{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T11:13:01Z","timestamp":1794049981912,"version":"4.8.0"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T00:00:00Z","timestamp":1686787200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Dentistry"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1016\/j.jdent.2023.104581","type":"journal-article","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T19:35:48Z","timestamp":1686166548000},"page":"104581","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":71,"special_numbering":"C","title":["A deep learning approach for radiological detection and classification of radicular cysts and periapical granulomas"],"prefix":"10.1016","volume":"135","author":[{"given":"Jonas","family":"Ver Berne","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soroush Baseri","family":"Saadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Constantinus","family":"Politis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reinhilde","family":"Jacobs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jdent.2023.104581_bib0001","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1111\/j.1600-0714.2006.00451.x","article-title":"An analysis of oral and maxillofacial pathology found in adults over a 30-year period","volume":"7","author":"Jones","year":"2006","journal-title":"J. Oral Pathol. Med."},{"key":"10.1016\/j.jdent.2023.104581_bib0002","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1111\/j.1600-9657.1989.tb00339.x","article-title":"Differentiation of radicular cyst and granulomas using computerized tomography","volume":"5","author":"Trope","year":"1989","journal-title":"Endod. Dent. Traumatol."},{"key":"10.1016\/j.jdent.2023.104581_bib0003","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/0030-4220(93)90268-9","article-title":"Differentiation of periapical granulomas and radicular cysts by digital radiometric analysis","volume":"76","author":"Shrout","year":"1993","journal-title":"Oral Surg. Oral Med. Oral Pathol."},{"key":"10.1016\/j.jdent.2023.104581_bib0004","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/0030-4220(94)90180-5","article-title":"Absence of radiometric differentiation between periapical cysts and granulomas","volume":"78","author":"White","year":"1994","journal-title":"Oral Surg. Oral Med. Oral Pathol."},{"key":"10.1016\/j.jdent.2023.104581_bib0005","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.joen.2006.03.008","article-title":"Differential diagnosis of large periapical lesions using cone-beam computed tomography measurements and biopsy","volume":"32","author":"Simon","year":"2006","journal-title":"J. Endod."},{"key":"10.1016\/j.jdent.2023.104581_bib0006","series-title":"Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro","first-page":"566","article-title":"Non-invasive differential diagnosis of dental periapical lesions in cone-beam CT","author":"Flores","year":"2009"},{"key":"10.1016\/j.jdent.2023.104581_bib0007","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1016\/j.joen.2013.08.019","article-title":"Evaluation of the reliability and accuracy of using cone-beam computed tomography for diagnosing periapical cysts from granulomas","volume":"39","author":"Guo","year":"2013","journal-title":"J. Endod."},{"key":"10.1016\/j.jdent.2023.104581_bib0008","doi-asserted-by":"crossref","first-page":"326","DOI":"10.4103\/JCD.JCD_124_17","article-title":"Reliability of cone beam computed tomography as a biopsy-independent tool in differential diagnosis of periapical cysts and granulomas: an in vivo study","volume":"20","author":"Chanani","year":"2017","journal-title":"J. Conservs. Dent."},{"key":"10.1016\/j.jdent.2023.104581_bib0009","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.joen.2016.10.046","article-title":"Binary decision trees for preoperative periapical cyst screening using cone-beam computed tomography","volume":"43","author":"Pitcher","year":"2017","journal-title":"J. Endod."},{"key":"10.1016\/j.jdent.2023.104581_bib0010","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1111\/iej.13355","article-title":"Accuracy of mean grey density values obtained with small field of view cone beam computed tomography in differentiation between periapical cystic and solid lesions","volume":"53","author":"AlMadi","year":"2020","journal-title":"Int. Endod. J."},{"key":"10.1016\/j.jdent.2023.104581_bib0011","doi-asserted-by":"crossref","first-page":"E05194","DOI":"10.1016\/j.heliyon.2020.e05194","article-title":"Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis","volume":"6","author":"de Rosa","year":"2020","journal-title":"Heliyon"},{"key":"10.1016\/j.jdent.2023.104581_bib0012","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.jdent.2023.104581_bib0013","first-page":"25","article-title":"ImageNet classification with deep convolutional neural networks","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10.1016\/j.jdent.2023.104581_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.jdent.2021.103786","article-title":"Layered deep learning for automatic mandibular segmentation in cone-beam computed tomography","volume":"114","author":"Verhelst","year":"2021","journal-title":"J. Dent."},{"key":"10.1016\/j.jdent.2023.104581_bib0015","doi-asserted-by":"crossref","DOI":"10.1259\/dmfr.20190364","article-title":"Validation of a 3D cBcT-based protocol for the follow-up of mandibular condyle remodeling","volume":"49","author":"Verhelst","year":"2020","journal-title":"Dentomaxillofacial Radiol."},{"key":"10.1016\/j.jdent.2023.104581_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.jdent.2022.104345","article-title":"Temporomandibular joint segmentation in MRI images using deep learning","volume":"127","author":"Li","year":"2022","journal-title":"J. Dent."},{"key":"10.1016\/j.jdent.2023.104581_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.jdent.2022.104238","article-title":"Deep convolutional neural network-based automated segmentation of the maxillofacial complex from cone-beam computed tomography: a validation study","volume":"124","author":"Preda","year":"2022","journal-title":"J. Dent."},{"key":"10.1016\/j.jdent.2023.104581_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.jdent.2022.104115","article-title":"Deep learning for caries detection: a systematic review","volume":"122","author":"Mohammad-Rahimi","year":"2022","journal-title":"J. Dent."},{"key":"10.1016\/j.jdent.2023.104581_bib0019","doi-asserted-by":"crossref","DOI":"10.1259\/dmfr.20220081","article-title":"Artificial intelligence system for automated landmark localization and analysis of cephalometry","volume":"52","author":"Jiang","year":"2023","journal-title":"Dentomaxillofacial Radiol."},{"key":"10.1016\/j.jdent.2023.104581_bib0020","doi-asserted-by":"crossref","first-page":"1380","DOI":"10.1177\/00220345221112333","article-title":"Automatic 3-dimensional cephalometric landmarking via deep learning","volume":"101","author":"Dot","year":"2022","journal-title":"J. Dent. Res."},{"key":"10.1016\/j.jdent.2023.104581_bib0021","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s11548-021-02309-0","article-title":"Differential diagnosis of ameloblastoma and odontogenic keratocyst by machine learning of panoramic radiographs","volume":"16","author":"Liu","year":"2021","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"10.1016\/j.jdent.2023.104581_bib0022","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2021.793417","article-title":"Improved diagnostic accuracy of ameloblastoma and odontogenic keratocyst on cone-beam CT by artificial intelligence","volume":"11","author":"Chai","year":"2022","journal-title":"Front. Oncol."},{"key":"10.1016\/j.jdent.2023.104581_bib0023","doi-asserted-by":"crossref","DOI":"10.1259\/dmfr.20210002","article-title":"Computer tomographic differential diagnosis of ameloblastoma and odontogenic keratocyst: classification using a convolutional neural network","volume":"50","author":"Bispo","year":"2021","journal-title":"Dentomaxillofacial Radiol."},{"key":"10.1016\/j.jdent.2023.104581_bib0024","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1038\/nmeth.2019","article-title":"Fiji: an open-source platform for biological-image analysis","volume":"9","author":"Schindelin","year":"2012","journal-title":"Nat. Methods"},{"key":"10.1016\/j.jdent.2023.104581_bib0025","doi-asserted-by":"crossref","first-page":"125","DOI":"10.3390\/info11020125","article-title":"Albumentations: fast and flexible image augmentations","volume":"11","author":"Buslaev","year":"2020","journal-title":"Information"},{"key":"10.1016\/j.jdent.2023.104581_bib0026","doi-asserted-by":"crossref","first-page":"56","DOI":"10.3389\/fnano.2022.972421","article-title":"SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer","volume":"4","author":"Prusty","year":"2022","journal-title":"Front. Nanotechnol."},{"key":"10.1016\/j.jdent.2023.104581_bib0027","unstructured":"A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, H. Adam, MobileNets: efficient convolutional neural networks for mobile vision applications, (2017). arXiv:1704.04861v1 (accessed April 16, 2023)."},{"key":"10.1016\/j.jdent.2023.104581_bib0028","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.jdent.2023.104581_sbref0029","article-title":"YOLOv3: an incremental improvement","author":"Redmon","year":"2018","journal-title":"Comput. Sci."},{"key":"10.1016\/j.jdent.2023.104581_bib0030","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s11263-007-0090-8","article-title":"LabelMe: A Database and Web-Based Tool for Image Annotation","volume":"77","author":"Russell","year":"2007","journal-title":"Int J Comput Vis"},{"key":"10.1016\/j.jdent.2023.104581_bib0031","unstructured":"F. Chollet, & O., Keras: the python deep learning API, keras: the python deep learning API. (2020). https:\/\/keras.io\/(accessed April 28, 2022)."},{"key":"10.1016\/j.jdent.2023.104581_sbref0032","series-title":"Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation","first-page":"265","article-title":"TensorFlow: a system for large-scale machine learning","author":"Abadi","year":"2016"},{"key":"10.1016\/j.jdent.2023.104581_bib0033","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: visual explanations from deep networks via gradient-based localization","volume":"128","author":"Selvaraju","year":"2016","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.jdent.2023.104581_bib0034","doi-asserted-by":"crossref","unstructured":"H. Narasimhan, W. Pan, P. Kar, P. Protopapas, H.G. Ramaswamy, Optimizing the multiclass f-measure via biconcave programming, Undefined (2016) 1101\u20131106. doi:10.1109\/ICDM.2016.0143.","DOI":"10.1109\/ICDM.2016.0143"},{"key":"10.1016\/j.jdent.2023.104581_bib0035","doi-asserted-by":"crossref","first-page":"100","DOI":"10.4317\/jced.59096","article-title":"Decompression induces inflammation but do not modify cell proliferation and apoptosis in odontogenic keratocyst","volume":"14","author":"Trujillo-Gonz\u00e1lez","year":"2022","journal-title":"J. Clin. Exp. Dent."},{"key":"10.1016\/j.jdent.2023.104581_bib0036","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/0030-4220(72)90325-8","article-title":"Nonsurgical resolution of radicular cysts, oral surgery, oral medicine","volume":"34","author":"Bhaskar","year":"1972","journal-title":"Oral Pathol."},{"key":"10.1016\/j.jdent.2023.104581_bib0037","unstructured":"J. Kauffmann, L. Ruff, G. Montavon, K.-R. M\u00fcller, The clever hans effect in anomaly detection, (2020) arXiv:2006.10609."},{"key":"10.1016\/j.jdent.2023.104581_sbref0038","series-title":"Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Demonstrations Session","first-page":"97","article-title":"Why should I trust you?\u201d: explaining the predictions of any classifier","author":"Ribeiro","year":"2016"},{"key":"10.1016\/j.jdent.2023.104581_bib0039","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: visual explanations from deep networks via gradient-based localization","volume":"128","author":"Selvaraju","year":"2016","journal-title":"Int. J. Comput. Vis."}],"container-title":["Journal of Dentistry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0300571223001677?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0300571223001677?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:41:12Z","timestamp":1759538472000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0300571223001677"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":39,"alternative-id":["S0300571223001677"],"URL":"https:\/\/doi.org\/10.1016\/j.jdent.2023.104581","relation":{},"ISSN":["0300-5712"],"issn-type":[{"value":"0300-5712","type":"print"}],"subject":[],"published":{"date-parts":[[2023,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A deep learning approach for radiological detection and classification of radicular cysts and periapical granulomas","name":"articletitle","label":"Article Title"},{"value":"Journal of Dentistry","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jdent.2023.104581","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"104581"}}