{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T00:52:01Z","timestamp":1792803121758,"version":"4.5.0"},"reference-count":99,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T00:00:00Z","timestamp":1650326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T00:00:00Z","timestamp":1650326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Clin Oral Invest"],"DOI":"10.1007\/s00784-022-04477-y","type":"journal-article","created":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T13:05:15Z","timestamp":1650373515000},"page":"5535-5555","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["Potential and impact of artificial intelligence algorithms in dento-maxillofacial radiology"],"prefix":"10.1007","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3971-3484","authenticated-orcid":false,"given":"Kuo Feng","family":"Hung","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1155-4389","authenticated-orcid":false,"given":"Qi Yong H.","family":"Ai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6670-6570","authenticated-orcid":false,"given":"Yiu Yan","family":"Leung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3672-357X","authenticated-orcid":false,"given":"Andy Wai Kan","family":"Yeung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,4,19]]},"reference":[{"key":"4477_CR1","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1259\/dmfr.21.4.1299632","volume":"21","author":"A Mol","year":"1992","unstructured":"Mol A, van der Stelt PF (1992) Application of computer-aided image interpretation to the diagnosis of periapical bone lesions. Dentomaxillofac Radiol 21:190\u2013194","journal-title":"Dentomaxillofac Radiol"},{"key":"4477_CR2","doi-asserted-by":"publisher","first-page":"20190107","DOI":"10.1259\/dmfr.20190107","volume":"49","author":"K Hung","year":"2020","unstructured":"Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM (2020) The use and performance of artificial intelligence applications in dental and maxillofacial radiology: a systematic review. Dentomaxillofac Radiol 49:20190107","journal-title":"Dentomaxillofac Radiol"},{"key":"4477_CR3","first-page":"227","volume":"10","author":"CA Kulikowski","year":"2015","unstructured":"Kulikowski CA (2015) An opening chapter of the first generation of artificial intelligence in medicine: the first rutgers AIM workshop, June 1975. Yearb Med Inform 10:227\u2013233","journal-title":"Yearb Med Inform"},{"key":"4477_CR4","doi-asserted-by":"publisher","first-page":"15006","DOI":"10.1038\/s41598-021-94093-9","volume":"11","author":"M Ezhov","year":"2021","unstructured":"Ezhov M, Gusarev M, Golitsyna M, Yates JM, Kushnerev E, Tamimi D, Aksoy S, Shumilov E, Sanders A, Orhan K (2021) Clinically applicable artificial intelligence system for dental diagnosis with CBCT. Sci Rep 11:15006","journal-title":"Sci Rep"},{"key":"4477_CR5","doi-asserted-by":"publisher","first-page":"4424","DOI":"10.3390\/ijerph17124424","volume":"17","author":"K Hung","year":"2020","unstructured":"Hung K, Yeung AWK, Tanaka R, Bornstein MM (2020) Current applications, opportunities, and limitations of AI for 3D imaging in dental research and practice. Int J Environ Res Public Health 17:4424","journal-title":"Int J Environ Res Public Health"},{"key":"4477_CR6","doi-asserted-by":"crossref","unstructured":"ter Haar Romeny BM (2019) A deeper understanding of deep learning. In artificial intelligence in medical imaging: opportunities, applications and risks, 1st ed.; Ranschaert, E.R., Morozov, S., Algra, P.R., Eds.; Springer: Berlin, Germany, 2019 25\u201338","DOI":"10.1007\/978-3-319-94878-2_3"},{"key":"4477_CR7","doi-asserted-by":"publisher","first-page":"e1900040","DOI":"10.1002\/prca.201900040","volume":"14","author":"AF Leite","year":"2020","unstructured":"Leite AF, Vasconcelos KF, Willems H, Jacobs R (2020) Radiomics and machine learning in oral healthcare. Proteomics Clin Appl 14:e1900040","journal-title":"Proteomics Clin Appl"},{"key":"4477_CR8","doi-asserted-by":"publisher","first-page":"33","DOI":"10.3348\/kjr.2019.0312","volume":"21","author":"S Do","year":"2020","unstructured":"Do S, Song KD, Chung JW (2020) Basics of deep learning: a radiologist\u2019s guide to understanding published radiology articles on deep learning. Korean J Radiol 21:33\u201341","journal-title":"Korean J Radiol"},{"key":"4477_CR9","doi-asserted-by":"publisher","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","volume":"30","author":"ZQ Zhao","year":"2019","unstructured":"Zhao ZQ, Zheng P, Xu ST, Wu X (2019) Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst 30:3212\u20133232","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4477_CR10","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/s10916-018-1003-9","volume":"42","author":"K Lan","year":"2018","unstructured":"Lan K, Wang DT, Fong S, Liu LS, Wong KKL, Dey N (2018) A survey of data mining and deep learning in bioinformatics. J Med Syst 42:139","journal-title":"J Med Syst"},{"key":"4477_CR11","doi-asserted-by":"publisher","first-page":"960","DOI":"10.1002\/j.0022-0337.2001.65.10.tb03470.x","volume":"65","author":"JD Bader","year":"2001","unstructured":"Bader JD, Shugars DA, Bonito AJ (2001) Systematic reviews of selected dental caries diagnostic and management methods. J Dent Educ 65:960\u2013968","journal-title":"J Dent Educ"},{"key":"4477_CR12","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1038\/sj.ebd.6401166","volume":"17","author":"JR Keenan","year":"2016","unstructured":"Keenan JR, Keenan AV (2016) Accuracy of dental radiographs for caries detection. Evid Based Dent 17:43","journal-title":"Evid Based Dent"},{"key":"4477_CR13","doi-asserted-by":"publisher","unstructured":"Srivastava MM, Kumar P, Pradhan L, Varadarajan S (2017) Detection of tooth caries in bitewing radiographs using deep learning. arXiv:1711.07312.\u00a0https:\/\/doi.org\/10.48550\/arXiv.1711.07312","DOI":"10.48550\/arXiv.1711.07312"},{"key":"4477_CR14","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.jdent.2018.07.015","volume":"77","author":"JH Lee","year":"2018","unstructured":"Lee JH, Kim DH, Jeong SN, Choi SH (2018) Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent 77:106\u2013111","journal-title":"J Dent"},{"key":"4477_CR15","doi-asserted-by":"crossref","unstructured":"Sornam M, Prabhakaran M (2019) Logit-based artificial bee colony optimization (LB-ABC) approach for dental caries classification using a back propagation neural network. In Integrated Intelligent Computing, Communication and Security; Krishna, A.N., Srikantaiah, K.C., Naveena, C., Eds.; Studies in Computational Intelligence; Springer: Singapore, 2019; Volume 771, 79\u201391","DOI":"10.1007\/978-981-10-8797-4_9"},{"key":"4477_CR16","doi-asserted-by":"publisher","first-page":"5192","DOI":"10.3390\/s21155192","volume":"21","author":"M Moran","year":"2021","unstructured":"Moran M, Faria M, Giraldi G, Bastos L, Oliveira L, Conci A (2021) Classification of approximal caries in bitewing radiographs using convolutional neural networks. Sensors 21:5192","journal-title":"Sensors"},{"key":"4477_CR17","unstructured":"AssistDent (2021) AssistDent Artificial Intelligence Software. https:\/\/www.assistdent.net. Accessed 1 Dec\u00a02021"},{"key":"4477_CR18","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1038\/s41415-021-3526-6","volume":"231","author":"H Devlin","year":"2021","unstructured":"Devlin H, Williams T, Graham J, Ashley M (2021) The ADEPT study: a comparative study of dentists\u2019 ability to detect enamel-only proximal caries in bitewing radiographs with and without the use of AssistDent artificial intelligence software. Br Dent J 231:481\u2013485","journal-title":"Br Dent J"},{"key":"4477_CR19","doi-asserted-by":"publisher","first-page":"1352","DOI":"10.1016\/j.joen.2021.06.003","volume":"47","author":"A Aminoshariae","year":"2021","unstructured":"Aminoshariae A, Kulild J, Nagendrababu V (2021) Artificial intelligence in endodontics: current applications and future directions. J Endod 47:1352\u20131357","journal-title":"J Endod"},{"key":"4477_CR20","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1016\/S1076-6332(03)80706-7","volume":"8","author":"DP Carmody","year":"2001","unstructured":"Carmody DP, McGrath SP, Dunn SM, van der Stelt PF, Schouten E (2001) Machine classification of dental images with visual search. Acad Radiol 8:1239\u20131246","journal-title":"Acad Radiol"},{"key":"4477_CR21","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1016\/j.joen.2019.03.016","volume":"45","author":"T Ekert","year":"2019","unstructured":"Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, Schwendicke F (2019) Deep learning for the radiographic detection of apical lesions. J Endod 45:917-922.e915","journal-title":"J Endod"},{"key":"4477_CR22","doi-asserted-by":"publisher","unstructured":"Flores A, Rysavy S, Enciso R, Okada K (2009) Non-invasive differential diagnosis of dental periapical lesions in cone-beam CT. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp 566\u2013569. https:\/\/doi.org\/10.1109\/ISBI.2009.5193110","DOI":"10.1109\/ISBI.2009.5193110"},{"key":"4477_CR23","doi-asserted-by":"publisher","first-page":"1653","DOI":"10.1118\/1.4914418","volume":"42","author":"K Okada","year":"2015","unstructured":"Okada K, Rysavy S, Flores A, Linguraru MG (2015) Noninvasive differential diagnosis of dental periapical lesions in cone-beam CT scans. Med Phys 42:1653\u20131665","journal-title":"Med Phys"},{"key":"4477_CR24","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1111\/iej.13265","volume":"53","author":"K Orhan","year":"2020","unstructured":"Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, \u00d6zy\u00fcrek T (2020) Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int Endod J 53:680\u2013689","journal-title":"Int Endod J"},{"key":"4477_CR25","doi-asserted-by":"publisher","first-page":"e12273","DOI":"10.1111\/jicd.12273","volume":"9","author":"C Garc\u00eda-Guerrero","year":"2018","unstructured":"Garc\u00eda-Guerrero C, Parra-Junco C, Quijano-Guauque S, Molano N, Pineda GA, Mar\u00edn-Zuluaga D (2018) Vertical root fractures in endodontically-treated teeth: a retrospective analysis of possible risk factors. J Investig Clin Dent 9:e12273","journal-title":"J Investig Clin Dent"},{"key":"4477_CR26","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1111\/j.1600-9657.2012.01148.x","volume":"29","author":"S Kositbowornchai","year":"2013","unstructured":"Kositbowornchai S, Plermkamon S, Tangkosol T (2013) Performance of an artificial neural network for vertical root fracture detection: an ex vivo study. Dent Traumatol 29:151\u2013155","journal-title":"Dent Traumatol"},{"key":"4477_CR27","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1007\/s11282-019-00409-x","volume":"36","author":"M Fukuda","year":"2020","unstructured":"Fukuda M, Inamoto K, Shibata N, Ariji Y, Yanashita Y, Kutsuna S, Nakata K, Katsumata A, Fujita H, Ariji E (2020) Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiol 36:337\u2013343","journal-title":"Oral Radiol"},{"key":"4477_CR28","doi-asserted-by":"publisher","first-page":"20160107","DOI":"10.1259\/dmfr.20160107","volume":"46","author":"M Johari","year":"2017","unstructured":"Johari M, Esmaeili F, Andalib A, Garjani S, Saberkari H (2017) Detection of vertical root fractures in intact and endodontically treated premolar teeth by designing a probabilistic neural network: an ex vivo study. Dentomaxillofac Radiol 46:20160107","journal-title":"Dentomaxillofac Radiol"},{"key":"4477_CR29","doi-asserted-by":"crossref","unstructured":"Jeon SJ, Yun JP, Yeom HG, Shin WS, Lee JH, Jeong SH, Seo MS (2021) Deep-learning for predicting C-shaped canals in mandibular second molars on panoramic radiographs. Dentomaxillofac Radiol\u00a050:20200513","DOI":"10.1259\/dmfr.20200513"},{"key":"4477_CR30","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1111\/j.1365-2591.2011.01970.x","volume":"45","author":"MA Saghiri","year":"2012","unstructured":"Saghiri MA, Asgar K, Boukani KK, Lotfi M, Aghili H, Delvarani A, Karamifar K, Saghiri AM, Mehrvarzfar P, Garcia-Godoy F (2012) A new approach for locating the minor apical foramen using an artificial neural network. Int Endod J 45:257\u2013265","journal-title":"Int Endod J"},{"key":"4477_CR31","doi-asserted-by":"publisher","first-page":"1130","DOI":"10.1016\/j.joen.2012.05.004","volume":"38","author":"MA Saghiri","year":"2012","unstructured":"Saghiri MA, Garcia-Godoy F, Gutmann JL, Lotfi M, Asgar K (2012) The reliability of artificial neural network in locating minor apical foramen: a cadaver study. J Endod 38:1130\u20131134","journal-title":"J Endod"},{"key":"4477_CR32","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.cmpb.2015.05.004","volume":"121","author":"PL Lin","year":"2015","unstructured":"Lin PL, Huang PW, Huang PY, Hsu HC (2015) Alveolar bone-loss area localization in periodontitis radiographs based on threshold segmentation with a hybrid feature fused of intensity and the H-value of fractional Brownian motion model. Comput Methods Programs Biomed 121:117\u2013126","journal-title":"Comput Methods Programs Biomed"},{"key":"4477_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cmpb.2017.06.012","volume":"148","author":"PL Lin","year":"2017","unstructured":"Lin PL, Huang PY, Huang PW (2017) Automatic methods for alveolar bone loss degree measurement in periodontitis periapical radiographs. Comput Methods Programs Biomed 148:1\u201311","journal-title":"Comput Methods Programs Biomed"},{"key":"4477_CR34","doi-asserted-by":"publisher","first-page":"17615","DOI":"10.1038\/s41598-019-53758-2","volume":"9","author":"J Kim","year":"2019","unstructured":"Kim J, Lee HS, Song IS, Jung KH (2019) DeNTNet: deep neural transfer network for the detection of periodontal bone loss using panoramic dental radiographs. Sci Rep 9:17615","journal-title":"Sci Rep"},{"key":"4477_CR35","doi-asserted-by":"publisher","first-page":"8495","DOI":"10.1038\/s41598-019-44839-3","volume":"9","author":"J Krois","year":"2019","unstructured":"Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, D\u00f6rfer C, Schwendicke F (2019) Deep learning for the radiographic detection of periodontal bone loss. Sci Rep 9:8495","journal-title":"Sci Rep"},{"key":"4477_CR36","doi-asserted-by":"publisher","first-page":"7531","DOI":"10.1038\/s41598-020-64509-z","volume":"10","author":"HJ Chang","year":"2020","unstructured":"Chang HJ, Lee SJ, Yong TH, Shin NY, Jang BG, Kim JE, Huh KH, Lee SS, Heo MS, Choi SC, Kim TI, Yi WJ (2020) Deep learning hybrid method to automatically diagnose periodontal bone loss and stage periodontitis. Sci Rep 10:7531","journal-title":"Sci Rep"},{"key":"4477_CR37","doi-asserted-by":"publisher","first-page":"114","DOI":"10.5051\/jpis.2018.48.2.114","volume":"48","author":"JH Lee","year":"2018","unstructured":"Lee JH, Kim DH, Jeong SN, Choi SH (2018) Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci 48:114\u2013123","journal-title":"J Periodontal Implant Sci"},{"key":"4477_CR38","doi-asserted-by":"publisher","first-page":"169","DOI":"10.5624\/isd.2020.50.2.169","volume":"50","author":"B Thanathornwong","year":"2020","unstructured":"Thanathornwong B, Suebnukarn S (2020) Automatic detection of periodontal compromised teeth in digital panoramic radiographs using faster regional convolutional neural networks. Imaging Sci Dent 50:169\u2013174","journal-title":"Imaging Sci Dent"},{"key":"4477_CR39","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1007\/s11548-021-02431-z","volume":"16","author":"RP Danks","year":"2021","unstructured":"Danks RP, Bano S, Orishko A, Tan HJ, Moreno Sancho F, D\u2019Aiuto F, Stoyanov D (2021) Automating periodontal bone loss measurement via dental landmark localisation. Int J Comput Assist Radiol Surg 16:1189\u20131199","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"4477_CR40","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1111\/jcpe.13574","volume":"49","author":"CT Lee","year":"2022","unstructured":"Lee CT, Kabir T, Nelson J, Sheng S, Meng HW, Van Dyke TE, Walji MF, Jiang X, Shams S (2022) Use of the deep learning approach to measure alveolar bone level. J Clin Periodontol 49:260\u2013269","journal-title":"J Clin Periodontol"},{"key":"4477_CR41","doi-asserted-by":"publisher","first-page":"e72891","DOI":"10.1371\/journal.pone.0072891","volume":"8","author":"YY Leung","year":"2013","unstructured":"Leung YY, Lee TC, Ho SM, Cheung LK (2013) Trigeminal neurosensory deficit and patient reported outcome measures: the effect on life satisfaction and depression symptoms. PLoS One 8:e72891","journal-title":"PLoS One"},{"key":"4477_CR42","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1016\/j.oooo.2020.04.005","volume":"130","author":"M Fukuda","year":"2020","unstructured":"Fukuda M, Ariji Y, Kise Y, Nozawa M, Kuwada C, Funakoshi T, Muramatsu C, Fujita H, Katsumata A, Ariji E (2020) Comparison of 3 deep learning neural networks for classifying the relationship between the mandibular third molar and the mandibular canal on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol 130:336\u2013343","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radiol"},{"key":"4477_CR43","doi-asserted-by":"crossref","unstructured":"Liu MQ, Xu ZN, Mao WY, Li Y, Zhang XH, Bai HL, Ding P, Fu KY (2022) Deep learning-based evaluation of the relationship between mandibular third molar and mandibular canal on CBCT. Clin Oral Investig\u00a026:981\u2013991","DOI":"10.1007\/s00784-021-04082-5"},{"key":"4477_CR44","doi-asserted-by":"publisher","first-page":"1954","DOI":"10.1038\/s41598-021-81449-4","volume":"11","author":"JH Yoo","year":"2021","unstructured":"Yoo JH, Yeom HG, Shin W, Yun JP, Lee JH, Jeong SH, Lim HJ, Lee J, Kim BC (2021) Deep learning based prediction of extraction difficulty for mandibular third molars. Sci Rep 11:1954","journal-title":"Sci Rep"},{"key":"4477_CR45","doi-asserted-by":"publisher","first-page":"430","DOI":"10.3390\/diagnostics10060430","volume":"10","author":"MG Endres","year":"2020","unstructured":"Endres MG, Hillen F, Salloumis M, Sedaghat AR, Niehues SM, Quatela O, Hanken H, Smeets R, Beck-Broichsitter B, Rendenbach C, Lakhani K, Heiland M, Gaudin RA (2020) Development of a deep learning algorithm for periapical disease detection in dental radiographs. Diagnostics 10:430","journal-title":"Diagnostics"},{"key":"4477_CR46","doi-asserted-by":"publisher","first-page":"236","DOI":"10.4258\/hir.2018.24.3.236","volume":"24","author":"W Poedjiastoeti","year":"2018","unstructured":"Poedjiastoeti W, Suebnukarn S (2018) Application of convolutional neural network in the diagnosis of jaw tumors. Healthc Inform Res 24:236\u2013241","journal-title":"Healthc Inform Res"},{"key":"4477_CR47","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.oooo.2019.05.014","volume":"128","author":"Y Ariji","year":"2019","unstructured":"Ariji Y, Yanashita Y, Kutsuna S, Muramatsu C, Fukuda M, Kise Y, Nozawa M, Kuwada C, Fujita H, Katsumata A, Ariji E (2019) Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique. Oral Surg Oral Med Oral Pathol Oral Radiol 128:424\u2013430","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radiol"},{"key":"4477_CR48","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1007\/s11282-020-00485-4","volume":"37","author":"H Watanabe","year":"2021","unstructured":"Watanabe H, Ariji Y, Fukuda M, Kuwada C, Kise Y, Nozawa M, Sugita Y, Ariji E (2021) Deep learning object detection of maxillary cyst-like lesions on panoramic radiographs: preliminary study. Oral Radiol 37:487\u2013493","journal-title":"Oral Radiol"},{"key":"4477_CR49","doi-asserted-by":"publisher","first-page":"20200185","DOI":"10.1259\/dmfr.20200185","volume":"49","author":"O Kwon","year":"2020","unstructured":"Kwon O, Yong TH, Kang SR, Kim JE, Huh KH, Heo MS (2020) Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network. Dentomaxillofac Radiol 49:20200185","journal-title":"Dentomaxillofac Radiol"},{"key":"4477_CR50","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.cmpb.2016.10.024","volume":"139","author":"F Abdolali","year":"2017","unstructured":"Abdolali F, Zoroofi RA, Otake Y, Sato Y (2017) Automated classification of maxillofacial cysts in cone beam CT images using contourlet transformation and Spherical Harmonics. Comput Methods Programs Biomed 139:197\u2013207","journal-title":"Comput Methods Programs Biomed"},{"key":"4477_CR51","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.cmpb.2017.05.012","volume":"146","author":"E Yilmaz","year":"2017","unstructured":"Yilmaz E, Kayikcioglu T, Kayipmaz S (2017) Computer-aided diagnosis of periapical cyst and keratocystic odontogenic tumor on cone beam computed tomography. Comput Methods Programs Biomed 146:91\u2013100","journal-title":"Comput Methods Programs Biomed"},{"key":"4477_CR52","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1111\/odi.13223","volume":"26","author":"JH Lee","year":"2020","unstructured":"Lee JH, Kim DH, Jeong SN (2020) Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network. Oral Dis 26:152\u2013158","journal-title":"Oral Dis"},{"key":"4477_CR53","doi-asserted-by":"publisher","first-page":"14036","DOI":"10.1038\/s41598-018-32441-y","volume":"8","author":"BH Kann","year":"2018","unstructured":"Kann BH, Aneja S, Loganadane GV, Kelly JR, Smith SM, Decker RH, Yu JB, Park HS, Yarbrough WG, Malhotra A, Burtness BA, Husain ZA (2018) Pretreatment identification of head and neck cancer nodal metastasis and extranodal extension using deep learning neural networks. Sci Rep 8:14036","journal-title":"Sci Rep"},{"key":"4477_CR54","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1016\/j.oooo.2018.10.002","volume":"127","author":"Y Ariji","year":"2018","unstructured":"Ariji Y, Fukuda M, Kise Y, Nozawa M, Yanashita Y, Fujita H, Katsumata A, Ariji E (2018) 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. Oral Surg Oral Med Oral Pathol Oral Radiol 127:458\u2013463","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radiol"},{"key":"4477_CR55","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1067\/mod.2000.110168","volume":"118","author":"JK Liu","year":"2000","unstructured":"Liu JK, Chen YT, Cheng KS (2000) Accuracy of computerized automatic identification of cephalometric landmarks. Am J Orthod Dentofacial Orthop 118:535\u2013540","journal-title":"Am J Orthod Dentofacial Orthop"},{"key":"4477_CR56","first-page":"159","volume":"9","author":"S Rueda","year":"2006","unstructured":"Rueda S, Alca\u00f1iz M (2006) An approach for the automatic cephalometric landmark detection using mathematical morphology and active appearance models. Med Image Comput Comput Assist Interv 9:159\u2013166","journal-title":"Med Image Comput Comput Assist Interv"},{"key":"4477_CR57","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1093\/ejo\/cjp099","volume":"32","author":"P Vucini\u0107","year":"2010","unstructured":"Vucini\u0107 P, Trpovski Z, S\u0107epan I (2010) Automatic landmarking of cephalograms using active appearance models. Eur J Orthod 32:233\u2013241","journal-title":"Eur J Orthod"},{"key":"4477_CR58","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1097\/SCS.0000000000004901","volume":"30","author":"S Nishimoto","year":"2019","unstructured":"Nishimoto S, Sotsuka Y, Kawai K, Ishise H, Kakibuchi M (2019) Personal computer-based cephalometric landmark detection with deep learning, using cephalograms on the Internet. J Craniofacial Surg 30:91\u201395","journal-title":"J Craniofacial Surg"},{"key":"4477_CR59","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/s00056-019-00203-8","volume":"81","author":"F Kunz","year":"2020","unstructured":"Kunz F, Stellzig-Eisenhauer A, Zeman F, Boldt J (2020) Artificial intelligence in orthodontics: evaluation of a fully automated cephalometric analysis using a customized convolutional neural network. J Orofac Orthop 81:52\u201368","journal-title":"J Orofac Orthop"},{"key":"4477_CR60","doi-asserted-by":"publisher","first-page":"903","DOI":"10.2319\/022019-127.1","volume":"89","author":"JH Park","year":"2019","unstructured":"Park JH, Hwang HW, Moon JH, Yu Y, Kim H, Her SB, Srinivasan G, Aljanabi MNA, Donatelli RE, Lee SJ (2019) Automated identification of cephalometric landmarks: Part 1-Comparisons between the latest deep-learning methods YOLOV3 and SSD. Angle Orthod 89:903\u2013909","journal-title":"Angle Orthod"},{"key":"4477_CR61","doi-asserted-by":"publisher","first-page":"69","DOI":"10.2319\/022019-129.1","volume":"90","author":"HW Hwang","year":"2020","unstructured":"Hwang HW, Park JH, Moon JH, Yu Y, Kim H, Her SB, Srinivasan G, Aljanabi MNA, Donatelli RE, Lee SJ (2020) Automated identification of cephalometric landmarks: Part 2-Might it be better than human? Angle Orthod 90:69\u201376","journal-title":"Angle Orthod"},{"key":"4477_CR62","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1111\/adj.12479","volume":"62","author":"WC Scarfe","year":"2017","unstructured":"Scarfe WC, Azevedo B, Toghyani S, Farman AG (2017) Cone beam computed tomographic imaging in orthodontics. Aust Dent 62:33\u201350","journal-title":"Aust Dent"},{"key":"4477_CR63","first-page":"6204","volume":"2011","author":"E Cheng","year":"2011","unstructured":"Cheng E, Chen J, Yang J, Deng H, Wu Y, Megalooikonomou V, Gable B, Ling H (2011) Automatic dent-landmark detection in 3-D CBCT dental volumes. Annu Int Conf IEEE Eng Med Biol Soc 2011:6204\u20136207","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"4477_CR64","doi-asserted-by":"crossref","unstructured":"Gupta A, Kharbanda OP, Sardana V, Balachandran R, Sardana HK (2015) A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images. Int J Comput Assist Radiol Surg\u00a010:1737\u20131752","DOI":"10.1007\/s11548-015-1173-6"},{"key":"4477_CR65","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1007\/s11548-015-1334-7","volume":"11","author":"A Gupta","year":"2016","unstructured":"Gupta A, Kharbanda OP, Sardana V, Balachandran R, Sardana HK (2016) Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm. Int J Comput Assist Radiol Surg 11:1297\u20131309","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"4477_CR66","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1016\/j.ajodo.2017.06.028","volume":"153","author":"J Mont\u00fafar","year":"2018","unstructured":"Mont\u00fafar J, Romero M, Scougall-Vilchis RJ (2018) Automatic 3-dimensional cephalometric landmarking based on active shape models in related projections. Am J Orthod Dentofacial Orthop 153:449\u2013458","journal-title":"Am J Orthod Dentofacial Orthop"},{"key":"4477_CR67","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.ajodo.2017.08.028","volume":"154","author":"J Mont\u00fafar","year":"2018","unstructured":"Mont\u00fafar J, Romero M, Scougall-Vilchis RJ (2018) Hybrid approach for automatic cephalometric landmark annotation on cone-beam computed tomography volumes. Am J Orthod Dentofacial Orthop 154:140\u2013150","journal-title":"Am J Orthod Dentofacial Orthop"},{"key":"4477_CR68","doi-asserted-by":"publisher","first-page":"20170054","DOI":"10.1259\/dmfr.20170054","volume":"47","author":"BC Neelapu","year":"2018","unstructured":"Neelapu BC, Kharbanda OP, Sardana V, Gupta A, Vasamsetti S, Balachandran R, Sardana HK (2018) Automatic localization of three-dimensional cephalometric landmarks on CBCT images by extracting symmetry features of the skull. Dentomaxillofac Radiol 47:20170054","journal-title":"Dentomaxillofac Radiol"},{"key":"4477_CR69","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.ajodo.2015.07.030","volume":"149","author":"SK Jung","year":"2016","unstructured":"Jung SK, Kim TW (2016) New approach for the diagnosis of extractions with neural network machine learning. Am J Orthod Dentofacial Orthop 149:127\u2013133","journal-title":"Am J Orthod Dentofacial Orthop"},{"key":"4477_CR70","doi-asserted-by":"publisher","first-page":"55","DOI":"10.3390\/bioengineering7020055","volume":"7","author":"Y Suhail","year":"2020","unstructured":"Suhail Y, Upadhyay M, Chhibber A, Kshitiz, (2020) Machine learning for the diagnosis of orthodontic extractions: a computational analysis using ensemble learning. Bioengineering (Basel) 7:55","journal-title":"Bioengineering (Basel)"},{"key":"4477_CR71","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1186\/s12903-021-01513-3","volume":"21","author":"W Shin","year":"2021","unstructured":"Shin W, Yeom HG, Lee GH, Yun JP, Jeong SH, Lee JH, Kim HK, Kim BC (2021) Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals. BMC Oral Health 21:130","journal-title":"BMC Oral Health"},{"key":"4477_CR72","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1177\/0022034520901715","volume":"99","author":"HJ Yu","year":"2020","unstructured":"Yu HJ, Cho SR, Kim MJ, Kim WH, Kim JW, Choi J (2020) Automated skeletal classification with lateral cephalometry based on artificial intelligence. J Dent Res 99:249\u2013256","journal-title":"J Dent Res"},{"key":"4477_CR73","first-page":"1294","volume":"2020","author":"I Kim","year":"2020","unstructured":"Kim I, Misra D, Rodriguez L, Gill M, Liberton DK, Almpani K, Lee JS, Antani S (2020) Malocclusion classification on 3D cone-beam CT craniofacial images using multi-channel deep learning models. Annu Int Conf IEEE Eng Med Biol Soc 2020:1294\u20131298","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"4477_CR74","doi-asserted-by":"publisher","first-page":"S224","DOI":"10.1097\/SAP.0000000000002687","volume":"86","author":"LJ Lo","year":"2021","unstructured":"Lo LJ, Yang CT, Ho CT, Liao CH, Lin HH (2021) Automatic assessment of 3-dimensional facial soft tissue symmetry before and after orthognathic surgery using a machine learning model: a preliminary experience. Ann Plast Surg 86:S224\u2013S228","journal-title":"Ann Plast Surg"},{"key":"4477_CR75","doi-asserted-by":"crossref","unstructured":"Lin HH, Chiang WC, Yang CT, Cheng CT, Zhang T, Lo LJ (2021) On construction of transfer learning for facial symmetry assessment before and after orthognathic surgery. Comput Methods Programs Biomed\u00a0200:105928","DOI":"10.1016\/j.cmpb.2021.105928"},{"key":"4477_CR76","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1186\/s12880-021-00618-z","volume":"21","author":"S Kurt Bayrakdar","year":"2021","unstructured":"Kurt Bayrakdar S, Orhan K, Bayrakdar IS, Bilgir E, Ezhov M, Gusarev M, Shumilov E (2021) A deep learning approach for dental implant planning in cone-beam computed tomography images. BMC Med Imaging 21:86","journal-title":"BMC Med Imaging"},{"key":"4477_CR77","first-page":"584","volume":"2710","author":"TM Lehmann","year":"1996","unstructured":"Lehmann TM, Schmitt W, Horn H (1996) Hillen W (1996) Idefix: identification of dental fixtures in intraoral X rays Proc SPIE: Medical Imaging. Image Process 2710:584\u2013595","journal-title":"Image Process"},{"key":"4477_CR78","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.3390\/jcm9041117","volume":"9","author":"JE Kim","year":"2020","unstructured":"Kim JE, Nam NE, Shim JS, Jung YH, Cho BH, Hwang JJ (2020) Transfer learning via deep neural networks for implant fixture system classification using periapical radiographs. J Clin Med 9:1117","journal-title":"J Clin Med"},{"key":"4477_CR79","doi-asserted-by":"publisher","first-page":"910","DOI":"10.3390\/diagnostics10110910","volume":"10","author":"JH Lee","year":"2020","unstructured":"Lee JH, Kim YT, Lee JB, Jeong SN (2020) A performance comparison between automated deep learning and dental professionals in classification of dental implant systems from dental imaging: a multi-center study. Diagnostics 10:910","journal-title":"Diagnostics"},{"key":"4477_CR80","doi-asserted-by":"publisher","first-page":"984","DOI":"10.3390\/biom10070984","volume":"10","author":"S Sukegawa","year":"2020","unstructured":"Sukegawa S, Yoshii K, Hara T, Yamashita K, Nakano K, Yamamoto N, Nagatsuka H, Furuki Y (2020) Deep neural networks for dental implant system classification. Biomolecules 10:984","journal-title":"Biomolecules"},{"key":"4477_CR81","doi-asserted-by":"publisher","first-page":"815","DOI":"10.3390\/biom11060815","volume":"11","author":"S Sukegawa","year":"2021","unstructured":"Sukegawa S, Yoshii K, Hara T, Matsuyama T, Yamashita K, Nakano K, Takabatake K, Kawai H, Nagatsuka H, Furuki Y (2021) Multi-task deep learning model for classification of dental implant brand and treatment stage using dental panoramic radiograph images. Biomolecules 11:815","journal-title":"Biomolecules"},{"key":"4477_CR82","doi-asserted-by":"publisher","first-page":"233","DOI":"10.3390\/diagnostics11020233","volume":"11","author":"DW Lee","year":"2021","unstructured":"Lee DW, Kim SY, Jeong SN, Lee JH (2021) Artificial intelligence in fractured dental implant detection and classification: evaluation using dataset from two dental hospitals. Diagnostics (Basel) 11:233","journal-title":"Diagnostics (Basel)"},{"key":"4477_CR83","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/s12903-021-02035-8","volume":"22","author":"M Liu","year":"2022","unstructured":"Liu M, Wang S, Chen H, Liu Y (2022) A pilot study of a deep learning approach to detect marginal bone loss around implants. BMC Oral Health 22:11","journal-title":"BMC Oral Health"},{"key":"4477_CR84","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.3390\/jcm10051009","volume":"10","author":"JY Cha","year":"2021","unstructured":"Cha JY, Yoon HI, Yeo IS, Huh KH, Han JS (2021) Peri-implant bone loss measurement using a region-based convolutional neural network on dental periapical radiographs. J Clin Med 10:1009","journal-title":"J Clin Med"},{"key":"4477_CR85","unstructured":"Bornstein MM, Yeung WKA, Montalvao C, Colsoul N, Parker QA, Jacobs R) Facts and fallacies of radiation risk in dental radiology. Available online: http:\/\/facdent.hku.hk\/docs\/ke\/2019_Radiology_KE_booklet_en.pdf (accessed on 7 December 2021)"},{"key":"4477_CR86","first-page":"2615","volume":"2018","author":"X Du","year":"2018","unstructured":"Du X, Chen Y, Zhao J, Xi Y (2018) A convolutional neural network based auto-positioning method for dental arch in rotational panoramic radiography. Annu Int Conf IEEE Eng Med Biol Soc 2018:2615\u20132618","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"4477_CR87","first-page":"e823","volume":"46","author":"K Liang","year":"2019","unstructured":"Liang K, Zhang L, Yang H, Yang Y, Chen Z, Xing Y (2019) Metal artifact reduction for practical dental computed tomography by improving interpolation-based reconstruction with deep learning. Med Phys 46:e823-834","journal-title":"Med Phys"},{"key":"4477_CR88","doi-asserted-by":"publisher","first-page":"145011","DOI":"10.1088\/1361-6560\/aacdd4","volume":"63","author":"J Park","year":"2018","unstructured":"Park J, Hwang D, Kim KY, Kang SK, Kim YK, Lee JS (2018) Computed tomography super-resolution using deep convolutional neural network. Phys Med Biol 63:145011","journal-title":"Phys Med Biol"},{"key":"4477_CR89","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1109\/TRPMS.2018.2827239","volume":"3","author":"J Hatvani","year":"2018","unstructured":"Hatvani J, Horvath A, Michetti J, Basarab A, Kouame D, Gyongy M (2018) Deep learning-based super-resolution applied to dental computed tomography. IEEE Trans Radiat Plasma Med Sci 3:120\u2013128","journal-title":"IEEE Trans Radiat Plasma Med Sci"},{"key":"4477_CR90","doi-asserted-by":"publisher","first-page":"4501","DOI":"10.1007\/s00784-020-03315-3","volume":"24","author":"K Hung","year":"2020","unstructured":"Hung K, Hui L, Yeung AWK, Scarfe WC, Bornstein MM (2020) Image retake rates of cone beam computed tomography in a dental institution. Clin Oral Investig 24:4501\u20134510","journal-title":"Clin Oral Investig"},{"key":"4477_CR91","first-page":"20200171","volume":"50","author":"R Kuwana","year":"2021","unstructured":"Kuwana R, Ariji Y, Fukuda M, Kise Y, Nozawa M, Kuwada C, Muramatsu C, Katsumata A, Fujita H, Ariji E (2021) Performance of deep learning object detection technology in the detection and diagnosis of maxillary sinus lesions on panoramic radiographs. Dentomaxillofac Radiol 50:20200171","journal-title":"Dentomaxillofac Radiol"},{"key":"4477_CR92","doi-asserted-by":"publisher","unstructured":"Hung KF, Ai QYH, King AD, Bornstein MM, Wong LM, Leung YY (2022) Automatic detection and segmentation of morphological changes of the maxillary sinus mucosa on cone-beam computed tomography images using a three-dimensional convolutional neural network. Clin Oral Investig Epub ahead of print https:\/\/doi.org\/10.1007\/s00784-021-04365-x","DOI":"10.1007\/s00784-021-04365-x"},{"key":"4477_CR93","doi-asserted-by":"publisher","first-page":"20200611","DOI":"10.1259\/dmfr.20200611","volume":"50","author":"M Nishiyama","year":"2021","unstructured":"Nishiyama M, Ishibashi K, Ariji Y, Fukuda M, Nishiyama W, Umemura M, Katsumata A, Fujita H, Ariji E (2021) Performance of deep learning models constructed using panoramic radiographs from two hospitals to diagnose fractures of the mandibular condyle. Dentomaxillofac Radiol 50:20200611","journal-title":"Dentomaxillofac Radiol"},{"key":"4477_CR94","doi-asserted-by":"publisher","first-page":"20170006","DOI":"10.1259\/dmfr.20170006","volume":"46","author":"JJ Hwang","year":"2017","unstructured":"Hwang JJ, Lee JH, Han SS, Kim YH, Jeong HG, Choi YJ, Park W (2017) Strut analysis for osteoporosis detection model using dental panoramic radiography. Dentomaxillofac Radiol 46:20170006","journal-title":"Dentomaxillofac Radiol"},{"key":"4477_CR95","doi-asserted-by":"publisher","first-page":"20190019","DOI":"10.1259\/dmfr.20190019","volume":"48","author":"Y Kise","year":"2019","unstructured":"Kise Y, Ikeda H, Fujii T, Fukuda M, Ariji Y, Fujita H, Katsumata A, Ariji E (2019) Preliminary study on the application of deep learning system to diagnosis of Sj\u00f6gren\u2019s syndrome on CT images. Dentomaxillofac Radiol 48:20190019","journal-title":"Dentomaxillofac Radiol"},{"key":"4477_CR96","doi-asserted-by":"publisher","first-page":"e210014","DOI":"10.1148\/ryai.2021210014","volume":"3","author":"S Candemir","year":"2021","unstructured":"Candemir S, Nguyen XV, Folio LR, Prevedello LM (2021) Training strategies for radiology deep learning models in data-limited scenarios. Radiol Artif Intell 3:e210014","journal-title":"Radiol Artif Intell"},{"key":"4477_CR97","doi-asserted-by":"publisher","first-page":"109098","DOI":"10.1016\/j.jneumeth.2021.109098","volume":"353","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Hong D, McClement D, Oladosu O, Pridham G, Slaney G (2021) Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging. J Neurosci Methods 353:109098","journal-title":"J Neurosci Methods"},{"key":"4477_CR98","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1007\/s11548-021-02319-y","volume":"16","author":"H Chen","year":"2021","unstructured":"Chen H, Li H, Zhao Y, Zhao J, Wang Y (2021) Dental disease detection on periapical radiographs based on deep convolutional neural networks. Int J Comput Assist Radiol Surg 16:649\u2013661","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"4477_CR99","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1177\/0022034520978774","volume":"100","author":"T Joda","year":"2021","unstructured":"Joda T, Yeung AWK, Hung K, Zitzmann NU, Bornstein MM (2021) Disruptive innovation in dentistry: what it is and what could be next. J Dent Res 100:448\u2013453","journal-title":"J Dent Res"}],"container-title":["Clinical Oral Investigations"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00784-022-04477-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00784-022-04477-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00784-022-04477-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T16:07:59Z","timestamp":1664986079000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00784-022-04477-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,19]]},"references-count":99,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["4477"],"URL":"https:\/\/doi.org\/10.1007\/s00784-022-04477-y","relation":{},"ISSN":["1436-3771"],"issn-type":[{"value":"1436-3771","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,19]]},"assertion":[{"value":"20 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}