{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T01:46:31Z","timestamp":1780710391525,"version":"3.54.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T00:00:00Z","timestamp":1660780800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T00:00:00Z","timestamp":1660780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Division for Research Development at Stellenbosch University"},{"name":"Ball Family Foundation"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s11517-022-02642-9","type":"journal-article","created":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T03:53:31Z","timestamp":1660794811000},"page":"2951-2968","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Deep learning-based dental implant recognition using synthetic X-ray images"],"prefix":"10.1007","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1436-5185","authenticated-orcid":false,"given":"Aviwe","family":"Kohlakala","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Coetzer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeroen","family":"Bertels","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dirk","family":"Vandermeulen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,8,18]]},"reference":[{"issue":"1","key":"2642_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-20657-4","volume":"12","author":"N Lassau","year":"2021","unstructured":"Lassau N, Ammari S, Chouzenoux E, Gortais H, Herent P, Devilder M, Soliman S, Meyrignac O, Talabard M-P, Lamarque J-P et al (2021) Integrating deep learning ct-scan model, biological and clinical variables to predict severity of covid-19 patients. Nature communications 12(1):1\u201311","journal-title":"Nature communications"},{"issue":"1","key":"2642_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-021-00438-z","volume":"4","author":"R Aggarwal","year":"2021","unstructured":"Aggarwal R, Sounderajah V, Martin G, Ting DS, Karthikesalingam A, King D, Ashrafian H, Darzi A (2021) Diagnostic accuracy of deep learning in medical imaging: A systematic review and meta-analysis. NPJ Digital Medicine 4(1):1\u201323","journal-title":"NPJ Digital Medicine"},{"key":"2642_CR3","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2642_CR4","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S-A (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV). IEEE, pp 565\u2013571","DOI":"10.1109\/3DV.2016.79"},{"key":"2642_CR5","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin P-M, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Medical Image Analysis 35:18\u201331","journal-title":"Medical Image Analysis"},{"issue":"4","key":"2642_CR6","first-page":"119","volume":"12","author":"M Fu","year":"2018","unstructured":"Fu M, Wu W, Hong X, Liu Q, Jiang J, Ou Y, Zhao Y, Gong X (2018) Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images. BMC Systems Biology 12(4):119\u2013127","journal-title":"BMC Systems Biology"},{"key":"2642_CR7","doi-asserted-by":"crossref","unstructured":"Jasti V, Zamani AS, Arumugam K, Naved M, Pallathadka H, Sammy F, Raghuvanshi A, Kaliyaperumal K (2022) Computational technique based on machine learning and image processing for medical image analysis of breast cancer diagnosis. Secur Commun Netw 2022","DOI":"10.1155\/2022\/1918379"},{"issue":"1","key":"2642_CR8","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/s42600-021-00151-6","volume":"38","author":"E Luz","year":"2022","unstructured":"Luz E, Silva P, Silva R, Silva L, Guimar\u00e3es J, Miozzo G, Moreira G, Menotti D (2022) Towards an effective and efficient deep learning model for covid-19 patterns detection in x-ray images. Research on Biomedical Engineering 38(1):149\u2013162","journal-title":"Research on Biomedical Engineering"},{"key":"2642_CR9","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.cviu.2014.12.006","volume":"137","author":"A Rozantsev","year":"2015","unstructured":"Rozantsev A, Lepetit V, Fua P (2015) On rendering synthetic images for training an object detector. Computer Vision and Image Understanding 137:24\u201337","journal-title":"Computer Vision and Image Understanding"},{"key":"2642_CR10","doi-asserted-by":"crossref","unstructured":"Tremblay J, Prakash A, Acuna D, Brophy M, Jampani V, Anil C, To T, Cameracci E, Boochoon S, Birchfield S (2018) Training deep networks with synthetic data: Bridging the reality gap by domain randomization. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. pp 969\u2013977","DOI":"10.1109\/CVPRW.2018.00143"},{"key":"2642_CR11","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.neucom.2016.09.116","volume":"257","author":"S Yu","year":"2017","unstructured":"Yu S, Wu Y, Li W, Song Z, Zeng W (2017) A model for fine-grained vehicle classification based on deep learning. Neurocomputing 257:97\u2013103","journal-title":"Neurocomputing"},{"key":"2642_CR12","doi-asserted-by":"crossref","unstructured":"Teixeira B, Singh V, Chen T, Ma K, Tamersoy B, Wu Y, Balashova E, Comaniciu D (2018) Generating synthetic x-ray images of a person from the surface geometry. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 9059\u20139067","DOI":"10.1109\/CVPR.2018.00944"},{"key":"2642_CR13","doi-asserted-by":"crossref","unstructured":"Moreira AH, Queir\u00f3s S, Morais P, Rodrigues NF, Correia AR, Fernandes V, Pinho AC, Fonseca JC, Vila\u00e7a JL (2015) Voxel-based registration of simulated and real patient cbct data for accurate dental implant pose estimation. In: Medical Imaging 2015: Computer-aided diagnosis, vol 9414. International Society for Optics and Photonics, p 94143","DOI":"10.1117\/12.2082806"},{"key":"2642_CR14","doi-asserted-by":"crossref","unstructured":"Morais P, Queir\u00f3s S, Moreira AH, Ferreira A, Ferreira E, Duque D, Rodrigues NF, Vila\u00e7a JL (2015) Computer-aided recognition of dental implants in x-ray images. In: Medical imaging 2015: Computer-aided diagnosis, vol. 9414. International Society for Optics and Photonics, p 94142","DOI":"10.1117\/12.2082796"},{"issue":"5","key":"2642_CR15","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/s11548-012-0802-6","volume":"8","author":"P Cunha","year":"2013","unstructured":"Cunha P, Guevara MA, Messias A, Rocha S, Reis R, Nicolau PM (2013) A method for segmentation of dental implants and crestal bone. International Journal of Computer Assisted Radiology and Surgery 8(5):711\u2013721","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"issue":"4","key":"2642_CR16","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1007\/s11548-013-0946-z","volume":"9","author":"R Pauwels","year":"2014","unstructured":"Pauwels R, Jacobs R, Bosmans H, Pittayapat P, Kosalagood P, Silkosessak O, Panmekiate S (2014) Automated implant segmentation in cone-beam ct using edge detection and particle counting. International Journal of Computer Assisted Radiology and Surgery 9(4):733\u2013743","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"issue":"4","key":"2642_CR17","doi-asserted-by":"publisher","first-page":"405","DOI":"10.4103\/jips.jips_324_21","volume":"21","author":"VB Benakatti","year":"2021","unstructured":"Benakatti VB, Nayakar RP, Anandhalli M et al (2021) Machine learning for identification of dental implant systems based on shape-a descriptive study. The Journal of Indian Prosthodontic Society 21(4):405","journal-title":"The Journal of Indian Prosthodontic Society"},{"issue":"11","key":"2642_CR18","doi-asserted-by":"publisher","first-page":"910","DOI":"10.3390\/diagnostics10110910","volume":"10","author":"J-H Lee","year":"2020","unstructured":"Lee J-H, Kim Y-T, Lee J-B, Jeong S-N (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(11):910","journal-title":"Diagnostics"},{"key":"2642_CR19","doi-asserted-by":"crossref","unstructured":"Hadj Sa\u00efd M, Le\u00a0Roux M-K, Catherine J-H, Lan R (2020) Development of an artificial intelligence model to identify a dental implant from a radiograph. Int J Oral Maxillofac Implants 35(6)","DOI":"10.11607\/jomi.8060"},{"issue":"7","key":"2642_CR20","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(7):984","journal-title":"Biomolecules"},{"key":"2642_CR21","doi-asserted-by":"crossref","unstructured":"Kim H-S, Ha E-G, Kim YH, Jeon KJ, Lee C, Han S-S (2022) Transfer learning in a deep convolutional neural network for implant fixture classification: A pilot study. Imaging Sci Dent 52","DOI":"10.5624\/isd.20210287"},{"key":"2642_CR22","unstructured":"Prince JL, Links JM (2006) Medical imaging signals and systems. Upper Saddle River: Pearson Prentice Hall 36"},{"key":"2642_CR23","unstructured":"Gonzalez RC, Woods RE (2009) Digital image processing"},{"key":"2642_CR24","doi-asserted-by":"crossref","unstructured":"Carmignato S, Dewulf W, Leach R (2018) Industrial X-ray computed tomography","DOI":"10.1007\/978-3-319-59573-3"},{"issue":"1","key":"2642_CR25","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.ejpb.2006.02.007","volume":"64","author":"V Busignies","year":"2006","unstructured":"Busignies V, Leclerc B, Porion P, Evesque P, Couarraze G, Tchoreloff P (2006) Quantitative measurements of localized density variations in cylindrical tablets using X-ray microtomography. European Journal of Pharmaceutics and Biopharmaceutics 64(1):38\u201350","journal-title":"European Journal of Pharmaceutics and Biopharmaceutics"},{"issue":"4","key":"2642_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/S1110865704309042","volume":"2004","author":"J Coetzer","year":"2004","unstructured":"Coetzer J, Herbst BM, du Preez JA (2004) Offline signature verification using the discrete radon transform and a hidden Markov model. EURASIP Journal on Advances in Signal Processing 2004(4):1\u201313","journal-title":"EURASIP Journal on Advances in Signal Processing"},{"issue":"4","key":"2642_CR27","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1109\/34.3918","volume":"10","author":"H Samet","year":"1988","unstructured":"Samet H, Tamminen M (1988) Efficient component labeling of images of arbitrary dimension represented by linear bintrees. IEEE Transactions on Pattern Analysis and Machine Intelligence 10(4):579\u2013586","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"9","key":"2642_CR28","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1109\/TCOM.1976.1093409","volume":"24","author":"AK Jain","year":"1976","unstructured":"Jain AK (1976) A fast karhunen-loeve transform for a class of random processes. IEEE Transactions on Communications 24(9):1023\u20131029","journal-title":"IEEE Transactions on Communications"},{"key":"2642_CR29","doi-asserted-by":"crossref","unstructured":"Deng G, Cahill L (1993) An adaptive gaussian filter for noise reduction and edge detection. In: 1993 IEEE conference record nuclear science symposium and medical imaging conference. IEEE, pp 1615\u20131619","DOI":"10.1109\/NSSMIC.1993.373563"},{"key":"2642_CR30","unstructured":"Maini R, Aggarwal H (2010) A comprehensive review of image enhancement techniques. arXiv:1003.4053"},{"key":"2642_CR31","doi-asserted-by":"crossref","unstructured":"Karl M, Irastorza-Landa A (2018) In vitro characterization of original and nonoriginal implant abutments. Int J Oral Maxillofac Implants 33(6)","DOI":"10.11607\/jomi.6921"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02642-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-022-02642-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02642-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T12:29:09Z","timestamp":1744201749000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-022-02642-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,18]]},"references-count":31,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["2642"],"URL":"https:\/\/doi.org\/10.1007\/s11517-022-02642-9","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,18]]},"assertion":[{"value":"27 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 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 in studies involving human participants were in accordance with the ethical standards of the institutional and national research committees and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study. All applicable international, national, and institutional guidelines for the care and use of animals were followed.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}