{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T20:29:24Z","timestamp":1790281764324,"version":"4.1.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,1,15]],"date-time":"2022-01-15T00:00:00Z","timestamp":1642204800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,15]],"date-time":"2022-01-15T00:00:00Z","timestamp":1642204800000},"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"],"published-print":{"date-parts":[[2022,5]]},"DOI":"10.1007\/s00784-021-04365-x","type":"journal-article","created":{"date-parts":[[2022,1,15]],"date-time":"2022-01-15T11:02:39Z","timestamp":1642244559000},"page":"3987-3998","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["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"],"prefix":"10.1007","volume":"26","author":[{"given":"Kuo Feng","family":"Hung","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi Yong H.","family":"Ai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ann D.","family":"King","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael M.","family":"Bornstein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lun M.","family":"Wong","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"}]}],"member":"297","published-online":{"date-parts":[[2022,1,15]]},"reference":[{"key":"4365_CR1","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":"4365_CR2","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1016\/j.oooo.2015.07.033","volume":"120","author":"AAE and AAOMR Joint Position Statement","year":"2015","unstructured":"AAE and AAOMR Joint Position Statement (2015) Use of cone beam computed tomography in endodontics 2015 update. Oral Surg Oral Med Oral Pathol Oral Radiol 120:508\u2013512","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radiol"},{"key":"4365_CR3","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1016\/j.jcms.2019.03.006","volume":"47","author":"P Procacci","year":"2019","unstructured":"Procacci P, Lanaro L, Trevisiol L, Bertossi D, Zotti F, Fabio L, D\u2019Agostino A (2019) Is post orthognathic maxillary sinusitis related to sino-nasal anatomical alterations? J Craniomaxillofac Surg 47:876\u2013882","journal-title":"J Craniomaxillofac Surg"},{"key":"4365_CR4","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1111\/clr.13582","volume":"31","author":"SFM Janner","year":"2020","unstructured":"Janner SFM, Dubach P, Suter VGA, Caversaccio MD, Buser D, Bornstein MM (2020) Sinus floor elevation or referral for further diagnosis and therapy: a comparison of maxillary sinus assessment by ENT specialists and dentists using cone beam computed tomography. Clin Oral Implants Res 31:463\u2013475","journal-title":"Clin Oral Implants Res"},{"key":"4365_CR5","doi-asserted-by":"publisher","first-page":"55","DOI":"10.11607\/jomi.2014suppl.g1.4","volume":"29","author":"MM Bornstein","year":"2014","unstructured":"Bornstein MM, Scarfe WC, Vaughn VM, Jacobs R (2014) Cone beam computed tomography in implant dentistry: a systematic review focusing on guidelines, indications, and radiation dose risks. Int J Oral Maxillofac Implants 29:55\u201377","journal-title":"Int J Oral Maxillofac Implants"},{"key":"4365_CR6","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1111\/clr.13259","volume":"29","author":"K Zhao","year":"2018","unstructured":"Zhao K, Lian M, Fan S, Huang W, Wang F, Wu Y (2018) Long-term Schneiderian membrane thickness changes following zygomatic implant placement: a retrospective radiographic analysis using cone beam computed tomography. Clin Oral Implants Res 29:679\u2013687","journal-title":"Clin Oral Implants Res"},{"key":"4365_CR7","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1111\/prd.12161","volume":"73","author":"MM Bornstein","year":"2000","unstructured":"Bornstein MM, Horner K (2000) Jacobs R (2017) Use of cone beam computed tomography in implant dentistry: current concepts, indications and limitations for clinical practice and research. Periodontol 73:51\u201372","journal-title":"Periodontol"},{"key":"4365_CR8","doi-asserted-by":"publisher","first-page":"20200171","DOI":"10.1259\/dmfr.20200171","volume":"50","author":"R Kuwana","year":"2020","unstructured":"Kuwana R, Ariji Y, Fukuda M, Kise Y, Nozawa M, Kuwada C, Muramatsu C, Katsumata A, Fujita H, Ariji E (2020) 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":"4365_CR9","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1007\/s11282-020-00485-4","volume":"37","author":"H Watanabe","year":"2020","unstructured":"Watanabe H, Ariji Y, Fukuda M, Kuwada C, Kise Y, Nozawa M, Sugita Y, Ariji E (2020) Deep learning object detection of maxillary cyst-like lesions on panoramic radiographs: preliminary study. Oral Radiol 37:487\u2013493","journal-title":"Oral Radiol"},{"key":"4365_CR10","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1007\/s11282-018-0363-7","volume":"35","author":"M Murata","year":"2019","unstructured":"Murata M, Ariji Y, Ohashi Y, Kawai T, Fukuda M, Funakoshi T, Kise Y, Nozawa M, Katsumata A, Fujita H, Ariji E (2019) Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiol 35:301\u2013307","journal-title":"Oral Radiol"},{"key":"4365_CR11","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":"4365_CR12","doi-asserted-by":"publisher","first-page":"1457","DOI":"10.1007\/s11548-020-02228-6","volume":"15","author":"J Xu","year":"2020","unstructured":"Xu J, Wang S, Zhou Z, Liu J, Jiang X, Chen X (2020) Automatic CT image segmentation of maxillary sinus based on VGG network and improved V-Net. Int J Comput Assist Radiol Surg 15:1457\u20131465","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"4365_CR13","first-page":"5689301","volume":"2020","author":"D Zhuofu","year":"2020","unstructured":"Zhuofu D, Binbin W, Zhiliang Z (2020) BE-FNet: 3D bounding box estimation feature pyramid network for accurate and efficient maxillary sinus segmentation. Math Probl Eng 2020:5689301","journal-title":"Math Probl Eng"},{"key":"4365_CR14","doi-asserted-by":"publisher","first-page":"688","DOI":"10.3390\/diagnostics11040688","volume":"11","author":"SK Jung","year":"2021","unstructured":"Jung SK, Lim HK, Lee S, Cho Y, Song IS (2021) Deep active learning for automatic segmentation of maxillary sinus lesions using a convolutional neural network. Diagnostics 11:688","journal-title":"Diagnostics"},{"key":"4365_CR15","doi-asserted-by":"publisher","first-page":"699","DOI":"10.11607\/prd.3435","volume":"38","author":"MM Bornstein","year":"2018","unstructured":"Bornstein MM, Yeung A, Tanaka R, von Arx T, Jacobs R, Khong PL (2018) Evaluation of health or pathology of bilateral maxillary sinuses in patients referred for cone beam computed tomography using a low-dose protocol. Int J Periodontics Restorative Dent 38:699\u2013710","journal-title":"Int J Periodontics Restorative Dent"},{"key":"4365_CR16","doi-asserted-by":"publisher","first-page":"103610","DOI":"10.1016\/j.jdent.2021.103610","volume":"107","author":"F Schwendicke","year":"2021","unstructured":"Schwendicke F, Singh T, Lee JH, Gaudin R, Chaurasia A, Wiegand T, Uribe S, Krois J, IADR e-oral health network and the ITU WHO focus group AI for Health (2021) Artificial intelligence in dental research: checklist for authors, reviewers, readers. J Dent 107:103610","journal-title":"J Dent"},{"key":"4365_CR17","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1016\/S1079-2104(05)80375-0","volume":"80","author":"K Soikkonen","year":"1995","unstructured":"Soikkonen K, Ainamo A (1995) Radiographic maxillary sinus findings in the elderly. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 80:487\u2013491","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radiol Endod"},{"key":"4365_CR18","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/s00276-019-02308-6","volume":"42","author":"K Hung","year":"2020","unstructured":"Hung K, Montalvao C, Yeung AWK, Li G, Bornstein MM (2020) Frequency, location, and morphology of accessory maxillary sinus ostia: a retrospective study using cone beam computed tomography (CBCT). Surg Radiol Anat 42:219\u2013228","journal-title":"Surg Radiol Anat"},{"key":"4365_CR19","doi-asserted-by":"publisher","first-page":"3977","DOI":"10.1007\/s00784-019-02829-9","volume":"23","author":"AWK Yeung","year":"2019","unstructured":"Yeung AWK, Colsoul N, Montalvao C, Hung K, Jacobs R, Bornstein MM (2019) Visibility, location, and morphology of the primary maxillary sinus ostium and presence of accessory ostia: a retrospective analysis using cone beam computed tomography (CBCT). Clin Oral Investig 23:3977\u20133986","journal-title":"Clin Oral Investig"},{"key":"4365_CR20","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","volume":"31","author":"PA Yushkevich","year":"2006","unstructured":"Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31:1116\u20131128","journal-title":"Neuroimage"},{"key":"4365_CR21","doi-asserted-by":"publisher","first-page":"103742","DOI":"10.1016\/j.jece.2020.103742","volume":"8","author":"MS Zaghloul","year":"2020","unstructured":"Zaghloul MS, Hamza RA, Iorhemen OT, Tay JH (2020) Comparison of adaptive neuro-fuzzy inference systems (ANFIS) and support vector regression (SVR) for data-driven modelling of aerobic granular sludge reactors. J Environ Chem Eng 8:103742","journal-title":"J Environ Chem Eng"},{"key":"4365_CR22","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S (2016) V-Net: fully convolutional neural networks for volumetric medical image segmentation. arXiv:160604797v1 565\u2013571","DOI":"10.1109\/3DV.2016.79"},{"key":"4365_CR23","first-page":"8024","volume":"32","author":"A Paszke","year":"2019","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) PyTorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst 32:8024\u20138035","journal-title":"Adv Neural Inf Process Syst"},{"key":"4365_CR24","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1148\/radiol.2017171920","volume":"286","author":"SH Park","year":"2018","unstructured":"Park SH, Han K (2018) Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology 286:800\u2013809","journal-title":"Radiology"},{"key":"4365_CR25","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s12880-015-0068-x","volume":"15","author":"AA Taha","year":"2015","unstructured":"Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging BMC Med Imaging 15:29","journal-title":"BMC Med Imaging BMC Med Imaging"},{"key":"4365_CR26","doi-asserted-by":"publisher","first-page":"20150419","DOI":"10.1259\/dmfr.20150419","volume":"45","author":"Y Ohashi","year":"2016","unstructured":"Ohashi Y, Ariji Y, Katsumata A, Fujita H, Nakayama M, Fukuda M, Nozawa M, Ariji E (2016) Utilization of computer-aided detection system in diagnosing unilateral maxillary sinusitis on panoramic radiographs. Dentomaxillofac Radiol 45:20150419","journal-title":"Dentomaxillofac Radiol"},{"key":"4365_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5624\/isd.2014.44.1.1","volume":"44","author":"RH Rondon","year":"2014","unstructured":"Rondon RH, Pereira YC, doNascimento GC (2014) Common positioning errors in panoramic radiography: a review. Imaging Sci Dent 44:1\u20136","journal-title":"Imaging Sci Dent"},{"key":"4365_CR28","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.visres.2020.04.003","volume":"172","author":"N Baker","year":"2020","unstructured":"Baker N, Lu H, Erlikhman G, Kellman PJ (2020) Local features and global shape information in object classification by deep convolutional neural networks. Vision Res 172:46\u201361","journal-title":"Vision Res"},{"key":"4365_CR29","first-page":"1205","volume":"27","author":"MI Kara","year":"2012","unstructured":"Kara MI, Kirmali O, Ay S (2012) Clinical evaluation of lateral and osteotome techniques for sinus floor elevation in the presence of an antral pseudocyst. Int J Oral Maxillofac Implants 27:1205\u20131210","journal-title":"Int J Oral Maxillofac Implants"},{"key":"4365_CR30","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1016\/j.bjoms.2019.04.007","volume":"57","author":"T Gong","year":"2019","unstructured":"Gong T, Hu C, Chen Y, Zhou N, Wu H, Man Y (2019) Raising the transcrestal sinus floor in the presence of antral pseudocysts, and in sinus floors with a normal Schneiderian membrane: a retrospective cohort study. Br J Oral Maxillofac Surg 57:466\u2013472","journal-title":"Br J Oral Maxillofac Surg"},{"key":"4365_CR31","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1002\/lary.26856","volume":"128","author":"YW Chen","year":"2018","unstructured":"Chen YW, Lee FY, Chang PH, Huang CC, Fu CH, Huang CC, Lee TJ (2018) A paradigm for evaluation and management of the maxillary sinus before dental implantation. Laryngoscope 128:1261\u20131267","journal-title":"Laryngoscope"},{"key":"4365_CR32","doi-asserted-by":"publisher","first-page":"807","DOI":"10.11607\/prd.2043","volume":"34","author":"B Friedland","year":"2014","unstructured":"Friedland B, Metson R (2014) A guide to recognizing maxillary sinus pathology and for deciding on further preoperative assessment prior to maxillary sinus augmentation. Int J Periodontics Restorative Dent 34:807\u2013815","journal-title":"Int J Periodontics Restorative Dent"},{"key":"4365_CR33","doi-asserted-by":"publisher","first-page":"117","DOI":"10.5624\/isd.20200267","volume":"51","author":"K Hung","year":"2021","unstructured":"Hung K, Hui L, Yeung AWK, Wu Y, Hsung RT, Bornstein MM (2021) Volumetric analysis of mucous retention cysts in the maxillary sinus: a retrospective study using cone-beam computed tomography. Imaging Sci Dent 51:117\u2013127","journal-title":"Imaging Sci Dent"},{"key":"4365_CR34","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.compbiomed.2016.11.003","volume":"80","author":"Y Miki","year":"2017","unstructured":"Miki Y, Muramatsu C, Hayashi T, Zhou X, Hara T, Katsumata A, Fujita H (2017) Classification of teeth in cone-beam CT using deep convolutional neural network. Comput Biol Med 80:24\u201329","journal-title":"Comput Biol Med"},{"key":"4365_CR35","doi-asserted-by":"publisher","first-page":"20180051","DOI":"10.1259\/dmfr.20180051","volume":"48","author":"DV Tuzoff","year":"2019","unstructured":"Tuzoff DV, Tuzova LN, Bornstein MM, Krasnov AS, Kharchenko MA, Nikolenko SI, Sveshnikov MM, Bednenko GB (2019) Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofac Radiol 48:20180051","journal-title":"Dentomaxillofac Radiol"},{"key":"4365_CR36","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":"4365_CR37","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"},{"key":"4365_CR38","first-page":"248","volume":"51","author":"YW Chen","year":"2020","unstructured":"Chen YW, Stanley K, Att W (2020) Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int 51:248\u2013257","journal-title":"Quintessence Int"},{"key":"4365_CR39","doi-asserted-by":"publisher","first-page":"2573","DOI":"10.1007\/s00784-019-02907-y","volume":"23","author":"AWK Yeung","year":"2019","unstructured":"Yeung AWK, Jacobs R, Bornstein MM (2019) Novel low-dose protocols using cone beam computed tomography in dental medicine: a review focusing on indications, limitations, and future possibilities. Clin Oral Investig 23:2573\u20132581","journal-title":"Clin Oral Investig"}],"container-title":["Clinical Oral Investigations"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00784-021-04365-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00784-021-04365-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00784-021-04365-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T06:15:14Z","timestamp":1651731314000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00784-021-04365-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,15]]},"references-count":39,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,5]]}},"alternative-id":["4365"],"URL":"https:\/\/doi.org\/10.1007\/s00784-021-04365-x","relation":{},"ISSN":["1436-3771"],"issn-type":[{"value":"1436-3771","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,15]]},"assertion":[{"value":"22 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 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":"All procedures performed were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study protocol was submitted to and approved by the local institutional review board (IRB) of the University of Hong Kong\/Hospital Authority Hong Kong West Cluster (approval number UW 21-446).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"For this type of study (retrospective study), formal consent is not required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}