 80","author":"Miki","year":"2017","journal-title":"Comput Biol Med"},{"issue":"4","key":"10.1016\/j.triboint.2023.108735_bib25","doi-asserted-by":"crossref","first-page":"20180051","DOI":"10.1259\/dmfr.20180051","article-title":"Tooth detection and numbering in panoramic radiographs using convolutional neural networks","volume":"vol. 48","author":"Tuzoff","year":"2019","journal-title":"Dentomaxillofacial Radio"},{"key":"10.1016\/j.triboint.2023.108735_bib26","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.compmedimag.2018.07.001","article-title":"An effective teeth recognition method using label tree with cascade network structure","volume":"vol. 68","author":"Zhang","year":"2018","journal-title":"Comput Med Imaging Graph"},{"issue":"1","key":"10.1016\/j.triboint.2023.108735_bib27","doi-asserted-by":"crossref","first-page":"3840","DOI":"10.1038\/s41598-019-40414-y","article-title":"A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films","volume":"vol. 9","author":"Chen","year":"2019","journal-title":"Sci Rep"},{"issue":"11","key":"10.1016\/j.triboint.2023.108735_bib28","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1177\/0022034519871884","article-title":"Caries detection with near-infrared transillumination using deep learning","volume":"vol. 98","author":"Casalegno","year":"2019","journal-title":"J Dent Res"},{"key":"10.1016\/j.triboint.2023.108735_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.jdent.2019.103260","article-title":"Deep learning for caries lesion detection in near-infrared light transillumination images: A pilot study","volume":"vol. 92","author":"Schwendicke","year":"2020","journal-title":"J Dent"},{"key":"10.1016\/j.triboint.2023.108735_bib30","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.jdent.2018.07.015","article-title":"Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm","volume":"vol. 77","author":"Lee","year":"2018","journal-title":"J Dent"},{"issue":"7","key":"10.1016\/j.triboint.2023.108735_bib31","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1016\/j.joen.2019.03.016","article-title":"Deep learning for the radiographic detection of apical lesions","volume":"vol. 45","author":"Ekert","year":"2019","journal-title":"J Endod"},{"issue":"4","key":"10.1016\/j.triboint.2023.108735_bib32","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.oooo.2019.05.014","article-title":"Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique","volume":"vol. 128","author":"Ariji","year":"2019","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radio"},{"issue":"3","key":"10.1016\/j.triboint.2023.108735_bib33","doi-asserted-by":"crossref","first-page":"236","DOI":"10.4258\/hir.2018.24.3.236","article-title":"Application of convolutional neural network in the diagnosis of jaw tumors","volume":"vol. 24","author":"Poedjiastoeti","year":"2018","journal-title":"Healthc Inform Res"},{"issue":"5","key":"10.1016\/j.triboint.2023.108735_bib34","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.oooo.2018.10.002","article-title":"Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence","volume":"vol. 127","author":"Ariji","year":"2019","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radio"},{"issue":"12","key":"10.1016\/j.triboint.2023.108735_bib35","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0207493","article-title":"Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities","volume":"vol. 13","author":"Uthoff","year":"2018","journal-title":"PLOS ONE"},{"key":"10.1016\/j.triboint.2023.108735_bib36","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.pdpdt.2019.05.008","article-title":"Deep convolutional neural networks for tongue squamous cell carcinoma classification using Raman spectroscopy","volume":"vol. 26","author":"Yu","year":"2019","journal-title":"Photo Photodyn Ther"},{"issue":"1","key":"10.1016\/j.triboint.2023.108735_bib37","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1097\/RLI.0000000000000503","article-title":"Deep learning in diagnosis of maxillary sinusitis using conventional radiography","volume":"vol. 54","author":"Kim","year":"2019","journal-title":"Invest Radiol"},{"issue":"3","key":"10.1016\/j.triboint.2023.108735_bib38","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s11282-018-0363-7","article-title":"Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography","volume":"vol. 35","author":"Murata","year":"2019","journal-title":"Oral Radio"},{"issue":"1","key":"10.1016\/j.triboint.2023.108735_bib39","doi-asserted-by":"crossref","first-page":"20170344","DOI":"10.1259\/dmfr.20170344","article-title":"Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study","volume":"vol. 48","author":"Lee","year":"2019","journal-title":"Dentomaxillofacial Radio"},{"issue":"1","key":"10.1016\/j.triboint.2023.108735_bib40","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1186\/s12903-018-0591-6","article-title":"Predicting oral malodour based on the microbiota in saliva samples using a deep learning approach","volume":"vol. 18","author":"Nakano","year":"2018","journal-title":"BMC Oral Health"},{"issue":"1","key":"10.1016\/j.triboint.2023.108735_bib41","doi-asserted-by":"crossref","first-page":"8495","DOI":"10.1038\/s41598-019-44839-3","article-title":"Deep learning for the radiographic detection of periodontal bone loss","volume":"vol. 9","author":"Krois","year":"2019","journal-title":"Sci Rep"},{"issue":"1","key":"10.1016\/j.triboint.2023.108735_bib42","doi-asserted-by":"crossref","first-page":"33581","DOI":"10.1038\/srep33581","article-title":"Fully automatic system for accurate localisation and analysis of cephalometric landmarks in lateral cephalograms","volume":"vol. 6","author":"Lindner","year":"2016","journal-title":"Sci Rep"},{"issue":"6","key":"10.1016\/j.triboint.2023.108735_bib43","doi-asserted-by":"crossref","first-page":"903","DOI":"10.2319\/022019-127.1","article-title":"Automated identification of cephalometric landmarks: part 1\u2014comparisons between the latest deep-learning methods YOLOV3 and SSD","volume":"vol. 89","author":"Park","year":"2019","journal-title":"Angle Orthod"},{"issue":"1","key":"10.1016\/j.triboint.2023.108735_bib44","doi-asserted-by":"crossref","first-page":"69","DOI":"10.2319\/022019-129.1","article-title":"Automated identification of cephalometric landmarks: part 2-might it be better than human?","volume":"vol. 90","author":"Hwang","year":"2019","journal-title":"Angle Orthod"},{"issue":"10","key":"10.1016\/j.triboint.2023.108735_bib45","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1177\/0022034519865187","article-title":"Minimally invasive approach for diagnosing TMJ osteoarthritis","volume":"vol. 98","author":"Shoukri","year":"2019","journal-title":"J Dent Res"},{"issue":"3","key":"10.1016\/j.triboint.2023.108735_bib46","doi-asserted-by":"crossref","first-page":"20180218","DOI":"10.1259\/dmfr.20180218","article-title":"A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography","volume":"vol. 48","author":"Hiraiwa","year":"2019","journal-title":"Dentomaxillofacial Radio"},{"key":"10.1016\/j.triboint.2023.108735_bib47","doi-asserted-by":"crossref","DOI":"10.1016\/j.corsci.2020.108697","article-title":"Towards understanding and prediction of atmospheric corrosion of an Fe\/Cu corrosion sensor via machine learning","volume":"vol. 170","author":"Pei","year":"2020","journal-title":"Corros Sci"},{"issue":"10","key":"10.1016\/j.triboint.2023.108735_bib48","doi-asserted-by":"crossref","first-page":"5624","DOI":"10.1021\/acs.jpcc.9b09538","article-title":"Elucidating structure\u2013property relationships in aluminum alloy corrosion inhibitors by machine learning","volume":"vol. 124","author":"Galv\u00e3o","year":"2020","journal-title":"J Phys Chem C"},{"key":"10.1016\/j.triboint.2023.108735_bib49","doi-asserted-by":"crossref","DOI":"10.1016\/j.apsusc.2020.145612","article-title":"Prediction of corrosion inhibition efficiency of pyridines and quinolines on an iron surface using machine learning-powered quantitative structure-property relationships","volume":"vol. 512","author":"Ser","year":"2020","journal-title":"Appl Surf Sci"},{"key":"10.1016\/j.triboint.2023.108735_bib50","doi-asserted-by":"crossref","DOI":"10.1016\/j.matdes.2020.109326","article-title":"Improvement of the machine learning-based corrosion rate prediction model through the optimization of input features","volume":"vol. 198","author":"Diao","year":"2021","journal-title":"Mater Des"},{"issue":"1","key":"10.1016\/j.triboint.2023.108735_bib51","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1080\/14686996.2020.1746196","article-title":"Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach","volume":"vol. 21","author":"Yan","year":"2020","journal-title":"Sci Technol Adv Mater"},{"key":"10.1016\/j.triboint.2023.108735_bib52","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2020.108141","article-title":"Ensemble machine learning model for corrosion initiation time estimation of embedded steel reinforced self-compacting concrete","volume":"vol. 165","author":"Salami","year":"2020","journal-title":"Measurement"},{"key":"10.1016\/j.triboint.2023.108735_bib53","doi-asserted-by":"crossref","DOI":"10.1155\/2019\/8097213","article-title":"Image processing-based detection of pipe corrosion using texture analysis and metaheuristic-optimized machine learning approach","volume":"vol. 2019","author":"Hoang","year":"2019","journal-title":"Comput Intell Neurosci"},{"key":"10.1016\/j.triboint.2023.108735_bib54","doi-asserted-by":"crossref","DOI":"10.1155\/2020\/6765274","article-title":"Image processing-based pitting corrosion detection using metaheuristic optimized multilevel image thresholding and machine-learning approaches","volume":"vol. 2020","author":"Hoang","year":"2020","journal-title":"Math Probl Eng"},{"key":"10.1016\/j.triboint.2023.108735_bib55","first-page":"186","article-title":"Machine learning in the problem of recognition of pitting corrosion on aluminum surfaces","author":"Enikeev","year":"2018","journal-title":"Data Sci"},{"key":"10.1016\/j.triboint.2023.108735_bib56","unstructured":"S. Zukhrufany, The Utilization of Supervised Machine Learning in Predicting Corrosion to Support Preventing Pipelines Leakage in Oil and Gas Industry, Jun. 2018, Accessed: Sep. 24, 2021. Online]. Available: https:\/\/uis.brage.unit.no\/uis-xmlui\/handle\/11250\/2565865."},{"key":"10.1016\/j.triboint.2023.108735_bib57","doi-asserted-by":"crossref","unstructured":"G. De Masi, M. Gentile, R. Vichi, R. Bruschi, and G. Gabetta, Machine learning approach to corrosion assessment in subsea pipelines, in OCEANS 2015 - Genova, May 2015, pp. 1\u20136. doi: 10.1109\/OCEANS-Genova.2015.7271592.","DOI":"10.1109\/OCEANS-Genova.2015.7271592"},{"key":"10.1016\/j.triboint.2023.108735_bib58","doi-asserted-by":"crossref","unstructured":"M. Zounemat-Kermani, Alizamir, M., Yaseen, Z.M. and Hinkelmann, R., Concrete corrosion in wastewater systems: Prediction and sensitivity analysis using advanced extreme learning machine, Front. Struct. Civ. Eng., vol. 15, no. 2, Art. no. 2, doi: 10.1007\/s11709\u2013021-0697\u20139.","DOI":"10.1007\/s11709-021-0697-9"},{"issue":"3","key":"10.1016\/j.triboint.2023.108735_bib59","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1007\/s10712-019-09558-4","article-title":"A machine learning-based data fusion approach for improved corrosion testing","volume":"vol. 41","author":"V\u00f6lker","year":"2020","journal-title":"Surv Geophys"},{"key":"10.1016\/j.triboint.2023.108735_bib60","unstructured":"C. V\u00f6lker, S. Kruschwitz, G. Ebell, and J. Shen, Towards Data Based Corrosion Analysis of Concrete with Supervised Machine Learning. 2018."},{"key":"10.1016\/j.triboint.2023.108735_bib61","doi-asserted-by":"crossref","DOI":"10.1016\/j.strusafe.2021.102098","article-title":"Probabilistic estimation of flexural loading capacity of existing RC structures based on observational corrosion-induced crack width distribution using machine learning","volume":"vol. 91","author":"Zhang","year":"2021","journal-title":"Struct Saf"},{"key":"10.1016\/j.triboint.2023.108735_bib62","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.electacta.2017.09.169","article-title":"Analysis of electrochemical noise data by use of recurrence quantification analysis and machine learning methods","volume":"vol. 256","author":"Hou","year":"2017","journal-title":"Electrochim Acta"},{"issue":"1","key":"10.1016\/j.triboint.2023.108735_bib63","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s40313-018-00423-0","article-title":"Identification of corrosive substances and types of corrosion through electrochemical noise using signal processing and machine learning","volume":"vol. 30","author":"Alves","year":"2019","journal-title":"J Control Autom Electr Syst"},{"key":"10.1016\/j.triboint.2023.108735_bib64","doi-asserted-by":"crossref","DOI":"10.1016\/j.msec.2021.112000","article-title":"Prediction of tribocorrosion processes in titanium-based dental implants using acoustic emission technique: Initial outcome","volume":"vol. 123","author":"Bar\u00e3o","year":"2021","journal-title":"Mater Sci Eng C"},{"key":"10.1016\/j.triboint.2023.108735_bib65","doi-asserted-by":"crossref","DOI":"10.1016\/j.jmbbm.2021.104484","article-title":"Non-invasive early detection of failure modes in total hip replacements (THR) via acoustic emission (AE)","volume":"vol. 118","author":"Lee","year":"2021","journal-title":"J Mech Behav Biomed Mater"},{"key":"10.1016\/j.triboint.2023.108735_bib66","unstructured":"Random Forests | SpringerLink. https:\/\/link.springer.com\/article\/10.1023\/a:1010933404324 (accessed Nov. 01, 2021)."},{"key":"10.1016\/j.triboint.2023.108735_bib67","unstructured":"M. Kon, Mathematics of Random Forests.\" Accessed: Mar. 25, 2022. Online]. Available: http:\/\/math.bu.edu\/people\/mkon\/MA751\/L19RandomForestMath.pdf."},{"key":"10.1016\/j.triboint.2023.108735_bib68","unstructured":"scikit-learn: machine learning in Python \u2014 scikit-learn 1.1.3 documentation. https:\/\/scikit-learn.org\/stable\/ (accessed Nov. 01, 2021)."},{"key":"10.1016\/j.triboint.2023.108735_bib69","unstructured":"Choosing the right estimator, scikit-learn. https:\/\/scikit-learn\/stable\/tutorial\/machine_learning_map\/index.html (accessed Nov. 01, 2021)."},{"key":"10.1016\/j.triboint.2023.108735_bib70","unstructured":"D. Varghese, Comparative study on Classic Machine learning Algorithms, Medium, May 10, 2019. https:\/\/towardsdatascience.com\/comparative-study-on-classic-machine-learning-algorithms-24f9ff6ab222 (accessed Mar. 25, 2022)."},{"key":"10.1016\/j.triboint.2023.108735_bib71","unstructured":"D. Varghese, Comparative Study on Classic Machine learning Algorithms, Part-2, Medium, Dec. 11, 2018. https:\/\/medium.com\/@dannymvarghese\/comparative-study-on-classic-machine-learning-algorithms-part-2\u20135ab58b683ec0 (accessed Mar. 25, 2022)."},{"key":"10.1016\/j.triboint.2023.108735_bib72","unstructured":"A. Kumar, Hold-out Method for Training Machine Learning Models, Data Analytics, Dec. 22, 2020. https:\/\/vitalflux.com\/hold-out-method-for-training-machine-learning-model\/ (accessed Nov. 02, 2021)."},{"key":"10.1016\/j.triboint.2023.108735_bib73","unstructured":"Machine Learning in Python, scikit-learn. https:\/\/scikit-learn.org\/stable\/ (accessed Nov. 01, 2021)."},{"issue":"4","key":"10.1016\/j.triboint.2023.108735_bib74","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1007\/s40735-021-00588-3","article-title":"Suitability of Ti\u2013Zr alloy for dental implants: tribocorrosion investigation","volume":"vol. 7","author":"Ampadi Ramachandran","year":"2021","journal-title":"J Bio- Tribo-Corros"},{"issue":"01","key":"10.1016\/j.triboint.2023.108735_bib75","article-title":"Performance evaluation in machine learning: the good, the bad, the ugly, and the way forward","volume":"vol. 33","author":"Flach","year":"2019","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"8","key":"10.1016\/j.triboint.2023.108735_bib77","doi-asserted-by":"crossref","DOI":"10.3390\/app9081526","article-title":"Deep learning in the biomedical applications: recent and future status","volume":"vol. 9","author":"Zemouri","year":"2019","journal-title":"Appl Sci"},{"key":"10.1016\/j.triboint.2023.108735_bib78","unstructured":"K.P. Kording, A. Benjamin, R. Farhoodi, and J.I. Glaser, The roles of machine learning in biomedical science, 2018."},{"key":"10.1016\/j.triboint.2023.108735_bib79","doi-asserted-by":"crossref","DOI":"10.1016\/j.jdent.2019.103226","article-title":"Convolutional neural networks for dental image diagnostics: a scoping review","volume":"vol. 91","author":"Schwendicke","year":"2019","journal-title":"J Dent"},{"issue":"2","key":"10.1016\/j.triboint.2023.108735_bib80","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.oooo.2020.11.003","article-title":"Applications of deep learning in dentistry","volume":"vol. 132","author":"Corbella","year":"2021","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radio"},{"issue":"7","key":"10.1016\/j.triboint.2023.108735_bib81","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1177\/0022034520915714","article-title":"Artificial intelligence in dentistry: chances and challenges","volume":"vol. 99","author":"Schwendicke","year":"2020","journal-title":"J Dent Res"},{"issue":"3","key":"10.1016\/j.triboint.2023.108735_bib82","doi-asserted-by":"crossref","DOI":"10.3390\/ma8030932","article-title":"A critical review of dental implant materials with an emphasis on titanium versus zirconia","volume":"vol. 8","author":"Osman","year":"2015","journal-title":"Materials"},{"issue":"1","key":"10.1016\/j.triboint.2023.108735_bib83","first-page":"41","article-title":"Use of cone-beam computed tomography in early detection of implant failure","volume":"vol. 59","author":"Yepes","year":"2015","journal-title":"Dent Clin"},{"issue":"3","key":"10.1016\/j.triboint.2023.108735_bib84","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.bjoms.2011.04.074","article-title":"Early dental implant failure: risk factors","volume":"vol. 50","author":"Baqain","year":"2012","journal-title":"Br J Oral Maxillofac Surg"},{"issue":"1","key":"10.1016\/j.triboint.2023.108735_bib85","doi-asserted-by":"crossref","first-page":"77","DOI":"10.2319\/012919-59.1","article-title":"Machine learning in orthodontics: introducing a 3d auto-segmentation and auto-landmark finder of cbct images to assess maxillary constriction in unilateral impacted canine patients","volume":"vol. 90","author":"Chen","year":"2019","journal-title":"Angle Orthod"},{"key":"10.1016\/j.triboint.2023.108735_bib86","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.cmpb.2017.05.012","article-title":"Computer-aided diagnosis of periapical cyst and keratocystic odontogenic tumor on cone beam computed tomography","volume":"vol. 146","author":"Yilmaz","year":"2017","journal-title":"Comput Methods Prog Biomed"},{"issue":"1","key":"10.1016\/j.triboint.2023.108735_bib87","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s10916-009-9347-9","article-title":"Diagnosis of dental deformities in cephalometry images using support vector machine","volume":"vol. 35","author":"Banumathi","year":"2011","journal-title":"J Med Syst"},{"issue":"4","key":"10.1016\/j.triboint.2023.108735_bib88","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1016\/j.ajodo.2016.03.028","article-title":"Objective method for evaluating orthodontic treatment from the lay perspective: an eye-tracking study","volume":"vol. 150","author":"Wang","year":"2016","journal-title":"Am J Orthod Dentofac Orthop"},{"key":"10.1016\/j.triboint.2023.108735_bib89","doi-asserted-by":"crossref","DOI":"10.1016\/j.matchemphys.2020.123974","article-title":"Prediction of the NiTi shape memory alloy composition with the best corrosion resistance for dental applications utilizing artificial intelligence","volume":"vol. 258","author":"Nazarahari","year":"2021","journal-title":"Mater Chem Phys"},{"issue":"9","key":"10.1016\/j.triboint.2023.108735_bib90","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1177\/0022034520920593","article-title":"Alveolar bone segmentation in intraoral ultrasonographs with machine learning","volume":"vol. 99","author":"Nguyen","year":"2020","journal-title":"J Dent Res"},{"issue":"10","key":"10.1016\/j.triboint.2023.108735_bib91","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1111\/jop.13089","article-title":"Machine learning and treatment outcome prediction for oral cancer","volume":"vol. 49","author":"Chu","year":"2020","journal-title":"J Oral Pathol Med"},{"issue":"2","key":"10.1016\/j.triboint.2023.108735_bib92","article-title":"Dataset augmentation for machine learning applications of dental radiography","volume":"vol. 11","author":"Khan","year":"2020","journal-title":"Int J Adv Comput Sci Appl"},{"issue":"4","key":"10.1016\/j.triboint.2023.108735_bib93","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1111\/ger.12432","article-title":"Application of machine learning for diagnostic prediction of root caries","volume":"vol. 36","author":"Hung","year":"2019","journal-title":"Gerodontology"},{"key":"10.1016\/j.triboint.2023.108735_bib94","series-title":"Machine Learning in Dentistry","first-page":"55","article-title":"Machine learning for facial recognition in orthodontics","author":"Tanikawa","year":"2021"},{"key":"10.1016\/j.triboint.2023.108735_bib95","first-page":"484","article-title":"Dental microfracture detection using wavelet features and machine learning","volume":"vol. 11596","author":"Vicory","year":"2021","journal-title":"Med Imaging 2021: Image Process"},{"issue":"12","key":"10.1016\/j.triboint.2023.108735_bib96","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1016\/j.joen.2021.09.009","article-title":"A deep learning approach to segment and classify C-shaped canal morphologies in mandibular second molars using cone-beam computed tomography","volume":"vol. 47","author":"Sherwood","year":"2021","journal-title":"J Endod"},{"key":"10.1016\/j.triboint.2023.108735_bib97","unstructured":"T. Ekert, Machine learning techniques for computer aided classification of dental radiographic images, 2020, doi: 10.17169\/refubium-26674."},{"issue":"5","key":"10.1016\/j.triboint.2023.108735_bib98","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.2214\/AJR.19.22593","article-title":"Machine learning\u2013based MRI texture analysis to predict the histologic grade of oral squamous cell carcinoma","volume":"vol. 215","author":"Ren","year":"2020","journal-title":"Am J Roentgenol"},{"issue":"12","key":"10.1016\/j.triboint.2023.108735_bib99","doi-asserted-by":"crossref","first-page":"2208","DOI":"10.1016\/j.joms.2020.06.015","article-title":"Machine learning predicts lymph node metastasis in early-stage oral tongue squamous cell carcinoma","volume":"vol. 78","author":"Shan","year":"2020","journal-title":"J Oral Maxillofac Surg"},{"issue":"8","key":"10.1016\/j.triboint.2023.108735_bib100","doi-asserted-by":"crossref","first-page":"1040","DOI":"10.1016\/j.ins.2008.12.001","article-title":"Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem","volume":"vol. 179","author":"Catal","year":"2009","journal-title":"Inf Sci"},{"key":"10.1016\/j.triboint.2023.108735_bib101","series-title":"Springer tracts in mechanical engineering","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/978-3-030-60124-9_2","article-title":"Biomechanical and Tribological Aspects of Orthopaedic Implants, in Multiscale Biomechanics and Tribology of Inorganic and Organic Systems: In memory of Professor Sergey Psakhie","author":"Gotman","year":"2021"},{"issue":"3","key":"10.1016\/j.triboint.2023.108735_bib102","doi-asserted-by":"crossref","DOI":"10.3390\/prosthesis4030040","article-title":"Finite element analysis of zirconia dental implant","volume":"vol. 4","author":"Fiorillo","year":"2022","journal-title":"Prosthesis"},{"key":"10.1016\/j.triboint.2023.108735_bib103","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2021.109330","article-title":"Deep learning through LSTM classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems","volume":"vol. 177","author":"Belagoune","year":"2021","journal-title":"Measurement"},{"issue":"1","key":"10.1016\/j.triboint.2023.108735_bib104","doi-asserted-by":"crossref","first-page":"20140282","DOI":"10.1259\/dmfr.20140282","article-title":"CBCT in orthodontics: assessment of treatment outcomes and indications for its use","volume":"vol. 44","author":"Kapila","year":"2015","journal-title":"Dentomaxillofacial Radio"},{"issue":"8","key":"10.1016\/j.triboint.2023.108735_bib105","doi-asserted-by":"crossref","first-page":"767","DOI":"10.3349\/ymj.2021.62.8.767","article-title":"Correlation between metal ions and cytokines in the saliva of patients with oral lichenoid lesions","volume":"vol. 62","author":"Ju","year":"2021","journal-title":"Yonsei Med J"},{"issue":"5","key":"10.1016\/j.triboint.2023.108735_bib106","doi-asserted-by":"crossref","DOI":"10.3390\/jcm9051264","article-title":"Salivary levels of titanium, nickel, vanadium, and arsenic in patients treated with dental implants: a case-control study","volume":"vol. 9","author":"Papi","year":"2020","journal-title":"J Clin Med"}],"container-title":["Tribology International"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0301679X23005236?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0301679X23005236?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T11:39:35Z","timestamp":1777549175000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0301679X23005236"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":105,"alternative-id":["S0301679X23005236"],"URL":"https:\/\/doi.org\/10.1016\/j.triboint.2023.108735","relation":{},"ISSN":["0301-679X"],"issn-type":[{"value":"0301-679X","type":"print"}],"subject":[],"published":{"date-parts":[[2023,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Early predicting tribocorrosion rate of dental implant titanium materials using random forest machine learning models","name":"articletitle","label":"Article Title"},{"value":"Tribology International","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.triboint.2023.108735","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"108735"}}