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Int Dent J. 2006;56:203\u20138. https:\/\/doi.org\/10.1111\/j.1875-595X.2006.tb00095.x PMID:16972394","DOI":"10.1111\/j.1875-595X.2006.tb00095.x"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] Hung K,Montalvao C,Tanaka R,Kawai T,Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. Dentomaxillofac Radiol. 2020;49:20190107. https:\/\/doi.org\/10.1259\/dmfr.20190107 PMID:31386555","DOI":"10.1259\/dmfr.20190107"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] Wenzel A,Hintze H,Kold LM,Kold S. Accuracy of computer-automated caries detection in digital radiographs compared with human observers. Eur J Oral Sci. 2002;110:199\u2013203. https:\/\/doi.org\/10.1034\/j.1600-0447.2002.21245.x PMID:12120704","DOI":"10.1034\/j.1600-0447.2002.21245.x"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] Chang HJ,Lee SJ,Yong TH,Shin NY,Jang BG,Kim JE,<i>et al.<\/i> Deep learning hybrid method to automatically diagnose periodontal bone loss and stage periodontitis. Sci Rep. 2020;10:7531. https:\/\/doi.org\/10.1038\/s41598-020-64509-z PMID:32372049","DOI":"10.1038\/s41598-020-64509-z"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] Feres M,Louzoun Y,Haber S,Faveri M,Figueiredo LC,Levin L. Support vector machine-based differentiation between aggressive and chronic periodontitis using microbial profiles. Int Dent J. 2018;68:39\u201346. https:\/\/doi.org\/10.1111\/idj.12326 PMID:28771699","DOI":"10.1111\/idj.12326"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10]Ekert T,Krois J,Meinhold L,Elhennawy K,Emara R,Golla T,<i>et al.<\/i> Deep learning for the radiographic detection of apical lesions. J Endod 2019;45:917-22. e5.","DOI":"10.1016\/j.joen.2019.03.016"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] Johari M,Esmaeili F,Andalib A,Garjani S,Saberkari H. Detection of vertical root fractures in intact and endodontically treated premolar teeth by designing a probabilistic neural network: an ex vivo study. Dentomaxillofac Radiol. 2017;46:20160107. https:\/\/doi.org\/10.1259\/dmfr.20160107 PMID:27786566","DOI":"10.1259\/dmfr.20160107"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] Patcas R,Bernini DAJ,Volokitin A,Agustsson E,Rothe R,Timofte R. Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age. Int J Oral Maxillofac Surg. 2019;48:77\u201383. https:\/\/doi.org\/10.1016\/j.ijom.2018.07.010 PMID:30087062","DOI":"10.1016\/j.ijom.2018.07.010"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] Kishimoto T,Goto T,Matsuda T,Iwawaki Y,Ichikawa T. Application of artificial intelligence in the dental field: A literature review. J Prosthodont Res. 2022;66:19\u201328. https:\/\/doi.org\/10.2186\/jpr.JPR_D_20_00139 PMID:33441504","DOI":"10.2186\/jpr.JPR_D_20_00139"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] Raith S,Vogel EP,Anees N,Keul C,G\u00fcth JF,Edelhoff D,<i>et al.<\/i> Artificial Neural Networks as a powerful numerical tool to classify specific features of a tooth based on 3D scan data. Comput Biol Med. 2017;80:65\u201376. https:\/\/doi.org\/10.1016\/j.compbiomed.2016.11.013 PMID:27915125","DOI":"10.1016\/j.compbiomed.2016.11.013"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] Cheng C,Cheng X,Dai N,Jiang X,Sun Y,Li W. Prediction of facial deformation after complete denture prosthesis using BP neural network. Comput Biol Med. 2015;66:103\u201312. https:\/\/doi.org\/10.1016\/j.compbiomed.2015.08.018 PMID:26386549","DOI":"10.1016\/j.compbiomed.2015.08.018"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] Papantonopoulos G,Gogos C,Housos E,Bountis T,Loos BG. Prediction of individual implant bone levels and the existence of implant \u201cphenotypes\u201d. Clin Oral Implants Res. 2017;28:823\u201332. https:\/\/doi.org\/10.1111\/clr.12887 PMID:27252014","DOI":"10.1111\/clr.12887"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] Sakai T,Li H,Shimada T,Kita S,Iida M,Lee C,<i>et al.<\/i> Development of artificial intelligence model for supporting implant drilling protocol decision making. J Prosthodont Res. 2023;67:360\u20135. https:\/\/doi.org\/10.2186\/jpr.JPR_D_22_00053 PMID:36002334","DOI":"10.2186\/jpr.JPR_D_22_00053"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] Chen Q,Wu J,Li S,Lyu P,Wang Y,Li M. An ontology-driven, case-based clinical decision support model for removable partial denture design. Sci Rep. 2016;6:27855. https:\/\/doi.org\/10.1038\/srep27855 PMID:27297679","DOI":"10.1038\/srep27855"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19]Chau RCW,Hsung RT-C,McGrath C,Pow EHN,Lam WYH. Accuracy of artificial intelligence-designed single-molar dental prostheses: A feasibility study. J Prosthet Dent 2023;S0022-3913(22):00764-8. https:\/\/doi.org\/10.1016\/j.prosdent.2022.12.004","DOI":"10.1016\/j.prosdent.2022.12.004"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20]Chau RCW,Li GH,Tew IM,Thu KM,McGrath C,Lo WL,<i>et al.<\/i> Accuracy of artificial intelligence-based photographic detection of gingivitis. Int Dent J 2023;73: 724-30. https:\/\/doi.org\/10.1016\/j.identj.2023.03.007","DOI":"10.1016\/j.identj.2023.03.007"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] Hung K,Yeung AWK,Tanaka R,Bornstein MM. Current applications, opportunities, and limitations of AI for 3D imaging in dental research and practice. Int J Environ Res Public Health. 2020;17:4424. https:\/\/doi.org\/10.3390\/ijerph17124424 PMID:32575560","DOI":"10.3390\/ijerph17124424"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] K\u00f6seo\u011flu M,Bay\u0131nd\u0131r F. Influence of different parameters on the smile esthetic perception of dentists, prosthodontists and laypeople. Meandros Medical and Dental Journal. 2021;22:77\u201384. https:\/\/doi.org\/10.4274\/meandros.galenos.2020.83723","DOI":"10.4274\/meandros.galenos.2020.83723"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] Koseoglu M,Bayindir F. Effects of gingival margin asymmetries on the smile esthetic perception of dental professionals and lay people. J Esthet Restor Dent. 2020;32:480\u20136. https:\/\/doi.org\/10.1111\/jerd.12595 PMID:32596944","DOI":"10.1111\/jerd.12595"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] \u00d6zdemir H,K\u00f6seo\u011flu M,Bayindir F. An investigation of the esthetic indicators of maxillary anterior teeth in young Turkish people. J Prosthet Dent. 2018;120:583\u20138. https:\/\/doi.org\/10.1016\/j.prosdent.2017.12.015 PMID:29724547","DOI":"10.1016\/j.prosdent.2017.12.015"},{"key":"25","unstructured":"[25] Chiche GJ,Pinault A. Esthetics of anterior fixed prosthodontics. 1st ed. Quintessence Pub Co: Chicago; 1994."},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] Ellakwa A,McNamara K,Sandhu J,James K,Arora A,Klineberg I,<i>et al.<\/i> Quantifying the selection of maxillary anterior teeth using intraoral and extraoral anatomical landmarks. J Contemp Dent Pract. 2011;12:414\u201321. https:\/\/doi.org\/10.5005\/jp-journals-10024-1069 PMID:22269230","DOI":"10.5005\/jp-journals-10024-1069"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] \u00d6zdemir H,K\u00f6seo\u011flu M. Relationship between different points on the face and the width of maxillary central teeth in a Turkish population. J Prosthet Dent. 2019;122:63\u20138. https:\/\/doi.org\/10.1016\/j.prosdent.2018.11.006 PMID:30803805","DOI":"10.1016\/j.prosdent.2018.11.006"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] Abdullah MA. Inner canthal distance and geometric progression as a predictor of maxillary central incisor width. J Prosthet Dent. 2002;88:16\u201320. https:\/\/doi.org\/10.1067\/mpr.2002.126095 PMID:12239474","DOI":"10.1016\/S0022-3913(02)00042-2"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] Cesario VA Jr,Latta GH Jr. Relationship between the mesiodistal width of the maxillary central incisor and interpupillary distance. J Prosthet Dent. 1984;52:641\u20133. https:\/\/doi.org\/10.1016\/0022-3913(84)90133-1 PMID:6593457","DOI":"10.1016\/0022-3913(84)90133-1"},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] Hoffman W Jr,Bomberg TJ,Hatch RA. Interalar width as a guide in denture tooth selection. J Prosthet Dent. 1986;55:219\u201321. https:\/\/doi.org\/10.1016\/0022-3913(86)90348-3 PMID:3514860","DOI":"10.1016\/0022-3913(86)90348-3"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] Latta GH Jr,Weaver JR,Conkin JE. The relationship between the width of the mouth, interalar width, bizygomatic width, and interpupillary distance in edentulous patients. J Prosthet Dent. 1991;65:250\u20134. https:\/\/doi.org\/10.1016\/0022-3913(91)90170-2 PMID:2051360","DOI":"10.1016\/0022-3913(91)90170-2"},{"key":"32","doi-asserted-by":"crossref","unstructured":"[32] Smith BJ. The value of the nose width as an esthetic guide in prosthodontics. J Prosthet Dent. 1975;34:562\u201373. https:\/\/doi.org\/10.1016\/0022-3913(75)90044-X PMID:1058958","DOI":"10.1016\/0022-3913(75)90044-X"},{"key":"33","doi-asserted-by":"crossref","unstructured":"[33] von Elm E,Altman DG,Egger M,Pocock SJ,G\u00f8tzsche PC,Vandenbroucke JP; STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. Int J Surg. 2014;12:1495\u20139. https:\/\/doi.org\/10.1016\/j.ijsu.2014.07.013 PMID:25046131","DOI":"10.1016\/j.ijsu.2014.07.013"},{"key":"34","doi-asserted-by":"crossref","unstructured":"[34] Alzubi J,Nayyar A,Kumar A. Machine learning from theory to algorithms: an overview. ed. IOP Publishing: Philadelphia;2018, p. 1.","DOI":"10.1088\/1742-6596\/1142\/1\/012012"},{"key":"35","unstructured":"[35] Bonaccorso G. Machine learning algorithms. Packt Publishing Ltd: Birmingham; 2017."},{"key":"36","doi-asserted-by":"crossref","unstructured":"[36] Waskom M. seaborn: statistical data visualization. J Open Source Softw. 2021;6:3021. https:\/\/doi.org\/10.21105\/joss.03021","DOI":"10.21105\/joss.03021"},{"key":"37","unstructured":"[37]Production Flask Documentation. Deploying (2.2.x).[https:\/\/flask.palletsprojects.com\/en\/2.2.x\/deploying\/]; [2010] [accessed 10 September 2022]"},{"key":"38","unstructured":"[38]Gunicorn. Flask Documentation (2.2.x) [https:\/\/flask.palletsprojects.com\/en\/2.2.x\/deploying\/gunicorn\/]; [2010] [accessed 10 September 2022]."},{"key":"39","unstructured":"[39] Pedregosa F,Varoquaux G,Gramfort A,Michel V,Thirion B,Grisel O,<i>et al.<\/i> Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825\u201330."},{"key":"40","unstructured":"[40]Pythonbasics. pythonbasics.split-train-test. [https:\/\/pythonbasics.org\/split-train-test\/]; [2021] [accessed 10 September 2022]"},{"key":"41","unstructured":"[41]medium.com. the-motivation-for-train-test-split. [https:\/\/medium.com\/@nahmed3536\/the-motivation-for-train-test-split-2b1837f596c3]; [2022] [accessed 12 December 2022]."},{"key":"42","doi-asserted-by":"crossref","unstructured":"[42] Lee JH,Jeong SN. Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs. Medicine (Baltimore). 2020;99:e20787. https:\/\/doi.org\/10.1097\/MD.0000000000020787 PMID:32590758","DOI":"10.1097\/MD.0000000000020787"},{"key":"43","doi-asserted-by":"crossref","unstructured":"[43] Lerner H,Mouhyi J,Admakin O,Mangano F. Artificial intelligence in fixed implant prosthodontics: a retrospective study of 106 implant-supported monolithic zirconia crowns inserted in the posterior jaws of 90 patients. BMC Oral Health. 2020;20:80. https:\/\/doi.org\/10.1186\/s12903-020-1062-4 PMID:32188431","DOI":"10.1186\/s12903-020-1062-4"},{"key":"44","doi-asserted-by":"crossref","unstructured":"[44] Yamaguchi S,Lee C,Karaer O,Ban S,Mine A,Imazato S. Predicting the debonding of CAD\/CAM composite resin crowns with AI. J Dent Res. 2019;98:1234\u20138. https:\/\/doi.org\/10.1177\/0022034519867641 PMID:31379234","DOI":"10.1177\/0022034519867641"},{"key":"45","doi-asserted-by":"crossref","unstructured":"[45] Wei J,Peng M,Li Q,Wang Y. Evaluation of a novel computer color matching system based on the improved back\u2010propagation neural network model. J Prosthodont. 2018;27:775\u201383. https:\/\/doi.org\/10.1111\/jopr.12561 PMID:27860023","DOI":"10.1111\/jopr.12561"},{"key":"46","doi-asserted-by":"crossref","unstructured":"[46] Takahashi T,Nozaki K,Gonda T,Ikebe K. A system for designing removable partial dentures using artificial intelligence. Part 1. Classification of partially edentulous arches using a convolutional neural network. J Prosthodont Res. 2021;65:115\u20138. https:\/\/doi.org\/10.2186\/jpr.JPOR_2019_354 PMID:32938860","DOI":"10.2186\/jpr.JPOR_2019_354"},{"key":"47","doi-asserted-by":"crossref","unstructured":"[47] Carrillo-Perez F,Pecho OE,Morales JC,Paravina RD,Della Bona A,Ghinea R,<i>et al.<\/i> Applications of artificial intelligence in dentistry: A comprehensive review. J Esthet Restor Dent. 2022;34:259\u201380. https:\/\/doi.org\/10.1111\/jerd.12844 PMID:34842324","DOI":"10.1111\/jerd.12844"},{"key":"48","doi-asserted-by":"crossref","unstructured":"[48] Bernauer SA,Zitzmann NU,Joda T. The use and performance of artificial intelligence in prosthodontics: a systematic review. Sensors (Basel). 2021;21:6628. https:\/\/doi.org\/10.3390\/s21196628 PMID:34640948","DOI":"10.3390\/s21196628"},{"key":"49","doi-asserted-by":"crossref","unstructured":"[49] Breiman L. Random forests. Mach Learn. 2001;45:5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","DOI":"10.1023\/A:1010933404324"},{"key":"50","doi-asserted-by":"crossref","unstructured":"[50] Hung M,Xu J,Lauren E,Voss MW,Rosales MN,Su W,<i>et al.<\/i> Development of a recommender system for dental care using machine learning. SN Applied Sciences. 2019;1:785. https:\/\/doi.org\/10.1007\/s42452-019-0795-7","DOI":"10.1007\/s42452-019-0795-7"},{"key":"51","doi-asserted-by":"crossref","unstructured":"[51] Akko\u00e7 B,Arslan A,K\u00f6k H. Automatic gender determination from 3D digital maxillary tooth plaster models based on the random forest algorithm and discrete cosine transform. Comput Methods Programs Biomed. 2017;143:59\u201365. https:\/\/doi.org\/10.1016\/j.cmpb.2017.03.001 PMID:28391819","DOI":"10.1016\/j.cmpb.2017.03.001"},{"key":"52","doi-asserted-by":"crossref","unstructured":"[52] Kang IA,Ngnamsie Njimbouom S,Lee KO,Kim JD. DCP: prediction of dental caries using machine learning in personalized medicine. Appl Sci (Basel). 2022;12:3043. https:\/\/doi.org\/10.3390\/app12063043","DOI":"10.3390\/app12063043"},{"key":"53","doi-asserted-by":"crossref","unstructured":"[53] J Iyer T,K R,Nersisson R,Zhuang Z,Joseph Raj AN,Refayee I. Machine learning-based facial beauty prediction and analysis of frontal facial images using facial landmarks and traditional image descriptors. Comput Intell Neurosci. 2021;2021:4423407. https:\/\/doi.org\/10.1155\/2021\/4423407 PMID:34484321","DOI":"10.1155\/2021\/4423407"},{"key":"54","doi-asserted-by":"crossref","unstructured":"[54] Moghimi S,Talebi M,Parisay I. Design and implementation of a hybrid genetic algorithm and artificial neural network system for predicting the sizes of unerupted canines and premolars. Eur J Orthod. 2012;34:480\u20136. https:\/\/doi.org\/10.1093\/ejo\/cjr042 PMID:21633091","DOI":"10.1093\/ejo\/cjr042"},{"key":"55","doi-asserted-by":"crossref","unstructured":"[55] Magne P,Gallucci GO,Belser UC. Anatomic crown width\/length ratios of unworn and worn maxillary teeth in white subjects. J Prosthet Dent. 2003;89:453\u201361. https:\/\/doi.org\/10.1016\/S0022-3913(03)00125-2 PMID:12806322","DOI":"10.1016\/S0022-3913(03)00125-2"},{"key":"56","doi-asserted-by":"crossref","unstructured":"[56] Zogheib T,Jacobs R,Bornstein MM,Agbaje JO,Anumendem D,Klazen Y,<i>et al.<\/i> Comparison of 3D Scanning Versus 2D Photography for the Identification of Facial Soft-Tissue Landmarks. Open Dent J. 2018;12:61\u201371. https:\/\/doi.org\/10.2174\/1874210601812010061 PMID:29492171","DOI":"10.2174\/1874210601812010061"},{"key":"57","unstructured":"[57] Auerbach BM. Human skeletal variation in the New World during the Holocene: Effects of climate and subsistence across geography and time-Part I. 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