Use of automated artificial intelligence to predict the need for orthodontic extractions
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Objective: To develop and explore the usefulness of an artificial intelligence system for the prediction of the need for dental extractions during orthodontic treatments based on gender, model variables, and cephalometric records. Methods: The gender, model variables, and radiographic records of 214 patients were obtained from an anonymized data bank containing 314 cases treated by two experienced orthodontists. The data were processed using an automated machine learning software (Auto-WEKA) and used to predict the need for extractions. Results: By generating and comparing several prediction models, an accuracy of 93.9% was achieved for determining whether extraction is required or not based on the model and radiographic data. When only model variables were used, an accuracy of 87.4% was attained, whereas a 72.7% accuracy was achieved if only cephalometric information was used. Conclusions: The use of an automated machine learning system allows the generation of orthodontic extraction prediction models. The accuracy of the optimal extraction prediction models increases with the combination of model and cephalometric data for the analytical process.
Abstract
Objective: To develop and explore the usefulness of an artificial intelligence system for the prediction of the need for dental extractions during orthodontic treatments based on gender, model variables, and cephalometric records. Methods: The gender, model variables, and radiographic records of 214 patients were obtained from an anonymized data bank containing 314 cases treated by two experienced orthodontists. The data were processed using an automated machine learning software (Auto-WEKA) and used to predict the need for extractions. Results: By generating and comparing several prediction models, an accuracy of 93.9% was achieved for determining whether extraction is required or not based on the model and radiographic data. When only model variables were used, an accuracy of 87.4% was attained, whereas a 72.7% accuracy was achieved if only cephalometric information was used. Conclusions: The use of an automated machine learning system allows the generation of orthodontic extraction prediction models. The accuracy of the optimal extraction prediction models increases with the combination of model and cephalometric data for the analytical process.
참고문헌 (30)
- "Machine learning vs. traditional statistics:different philosophies, different approaches" (2016)[URL]
- Bahaa K, "Principles in contemporary orthodontics", pp.451~492 (2011), IntechOpen
- Xie X, "Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment", Angle Orthod, 80, pp.262~266 (2010)
- Jung SK, "New approach for the diagnosis of extractions with neural network machine learning", Am J Orthod Dentofacial Orthop, 149, pp.127~133 (2016)
- Cui C, "Machine learning analysis of image data based on detailed MR image reports for nasopharyngeal carcinoma prognosis", Biomed Res Int, 2020, pp.8068913~ (2020)
- Behr M, "Feasibility of a support vector machine classifier for myofascial pain syndrome : diagnostic case-control study", J Ultrasound Med, 38, pp.2119~2132 (2019)
- Peeken JC, "Tumor grading of soft tissue sarcomas using MRI-based radiomics", EBioMedicine, 48, pp.332~340 (2019)
- "Automated machine learning: a short history" (2016)[URL]
- Kotthof L, "Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA", J Mach Learn Res, 18, pp.1~5 (2017)
- Orlenko A, "Model selection for metabolomics : predicting diagnosis of coronary artery disease using automated machine learning", Bioinformatics, 36, pp.1772~1778 (2020)
- Waring J, "Automated machine learning : review of the state-of-the-art and opportunities for healthcare", Artif Intell Med, 104, pp.101822~ (2020)
- Lee JH, "Detection and diagnosis of dental caries using a deep learningbased convolutional neural network algorithm", J Dent, 77, pp.106~111 (2018)
- Jae-Hong Lee, "Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm", Journal of Periodontal & Implant Science, 48(2), pp.114~123 (2018), 대한치주과학회
- Suebnukarn S, "A Bayesian decision support model for assessment of endodontic treatment outcome", Oral Surg Oral Med Oral Pathol Oral Radiol Endod, 106, pp.e48~e58 (2008)
- Murata S, "Towards a fully automated diagnostic system for orthodontic treatment in dentistry", Paper presented at: 2017IEEE 13th International Conference on e-Science (e-Science), pp.1~8 (2017), IEEE
- Scala A, "Complex networks for datadriven medicine : the case of Class III dentoskeletal disharmony", New J Phys, 16, pp.115017~ (2014)
- Auconi P, "Prediction of Class III treatment outcomes through orthodontic data mining", Eur J Orthod, 37, pp.257~267 (2015)
- Sokic E, "Semi-automatic assessment of cervical vertebral maturation stages using cephalograph images and centroidbased clustering", Acta Stomatol Croat, 46, pp.280~290 (2012)
- Martina R, "Intelligent production machines and systems", pp.235~240 (2006), Elsevier Science
- Takada K, "Computational formulation of orthodontic tooth-extraction decisions. Part I: to extract or not to extract", Angle Orthod, 79, pp.885~891 (2009)
- Li P, "Orthodontic treatment planning based on artificial neural networks", Sci Rep, 9, pp.2037~ (2019)
- Zaytoun ML, "An empirical approach to the extraction versus non-extraction decision in orthodontics", Master, pp.2019
- Suhail Y, "Machine learning for the diagnosis of orthodontic extractions:a computational analysis using ensemble learning", Bioengineering(Basel), 7, pp.55~ (2020)
- Paton C, "An open science approach to artificial intelligence in healthcare", Yearb Med Inform, 28, pp.47~51 (2019)
- The Lancet Respiratory Medicine, "Opening the black box of machine learning", Lancet Respir Med, 6, pp.801~ (2018)
- Mew J, "How many teeth are extracted as part of orthodontic treatment? A survey of 2038UK residents", Int J Dent Oral Sci, (S1) (2018)
- Dardengo Cde S, "Frequency of orthodontic extraction", Dental Press J Orthod, 21, pp.54~59 (2016)
- Jackson TH, "Extraction frequencies at a university orthodontic clinic in the 21st century : demographic and diagnostic factors affecting the likelihood of extraction", Am J Orthod Dentofacial Orthop, 151, pp.456~462 (2017)
- "Imbalanced data"[URL]
- Proffit WR, "Contemporary orthodontics" (2013), Elsevier