{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T19:12:26Z","timestamp":1782933146598,"version":"3.54.5"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,3,15]],"date-time":"2023-03-15T00:00:00Z","timestamp":1678838400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,15]],"date-time":"2023-03-15T00:00:00Z","timestamp":1678838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Small and Medium Enterprises Development Organization of Turkey","award":["62146"],"award-info":[{"award-number":["62146"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Oral Radiol"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s11282-023-00677-8","type":"journal-article","created":{"date-parts":[[2023,3,15]],"date-time":"2023-03-15T10:03:22Z","timestamp":1678874602000},"page":"614-628","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["ResMIBCU-Net: an encoder\u2013decoder network with residual blocks, modified inverted residual block, and bi-directional ConvLSTM for impacted tooth segmentation in panoramic X-ray images"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3654-040X","authenticated-orcid":false,"given":"Anda\u00e7","family":"Imak","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adalet","family":"\u00c7elebi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Onur","family":"Polat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muammer","family":"T\u00fcrko\u011flu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulkadir","family":"\u015eeng\u00fcr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,3,15]]},"reference":[{"issue":"4","key":"677_CR1","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.jormas.2020.12.006","volume":"122","author":"K Orhan","year":"2021","unstructured":"Orhan K, Bilgir E, Bayrakdar IS, Ezhov M, Gusarev M, Shumilov E. Evaluation of artificial intelligence for detecting impacted third molars on cone-beam computed tomography scans. J Stomatol Oral Maxillofac Surg. 2021;122(4):333\u20137. https:\/\/doi.org\/10.1016\/j.jormas.2020.12.006.","journal-title":"J Stomatol Oral Maxillofac Surg."},{"issue":"5","key":"677_CR2","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1016\/j.ajoms.2022.02.004","volume":"34","author":"S Tajima","year":"2022","unstructured":"Tajima S, Okamoto Y, Kobayashi T, Kiwaki M, Sonoda C, Tomie K, et al. Development of an automatic detection model using artificial intelligence for the detection of cyst-like radiolucent lesions of the jaws on panoramic radiographs with small training datasets. J Oral Maxillofac Surg Med Pathol. 2022;34(5):553\u201360. https:\/\/doi.org\/10.1016\/j.ajoms.2022.02.004.","journal-title":"J Oral Maxillofac Surg Med Pathol"},{"key":"677_CR3","doi-asserted-by":"crossref","unstructured":"Faure J, Engelbrecht A. 2021. Impacted tooth detection in panoramic radiographs. In: International work-conference on artificial neural networks, vol\u00a012861. Springer, Cham, pp\u00a0525\u2013536.","DOI":"10.1007\/978-3-030-85030-2_43"},{"key":"677_CR4","doi-asserted-by":"publisher","unstructured":"Padilla R, Netto SL, da Silva EAB. A survey on performance metrics for object-detection algorithms. In: 2020 \u0131nternational conference on systems, signals and \u0131mage processing (IWSSIP). 2020;237\u2013242. https:\/\/doi.org\/10.1109\/IWSSIP48289.2020.9145130","DOI":"10.1109\/IWSSIP48289.2020.9145130"},{"issue":"1","key":"677_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13755-019-0096-y","volume":"8","author":"V Geetha","year":"2020","unstructured":"Geetha V, Aprameya KS, Hinduja DM. Dental caries diagnosis in digital radiographs using back-propagation neural network. Health Inform Sci Syst. 2020;8(1):1\u201314. https:\/\/doi.org\/10.1007\/s13755-019-0096-y.","journal-title":"Health Inform Sci Syst"},{"key":"677_CR6","doi-asserted-by":"publisher","first-page":"103425","DOI":"10.1016\/j.jdent.2020.103425","volume":"100","author":"AG Cantu","year":"2020","unstructured":"Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi JG, Gaudin R, et al. Detecting caries lesions of different radiographic extension on bitewings using deep learning. J Dentistry. 2020;100:103425. https:\/\/doi.org\/10.1016\/j.jdent.2020.103425.","journal-title":"J Dentistry."},{"issue":"3","key":"677_CR7","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/s11282-018-0354-8","volume":"36","author":"R Obuchowicz","year":"2020","unstructured":"Obuchowicz R, Nurzynska K, Obuchowicz B, Urbanik A, Pi\u00f3rkowski A. Caries detection enhancement using texture feature maps of intraoral radiographs. Oral Radiol. 2020;36(3):275\u201387. https:\/\/doi.org\/10.1007\/s11282-018-0354-8.","journal-title":"Oral Radiol"},{"key":"677_CR8","doi-asserted-by":"publisher","first-page":"18320","DOI":"10.1109\/ACCESS.2022.3150358","volume":"10","author":"A Imak","year":"2022","unstructured":"Imak A, Celebi A, Siddique K, Turkoglu M, Sengur A, Salam I. Dental caries detection using score-based multi-input deep convolutional neural network. IEEE Access. 2022;10:18320\u20139. https:\/\/doi.org\/10.1109\/ACCESS.2022.3150358.","journal-title":"IEEE Access."},{"key":"677_CR9","doi-asserted-by":"crossref","unstructured":"Lakshmi MM, Chitra P. 2020. Tooth decay prediction and classification from X-ray images using deep CNN. In: Proceedings of the 2020 \u0131nternational conference on communication and signal processing (ICCSP), pp\u00a01349\u20131355.","DOI":"10.1109\/ICCSP48568.2020.9182141"},{"issue":"4","key":"677_CR10","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, et al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiol. 2020;36(4):337\u201343. https:\/\/doi.org\/10.1007\/s11282-019-00409-x.","journal-title":"Oral Radiol"},{"issue":"3","key":"677_CR11","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, et al. Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiol. 2019;35(3):301\u20137. https:\/\/doi.org\/10.1007\/s11282-018-0363-7.","journal-title":"Oral Radiol"},{"issue":"1","key":"677_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-45487-3","volume":"9","author":"S Vinayahalingam","year":"2019","unstructured":"Vinayahalingam S, Xi T, Berg\u00e9 S, Maal T, De Jong G. Automated detection of third molars and mandibular nerve by deep learning. Sci Rep. 2019;9(1):1\u20137. https:\/\/doi.org\/10.1038\/s41598-019-45487-3.","journal-title":"Sci Rep"},{"issue":"2","key":"677_CR13","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1111\/ocr.12297","volume":"22","author":"M Vranckx","year":"2019","unstructured":"Vranckx M, Ockerman A, Coucke W, Claerhout E, Grommen B, Miclotte A, et al. Radiographic prediction of mandibular third molar eruption and mandibular canal involvement based on angulation. Orthod Craniofac Res. 2019;22(2):118\u201323. https:\/\/doi.org\/10.1111\/ocr.12297.","journal-title":"Orthod Craniofac Res"},{"issue":"4","key":"677_CR14","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, et al. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofac Radiol. 2019;48(4):20180051. https:\/\/doi.org\/10.1259\/dmfr.20180051.","journal-title":"Dentomaxillofac Radiol"},{"key":"677_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-021-10721-5","author":"A Imak","year":"2022","unstructured":"Imak A, \u00c7elebi A, T\u00fcrko\u011flu M, \u015eeng\u00fcr A. Dental material detection based on faster regional convolutional neural networks and shape features. Neural Process Lett. 2022. https:\/\/doi.org\/10.1007\/s11063-021-10721-5.","journal-title":"Neural Process Lett"},{"issue":"1","key":"677_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-40414-y","volume":"9","author":"H Chen","year":"2019","unstructured":"Chen H, Zhang K, Lyu P, Li H, Zhang L, Wu J, Lee CH. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Sci Rep. 2019;9(1):1\u201311. https:\/\/doi.org\/10.1038\/s41598-019-40414-y.","journal-title":"Sci Rep"},{"issue":"5","key":"677_CR17","doi-asserted-by":"publisher","first-page":"1831","DOI":"10.1007\/s00414-020-02283-3","volume":"134","author":"N Banar","year":"2020","unstructured":"Banar N, Bertels J, Laurent F, Boedi RM, De Tobel J, Thevissen P, Vandermeulen D. Towards fully automated third molar development staging in panoramic radiographs. Int J Legal Med. 2020;134(5):1831\u201341. https:\/\/doi.org\/10.1007\/s00414-020-02283-3.","journal-title":"Int J Legal Med"},{"issue":"7","key":"677_CR18","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. Deep learning for the radiographic detection of apical lesions. J Endodont. 2019;45(7):917\u201322. https:\/\/doi.org\/10.1016\/j.joen.2019.03.016.","journal-title":"J Endodont."},{"issue":"4","key":"677_CR19","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1016\/j.oooo.2020.04.813","volume":"130","author":"C Kuwada","year":"2020","unstructured":"Kuwada C, Ariji Y, Fukuda M, Kise Y, Fujita H, Katsumata A, Ariji E. Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020;130(4):464\u20139. https:\/\/doi.org\/10.1016\/j.oooo.2020.04.813.","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radiol"},{"issue":"1","key":"677_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-29934-1","volume":"8","author":"W Zhang","year":"2018","unstructured":"Zhang W, Li J, Li ZB, Li Z. Predicting postoperative facial swelling following impacted mandibular third molars extraction by using artificial neural networks evaluation. Sci Rep. 2018;8(1):1\u20139. https:\/\/doi.org\/10.1038\/s41598-018-29934-1.","journal-title":"Sci Rep"},{"issue":"3","key":"677_CR21","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/s11282-021-00572-0","volume":"38","author":"M Ba\u015faran","year":"2022","unstructured":"Ba\u015faran M, \u00c7elik \u00d6, Bayrakdar IS, Bilgir E, Orhan K, Odaba\u015f A, et al. Diagnostic charting of panoramic radiography using deep-learning artificial intelligence system. Oral Radiol. 2022;38(3):363\u20139. https:\/\/doi.org\/10.1007\/s11282-021-00572-0.","journal-title":"Oral Radiol"},{"issue":"4","key":"677_CR22","doi-asserted-by":"publisher","first-page":"942","DOI":"10.3390\/diagnostics12040942","volume":"12","author":"ME Celik","year":"2022","unstructured":"Celik ME. Deep learning based detection tool for impacted mandibular third molar teeth. Diagnostics. 2022;12(4):942. https:\/\/doi.org\/10.3390\/diagnostics12040942.","journal-title":"Diagnostics"},{"key":"677_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07419-7","author":"M Mubashar","year":"2022","unstructured":"Mubashar M, Ali H, Gr\u00f6nlund C, Azmat S. R2U++: a multiscale recurrent residual U-Net with dense skip connections for medical image segmentation. Neural Comput Appl. 2022. https:\/\/doi.org\/10.1007\/s00521-022-07419-7.","journal-title":"Neural Comput Appl"},{"key":"677_CR24","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1505.04597","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. Proc Int Conf Med Image Comput Computer-Assisted Intervent. 2015. https:\/\/doi.org\/10.48550\/arXiv.1505.04597.","journal-title":"Proc Int Conf Med Image Comput Computer-Assisted Intervent."},{"key":"677_CR25","doi-asserted-by":"publisher","first-page":"448","DOI":"10.48550\/arXiv.1502.03167","volume":"37","author":"S Ioffe","year":"2015","unstructured":"Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. Proc Int Conf Mach Learn. 2015;37:448\u201356. https:\/\/doi.org\/10.48550\/arXiv.1502.03167.","journal-title":"Proc Int Conf Mach Learn."},{"key":"677_CR26","doi-asserted-by":"publisher","first-page":"103137","DOI":"10.1016\/j.bspc.2021.103137","volume":"71","author":"N Badshah","year":"2022","unstructured":"Badshah N, Ahmad A. ResBCU-Net: deep learning approach for segmentation of skin images. Biomed Signal Process Control. 2022;71:103137. https:\/\/doi.org\/10.1016\/j.bspc.2021.103137.","journal-title":"Biomed Signal Process Control"},{"key":"677_CR27","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1801.04381","author":"M Sandler","year":"2018","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC. Mobilenetv 2: inverted residuals and linear bottlenecks. Proc IEEE Conf Comput Vision Pattern Recognit. 2018. https:\/\/doi.org\/10.48550\/arXiv.1801.04381.","journal-title":"Proc IEEE Conf Comput Vision Pattern Recognit."},{"key":"677_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-020-01248-7","author":"DN Le","year":"2021","unstructured":"Le DN, Parvathy VS, Gupta D, Khanna A, Rodrigues JJ, Shankar K. IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification. Int J Mach Learn Cybernet. 2021. https:\/\/doi.org\/10.1007\/s13042-020-01248-7.","journal-title":"Int J Mach Learn Cybernet."},{"key":"677_CR29","doi-asserted-by":"publisher","first-page":"103819","DOI":"10.1016\/j.imavis.2019.10.005","volume":"92","author":"Y Li","year":"2019","unstructured":"Li Y, Zhang D, Lee DJ. IIRNet: a lightweight deep neural network using intensely inverted residuals for image recognition. Image Vision Comput. 2019;92:103819. https:\/\/doi.org\/10.1016\/j.imavis.2019.10.005.","journal-title":"Image Vision Comput."},{"key":"677_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2021.101325","author":"W Boulila","year":"2021","unstructured":"Boulila W, Ghandorh H, Khan MA, Ahmed F, Ahmad J. A novel CNN-LSTM-based approach to predict urban expansion. Ecol Inform. 2021. https:\/\/doi.org\/10.1016\/j.ecoinf.2021.101325.","journal-title":"Ecol Inform."},{"issue":"8","key":"677_CR31","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735\u201380. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735.","journal-title":"Neural Comput"},{"key":"677_CR32","doi-asserted-by":"publisher","first-page":"109090","DOI":"10.1016\/j.measurement.2021.109090","volume":"176","author":"G Huang","year":"2021","unstructured":"Huang G, Zhang Y, Ou J. Transfer remaining useful life estimation of bearing using depth-wise separable convolution recurrent network. Measurement. 2021;176:109090. https:\/\/doi.org\/10.1016\/j.measurement.2021.109090.","journal-title":"Measurement"},{"key":"677_CR33","unstructured":"Albumentations. https:\/\/albumentations.ai\/. 2022"},{"key":"677_CR34","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1909.00166","author":"R Azad","year":"2019","unstructured":"Azad R, Asadi-Aghbolaghi M, Fathy M, Escalera S. Bi-directional ConvLSTM U-Net with densley connected convolutions. Proc IEEE\/CVF Int Conf Comput Vision Workshops. 2019. https:\/\/doi.org\/10.48550\/arXiv.1909.00166.","journal-title":"Proc IEEE\/CVF Int Conf Comput Vision Workshops."},{"key":"677_CR35","doi-asserted-by":"publisher","DOI":"10.1109\/ISM46123.2019.00049","author":"D Jha","year":"2019","unstructured":"Jha D, Smedsrud PH, Riegler MA, Johansen D, De Lange T, Halvorsen P, Johansen HD. Resunet++: an advanced architecture for medical image segmentation. 2019 IEEE Int Symp Multimed (ISM). 2019. https:\/\/doi.org\/10.1109\/ISM46123.2019.00049.","journal-title":"2019 IEEE Int Symp Multimed (ISM)."}],"container-title":["Oral Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11282-023-00677-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11282-023-00677-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11282-023-00677-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T19:08:52Z","timestamp":1694804932000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11282-023-00677-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,15]]},"references-count":35,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["677"],"URL":"https:\/\/doi.org\/10.1007\/s11282-023-00677-8","relation":{},"ISSN":["0911-6028","1613-9674"],"issn-type":[{"value":"0911-6028","type":"print"},{"value":"1613-9674","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,15]]},"assertion":[{"value":"18 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Prior to the study, an approval was obtained from Firat University Noninterventional Clinical Research Ethics Committee (Approval Date: December 31, 2020; No: 2020\/17-15).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}