{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T20:21:59Z","timestamp":1792009319918,"version":"4.4.0"},"reference-count":41,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T00:00:00Z","timestamp":1719187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: Machine learning (ML) models predicting the risk of refinement (i.e., a subsequent course of treatment being necessary) in clear aligner therapy (CAT) were developed and evaluated. Methods: An anonymized sample of 9942 CAT patients (70.6% females, 29.4% males, age range 18\u201364 years, median 30.5 years), as provided by DrSmile, a large European CAT provider based in Berlin, Germany, was used. Three different ML methods were employed: (1) logistic regression with L1 regularization, (2) extreme gradient boosting (XGBoost), and (3) support vector classification with a radial basis function kernel. In total, 74 factors were selected as predictors for these methods and are consistent with clinical reasoning. Results: On a held-out test set with a true-positive rate of 0.58, the logistic regression model has an area under the ROC curve (AUC) of 0.67, an average precision (AP) of 0.73, and Brier loss of 0.22; the XGBoost model has an AUC of 0.67, an AP of 0.74, and Brier loss of 0.22; and the support vector model has a recall of 0.61 and a precision of 0.64. The logistic regression and XGBoost models identify predictors influencing refinement risk, including patient compliance, interproximal enamel reduction (IPR) and certain planned tooth movements, for example, lingual translation of maxillary incisors being associated with the lowest risk of refinement and rotation of mandibular incisors with the highest risk. Conclusions: These findings suggest moderate, well-calibrated predictive accuracy with both regularized logistic regression and XGBoost and underscore the influence the identified factors have on the risk of refinement in CAT, emphasizing their importance in the careful planning of orthodontic treatment and the potential for shorter treatment times, less patient discomfort, and fewer clinic visits. Identification of at-risk individuals could support tailored clinical decision-making and enable targeted interventions<\/jats:p>","DOI":"10.3390\/jcm13133672","type":"journal-article","created":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T06:59:58Z","timestamp":1719212398000},"page":"3672","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Predicting Outcome in Clear Aligner Treatment: A Machine Learning Analysis"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5231-4333","authenticated-orcid":false,"given":"Daniel","family":"Wolf","sequence":"first","affiliation":[{"name":"Independent Researcher, Berlin 13089, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8992-4629","authenticated-orcid":false,"given":"Gasser","family":"Farrag","sequence":"additional","affiliation":[{"name":"Straumann Group\u2014etkon GmbH, Lochhamer Schlag 6, 82166 Gr\u00e4felfing, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1288-8452","authenticated-orcid":false,"given":"Tabea","family":"Fl\u00fcgge","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Surgery, Charit\u00e9\u2014Universit\u00e4tsmedizin Berlin, Corporate Member of Freie Universit\u00e4t Berlin and Humboldt-Universit\u00e4t zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5418-5683","authenticated-orcid":false,"given":"Lan Huong","family":"Timm","sequence":"additional","affiliation":[{"name":"DrSmile\u2014DZK Deutsche Zahnklinik GmbH, K\u00f6nigsallee 92a, 40212 D\u00fcsseldorf, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1111\/adj.12480","article-title":"Clear Aligners in Orthodontic Treatment","volume":"62","author":"Weir","year":"2017","journal-title":"Aust. Dent. J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Putrino, A., Barbato, E., and Galluccio, G. (2021). Clear Aligners: Between Evolution and Efficiency\u2014A Scoping Review. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18062870"},{"key":"ref_3","first-page":"163","article-title":"Communication through Telemedicine: Home Teleassistance in Orthodontics","volume":"10","author":"Favero","year":"2009","journal-title":"Eur. J. Paediatr. Dent."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Maspero, C., Abate, A., Cavagnetto, D., El Morsi, M., Fama, A., and Farronato, M. (2020). Available Technologies, Applications and Benefits of Teleorthodontics. A Literature Review and Possible Applications during the COVID-19 Pandemic. J. Clin. Med., 9.","DOI":"10.3390\/jcm9061891"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Timm, L.H., Farrag, G., Baxmann, M., and Schwendicke, F. (2021). Factors Influencing Patient Compliance during Clear Aligner Therapy: A Retrospective Cohort Study. J. Clin. Med., 10.","DOI":"10.3390\/jcm10143103"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Torsello, F., D\u2019Amico, G., Staderini, E., Marigo, L., Cordaro, M., and Castagnola, R. (2022). Factors Influencing Appliance Wearing Time during Orthodontic Treatments: A Literature Review. Appl. Sci., 12.","DOI":"10.3390\/app12157807"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1111\/j.1600-051X.2012.01895.x","article-title":"Prognostic Value of the Periodontal Risk Assessment in Patients with Aggressive Periodontitis","volume":"39","author":"Pritsch","year":"2012","journal-title":"J. Clin. Periodontol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.jebdp.2016.01.020","article-title":"Periodontal Management by Risk Assessment: A Pragmatic Approach","volume":"16","author":"Mullins","year":"2016","journal-title":"J. Evid. Based Dent. Pract."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1111\/ocr.12641","article-title":"Can We Predict Orthodontic Extraction Patterns by Using Machine Learning?","volume":"26","author":"Leavitt","year":"2023","journal-title":"Orthod. Craniofac. Res."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Qu, X., Zhang, C., Houser, S.H., Zhang, J., Zou, J., Zhang, W., and Zhang, Q. (2022). Prediction Model for Early Childhood Caries Risk Based on Behavioral Determinants Using a Machine Learning Algorithm. Comput. Methods Programs Biomed., 227.","DOI":"10.1016\/j.cmpb.2022.107221"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1111\/ocr.12502","article-title":"Machine Learning from Clinical Data Sets of a Contemporary Decision for Orthodontic Tooth Extraction","volume":"24","author":"Etemad","year":"2021","journal-title":"Orthod. Craniofac. Res."},{"key":"ref_12","first-page":"231","article-title":"Primary Enamel Permeability: A SEM Evaluation In Vivo","volume":"13","author":"Lucchese","year":"2012","journal-title":"Eur. J. Paediatr. Dent."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Couronn\u00e9, R., Probst, P., and Boulesteix, A.-L. (2018). Random Forest versus Logistic Regression: A Large-Scale Benchmark Experiment. BMC Bioinform., 19.","DOI":"10.1186\/s12859-018-2264-5"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"17464","DOI":"10.1038\/s41598-022-21373-3","article-title":"Predicting Mortality in the Very Old: A Machine Learning Analysis on Claims Data","volume":"12","author":"Krasowski","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1177\/14653125231166015","article-title":"Digital Treatment Planning and Clear Aligner Therapy: A Retrospective Cohort Study","volume":"50","author":"Meade","year":"2023","journal-title":"J. Orthod."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From Local Explanations to Global Understanding with Explainable AI for Trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1038\/sj.bdj.2012.666","article-title":"Oral Diagnosis and Treatment Planning: Part 3. Periodontal Disease and Assessment of Risk","volume":"213","author":"Corbet","year":"2012","journal-title":"Br. Dent. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1038\/sj.bdj.2019.3","article-title":"Periodontal Diagnosis in the Context of the 2017 Classification System of Periodontal Diseases and Conditions\u2014Implementation in Clinical Practice","volume":"226","author":"Dietrich","year":"2019","journal-title":"Br. Dent. J."},{"key":"ref_19","first-page":"203","article-title":"Evidence-Based Development of a Diagnosis-Dependent Therapy Planning System and Its Implementation in Modern Diagnostic Software","volume":"8","author":"Ahlers","year":"2005","journal-title":"Int. J. Comput. Dent."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Timm, L.H., R\u00f6\u00dfler, R., and Baxmann, M. (2023). Comparison of Clear Aligner Treatment in First-Treatment and Re-Treatment Patients: A Retrospective Cohort Study. Appl. Sci., 13.","DOI":"10.3390\/app13074303"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Benchimol, E.I., Smeeth, L., Guttmann, A., Harron, K., Moher, D., Petersen, I., S\u00f8rensen, H.T., von Elm, E., and Langan, S.M. (2015). The REporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) Statement. PLoS Med., 12.","DOI":"10.1371\/journal.pmed.1001885"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"W1","DOI":"10.7326\/M14-0698","article-title":"Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and Elaboration","volume":"162","author":"Moons","year":"2015","journal-title":"Ann. Intern. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1002\/wps.20882","article-title":"The Promise of Machine Learning in Predicting Treatment Outcomes in Psychiatry","volume":"20","author":"Chekroud","year":"2021","journal-title":"World Psychiatry"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1016\/S2215-0366(16)30171-7","article-title":"Multisite Prediction of 4-Week and 52-Week Treatment Outcomes in Patients with First-Episode Psychosis: A Machine Learning Approach","volume":"3","author":"Koutsouleris","year":"2016","journal-title":"Lancet Psychiatry"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"881","DOI":"10.2319\/061614-436.1","article-title":"Efficacy of Clear Aligners in Controlling Orthodontic Tooth Movement: A Systematic Review","volume":"85","author":"Rossini","year":"2015","journal-title":"Angle Orthod."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.ajodo.2017.11.028","article-title":"Accuracy of Clear Aligners: A Retrospective Study of Patients Who Needed Refinement","volume":"154","author":"Charalampakis","year":"2018","journal-title":"Am. J. Orthod. Dentofac. Orthop."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s40510-017-0190-0","article-title":"Predictability of Orthodontic Movement with Orthodontic Aligners: A Retrospective Study","volume":"18","author":"Lombardo","year":"2017","journal-title":"Prog. Orthod."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s40510-022-00406-7","article-title":"Accuracy Evaluation of Orthodontic Movements with Aligners: A Prospective Observational Study","volume":"23","author":"Bilello","year":"2022","journal-title":"Prog. Orthod."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.ajodo.2019.12.015","article-title":"Has Invisalign Improved? A Prospective Follow-Up Study on the Efficacy of Tooth Movement with Invisalign","volume":"158","author":"Haouili","year":"2020","journal-title":"Am. J. Orthod. Dentofac. Orthop."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s40510-020-00329-1","article-title":"Accuracy of Interproximal Enamel Reduction during Clear Aligner Treatment","volume":"21","author":"Nucci","year":"2020","journal-title":"Prog. Orthod."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1302\/2046-3758.510.BJR-2016-0004.R2","article-title":"Age-Related Effects on Osteoclastic Activities after Orthodontic Tooth Movement","volume":"5","author":"Li","year":"2016","journal-title":"Bone Jt. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s00056-019-00206-5","article-title":"Age Effect on Orthodontic Tooth Movement Rate and the Composition of Gingival Crevicular Fluid","volume":"81","author":"Schubert","year":"2020","journal-title":"J. Orofac. Orthop.\/Fortschritte Kieferorthop\u00e4die"},{"key":"ref_33","unstructured":"Chen, H., Janizek, J.D., Lundberg, S., and Lee, S.-I. (2020). True to the Model or True to the Data?. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.jocm.2018.07.002","article-title":"Is Your Dataset Big Enough? Sample Size Requirements When Using Artificial Neural Networks for Discrete Choice Analysis","volume":"28","author":"Alwosheel","year":"2018","journal-title":"J. Choice Model."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2019). Feature Engineering and Selection, Chapman and Hall\/CRC.","DOI":"10.1201\/9781315108230"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Van Calster, B., McLernon, D.J., van Smeden, M., Wynants, L., and Steyerberg, E.W. (2019). Calibration: The Achilles Heel of Predictive Analytics. BMC Med., 17.","DOI":"10.1186\/s12916-019-1466-7"},{"key":"ref_37","unstructured":"Kumar, I.E., Venkatasubramanian, S., Scheidegger, C., and Friedler, S. (2020, January 13\u201318). Problems with Shapley-Value-Based Explanations as Feature Importance Measures. Proceedings of the 37th International Conference on Machine Learning, Virtual Event."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_39","unstructured":"Haussler, D., Boser, B.E., Guyon, I.M., and Vapnik, V.N. (1992, January 27\u201329). A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, PA, USA."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). Resampling Methods. An Introduction to Statistical Learning, Springer. Springer Texts in Statistic.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression Shrinkage and Selection Via the Lasso","volume":"58","author":"Tibshirani","year":"2018","journal-title":"J. R. Stat. Soc. Ser. B"}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/13\/13\/3672\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:03:27Z","timestamp":1760108607000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/13\/13\/3672"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,24]]},"references-count":41,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["jcm13133672"],"URL":"https:\/\/doi.org\/10.3390\/jcm13133672","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,24]]}}}