{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T06:59:34Z","timestamp":1781161174727,"version":"3.54.1"},"reference-count":26,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T00:00:00Z","timestamp":1781049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Program of Jiangsu Province","award":["BE2022795"],"award-info":[{"award-number":["BE2022795"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Tooth extraction and maximum anchorage assessment are key decision points in orthodontic treatment planning, yet existing machine learning models for orthodontic decision support often lack transparency, limiting their clinical interpretability and trustworthiness. In this study, we developed and externally validated an explainable orthodontic treatment decision-support model that integrates expert-derived Analytic Hierarchy Process (AHP) weighting with machine learning. A diagnostic indicator framework comprising 18 orthodontic variables was established through a literature review, clinical data analysis, and two rounds of expert surveys. A retrospective cohort of 485 patients receiving fixed-appliance orthodontic treatment was used for model development and internal validation. AHP-derived composite scores were incorporated into the machine learning models for two prediction tasks, namely tooth extraction and maximum anchorage requirement, and an expert-informed fuzzy-rule score was calculated from pretreatment indicators for the maximum anchorage task to capture clinically interpretable anchorage tendencies. Model performance was evaluated using ROC-AUC, F1 score, precision, recall, PR-AUC, calibration analysis, and decision curve analysis, while SHAP was applied to interpret feature contributions. The AHP-RF extraction model and AHP-enhanced LR maximum anchorage model achieved the highest AUCs among the compared models (0.864 and 0.822, respectively), although paired DeLong tests showed no significant differences from the closest competing models. SHAP analysis identified lower lip-to-E-line distance, U1-NA, and the AHP composite score as important predictors, indicating consistency between model outputs and clinical reasoning. In the external validation cohort, the extraction model correctly classified 57 of 74 cases, and the maximum anchorage model correctly classified 24 of 29 cases, supporting the preliminary transportability of the proposed framework. These results suggest that integrating AHP-derived expert knowledge with machine learning provides an explainable and clinically interpretable decision-support model for orthodontic treatment planning, with potential value in improving standardized, evidence-informed, and patient-specific orthodontic decision-making.<\/jats:p>","DOI":"10.3390\/bioengineering13060671","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T05:50:58Z","timestamp":1781157058000},"page":"671","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and External Validation of an Explainable AHP-ML Model for Orthodontic Tooth Extraction and Anchorage Decision Support"],"prefix":"10.3390","volume":"13","author":[{"given":"Yang","family":"Yi","sequence":"first","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinhang","family":"Shen","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Orthodontics, School of Stomatology, Nanjing Medical University, Nanjing 210029, China"},{"name":"Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing 210029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mao","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Orthodontics, School of Stomatology, Nanjing Medical University, Nanjing 210029, China"},{"name":"Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, School of Mechanical Engineering, Southeast University, Nanjing 211000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9169-4615","authenticated-orcid":false,"given":"Bin","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Orthodontics, School of Stomatology, Nanjing Medical University, Nanjing 210029, China"},{"name":"Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing 210029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,10]]},"reference":[{"key":"ref_1","unstructured":"Zhao, Z., Zhou, Y., and Bai, Y. (2018). Kouqiang Zhengjixue [Orthodontics], People\u2019s Medical Publishing House. [7th ed.]. (In Chinese)."},{"key":"ref_2","unstructured":"Proffit, W.R., Fields, H.W., and Sarver, D.M. (2019). Contemporary Orthodontics, Elsevier. [6th ed.]."},{"key":"ref_3","unstructured":"Graber, L.W., Vanarsdall, R.L., and Vig, K.W.L. (2017). Orthodontics: Current Principles and Techniques, Elsevier. [6th ed.]."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1067\/mod.2001.114301","article-title":"The importance of incisor positioning in the esthetic smile: The smile arc","volume":"120","author":"Sarver","year":"2001","journal-title":"Am. J. Orthod. Dentofac. Orthop."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","article-title":"A guide to deep learning in healthcare","volume":"25","author":"Esteva","year":"2019","journal-title":"Nat. Med."},{"key":"ref_7","first-page":"278","article-title":"Progress on application of artificial intelligence technology in orthodontic diagnosis and treatment","volume":"30","author":"Ma","year":"2022","journal-title":"Kouqiang Jibing Fangzhi"},{"key":"ref_8","first-page":"29","article-title":"Application of machine learning in orthodontics","volume":"42","author":"Wu","year":"2022","journal-title":"Stomatology"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, J., Zhang, C., and Shan, Z. (2023). Application of artificial intelligence in orthodontics: Current state and future perspectives. Healthcare, 11.","DOI":"10.3390\/healthcare11202760"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1038\/s41598-018-38439-w","article-title":"Orthodontic treatment planning based on artificial neural networks","volume":"9","author":"Li","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Prasad, J., Mallikarjunaiah, D.R., Shetty, A., Gandedkar, N., Chikkamuniswamy, A.B., and Shivashankar, P.C. (2023). Machine learning predictive model as clinical decision support system in orthodontic treatment planning. Dent. J., 11.","DOI":"10.3390\/dj11010001"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"100759","DOI":"10.1016\/j.ortho.2023.100759","article-title":"A machine learning model for orthodontic extraction\/non-extraction decision in a racially and ethnically diverse patient population","volume":"21","author":"Mason","year":"2023","journal-title":"Int. Orthod."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Etemad, L.E., Heiner, J.P., Amin, A.A., Wu, T.H., Chao, W.L., Hsieh, S.J., Sun, Z., Guez, C., and Ko, C.-C. (2024). Effectiveness of Machine Learning in Predicting Orthodontic Tooth Extractions: A Multi-Institutional Study. Bioengineering, 11.","DOI":"10.3390\/bioengineering11090888"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ueda, A., Tussie, C., Kim, S., Kuwajima, Y., Matsumoto, S., Kim, G., Satoh, K., and Nagai, S. (2023). Classification of Maxillofacial Morphology by Artificial Intelligence Using Cephalometric Analysis Measurements. Diagnostics, 13.","DOI":"10.3390\/diagnostics13132134"},{"key":"ref_15","first-page":"83","article-title":"Decision making with the analytic hierarchy process","volume":"1","author":"Saaty","year":"2008","journal-title":"Int. J. Serv. Sci."},{"key":"ref_16","first-page":"14336","article-title":"Review of the main developments in the analytic hierarchy process","volume":"38","author":"Ishizaka","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ruan, C., Xiong, J., Li, Z., Zhu, Y., and Cai, Q. (2024). Study on decision-making for orthodontic treatment plans based on analytic hierarchy process. BMC Oral Health, 24.","DOI":"10.1186\/s12903-024-04281-y"},{"key":"ref_18","first-page":"4765","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv. Neural Inf. Process Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Amann, J., Blasimme, A., Vayena, E., Frey, D., and Madai, V.I. (2020). Precise4Q Consortium. Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Med. Inform. Decis. Mak., 20.","DOI":"10.1186\/s12911-020-01332-6"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1053\/j.sodo.2025.10.008","article-title":"Space gaining in orthodontic treatment planning: The SPEED-L decision-making framework","volume":"32","author":"Gandedkar","year":"2025","journal-title":"Semin. Orthod."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1111\/ocr.12811","article-title":"Evaluation of different machine learning algorithms for extraction decision in orthodontic treatment","volume":"27","year":"2024","journal-title":"Orthod. Craniofac Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1067\/mod.2002.125235","article-title":"Changes in soft tissue profile after orthodontic treatment with and without extractions","volume":"122","author":"Kocadereli","year":"2002","journal-title":"Am. J. Orthod. Dentofac. Orthop."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1186\/2196-1042-14-47","article-title":"Extraction decision and identification of treatment predictors in Class I malocclusions","volume":"14","author":"Konstantonis","year":"2013","journal-title":"Prog. Orthod."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"320","DOI":"10.2319\/051016-375.1","article-title":"Comparison of the effects of mini-implant and traditional anchorage on patients with maxillary dentoalveolar protrusion","volume":"87","author":"Xu","year":"2017","journal-title":"Angle Orthod."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.ortho.2020.10.004","article-title":"Predictors of treatment decisions made by adult orthodontic patients presenting with unerupted permanent teeth","volume":"19","author":"Liu","year":"2021","journal-title":"Int. Orthod."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, M., Huang, J., Tang, X., Zheng, L., and Yang, C. (2025). Anterior alveolar bone and tooth inclination in different skeletal patterns: A cone-beam computed tomography study. BMC Oral Health, 25.","DOI":"10.1186\/s12903-025-06537-7"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/6\/671\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T06:35:37Z","timestamp":1781159737000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/6\/671"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,10]]},"references-count":26,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["bioengineering13060671"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13060671","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,10]]}}}