{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T23:25:52Z","timestamp":1790810752206,"version":"4.1.0"},"reference-count":65,"publisher":"Elsevier BV","issue":"4","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Evidence-Based Dental Practice"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1016\/j.jebdp.2025.102164","type":"journal-article","created":{"date-parts":[[2025,5,2]],"date-time":"2025-05-02T19:24:59Z","timestamp":1746213899000},"page":"102164","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"title":["THE UTILITY OF DEEP LEARNING MODEL IN CLINICAL TREATMENT DECISION-MAKING OF MANDIBULAR THIRD MOLAR: A SYSTEMATIC REVIEW AND META-ANALYSIS"],"prefix":"10.1016","volume":"25","author":[{"given":"CHENJIE","family":"ZHAO","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"XUESHAN","family":"BAI","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ZIYANG","family":"LIU","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"WENJIE","family":"CHENG","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"SONGXIA","family":"XIA","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"LI","family":"TENG","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"LIYA","family":"YANG","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.jebdp.2025.102164_bib0001","doi-asserted-by":"crossref","DOI":"10.1259\/dmfr.20150224","article-title":"Neurosensoric disturbances after surgical removal of the mandibular third molar based on either panoramic imaging or cone beam CT scanning: a randomized controlled trial (RCT)","volume":"45","author":"Petersen","year":"2016","journal-title":"Dento Maxillo Facial Radiol"},{"issue":"2","key":"10.1016\/j.jebdp.2025.102164_bib0002","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.tripleo.2009.09.023","article-title":"The prognostic value of panoramic radiography of inferior alveolar nerve damage after mandibular third molar removal: retrospective study of 400 cases","volume":"109","author":"Szalma","year":"2010","journal-title":"Oral Surg, Oral Med, Oral Pathol, Oral Radiol Endodont"},{"issue":"3","key":"10.1016\/j.jebdp.2025.102164_bib0003","doi-asserted-by":"crossref","first-page":"532","DOI":"10.5114\/aoms.2016.58842","article-title":"Radiographic features of anatomic relationship between impacted third molar and inferior alveolar canal on coronal CBCT images: risk factors for nerve injury after tooth extraction","volume":"14","author":"Wang","year":"2018","journal-title":"Arch Med Sci: AMS"},{"issue":"1","key":"10.1016\/j.jebdp.2025.102164_bib0004","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.joms.2021.08.162","article-title":"A prospective observational study on the variables affecting the risk of inferior alveolar nerve damage during lower third molar surgery with nerve\/root proximity","volume":"80","author":"Pippi","year":"2022","journal-title":"J Oral Maxillofac Surg"},{"issue":"1","key":"10.1016\/j.jebdp.2025.102164_bib0005","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ijom.2010.09.005","article-title":"Risk factors of neurosensory deficits in lower third molar surgery: an literature review of prospective studies","volume":"40","author":"Leung","year":"2011","journal-title":"Int J Oral Maxillofac Surg"},{"issue":"12","key":"10.1016\/j.jebdp.2025.102164_bib0006","doi-asserted-by":"crossref","first-page":"2669","DOI":"10.1016\/j.joms.2018.06.001","article-title":"Does low-level laser therapy affect recovery of lingual and inferior alveolar nerve injuries?","volume":"76","author":"Miloro","year":"2018","journal-title":"J Oral Maxillofac Surg"},{"key":"10.1016\/j.jebdp.2025.102164_bib0007","doi-asserted-by":"crossref","unstructured":"Rozylo-Kalinowska, I. Imaging Techniques in Dental Radiology. 2020;43\u201356. https:\/\/doi.org\/10.1007\/978-3-030-41372-9.","DOI":"10.1007\/978-3-030-41372-9_4"},{"key":"10.1016\/j.jebdp.2025.102164_bib0008","doi-asserted-by":"crossref","unstructured":"Huang, Y.-C.; Chen, C.-A.; Chen, T.-Y., et al. Tooth position determination by automatic cutting and marking of dental panoramic X-ray film in medical image processing. 2021, 11 (24), 11904.","DOI":"10.3390\/app112411904"},{"key":"10.1016\/j.jebdp.2025.102164_bib0009","doi-asserted-by":"crossref","unstructured":"Chen, S.-L.; Chen, T.-Y.; Mao, Y.-C., et al. Automated detection system based on convolution neural networks for retained root, endodontic treated teeth, and implant recognition on dental panoramic images. 2022, 22 (23), 23293-23306. https:\/\/ieeexplore.ieee.org\/document\/9915316.","DOI":"10.1109\/JSEN.2022.3211981"},{"key":"10.1016\/j.jebdp.2025.102164_bib0010","series-title":"In tooth restoration and dental work detection on panoramic dental images via CNN, 2020 medical technologies congress (TIPTEKNO)","first-page":"1","author":"Gurses","year":"2020"},{"issue":"4","key":"10.1016\/j.jebdp.2025.102164_bib0011","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/s11282-019-00409-x","article-title":"Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography","volume":"36","author":"Fukuda","year":"2020","journal-title":"Oral Radiol"},{"issue":"4","key":"10.1016\/j.jebdp.2025.102164_bib0012","doi-asserted-by":"crossref","DOI":"10.1259\/dmfr.20180051","article-title":"Tooth detection and numbering in panoramic radiographs using convolutional neural networks","volume":"48","author":"Tuzoff","year":"2019","journal-title":"Dento Maxillo Facial Radiol"},{"issue":"21","key":"10.1016\/j.jebdp.2025.102164_bib0013","doi-asserted-by":"crossref","first-page":"7464","DOI":"10.3390\/app10217464","article-title":"Expert system for mandibular condyle detection and osteoarthritis classification in panoramic imaging using r-cnn and cnn","volume":"10","author":"Kim","year":"2020","journal-title":"Appl Sci"},{"issue":"1","key":"10.1016\/j.jebdp.2025.102164_bib0014","doi-asserted-by":"crossref","first-page":"5842","DOI":"10.1038\/s41598-020-62321-3","article-title":"Deep learning method for mandibular canal segmentation in dental cone beam computed tomography volumes","volume":"10","author":"Jaskari","year":"2020","journal-title":"Scient Rep"},{"issue":"7","key":"10.1016\/j.jebdp.2025.102164_bib0015","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1016\/j.joen.2019.03.016","article-title":"Deep learning for the radiographic detection of apical lesions","volume":"45","author":"Ekert","year":"2019","journal-title":"J Endodont"},{"issue":"3","key":"10.1016\/j.jebdp.2025.102164_bib0016","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s11282-018-0363-7","article-title":"Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography","volume":"35","author":"Murata","year":"2019","journal-title":"Oral Radiol"},{"issue":"3","key":"10.1016\/j.jebdp.2025.102164_bib0017","doi-asserted-by":"crossref","first-page":"179","DOI":"10.5624\/isd.2019.49.3.179","article-title":"New evolution of cone-beam computed tomography in dentistry: combining digital technologies","volume":"49","author":"Jain","year":"2019","journal-title":"Imaging Sci Dentistry"},{"issue":"6","key":"10.1016\/j.jebdp.2025.102164_bib0018","doi-asserted-by":"crossref","first-page":"2806","DOI":"10.1109\/TPAMI.2020.3045007","article-title":"A review on deep learning techniques for video prediction","volume":"44","author":"Oprea","year":"2022","journal-title":"IEEE Transact Pattern Analys\\ Mach Intellig"},{"issue":"3","key":"10.1016\/j.jebdp.2025.102164_bib0019","article-title":"Radiomics and machine learning in Oral healthcare","volume":"14","author":"Leite","year":"2020","journal-title":"Proteomics"},{"issue":"2","key":"10.1016\/j.jebdp.2025.102164_bib0020","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1148\/radiol.211706","article-title":"Deep learning for detecting pneumothorax on chest radiographs after needle biopsy: clinical implementation","volume":"303","author":"Hong","year":"2022","journal-title":"Radiology"},{"issue":"1","key":"10.1016\/j.jebdp.2025.102164_bib0021","doi-asserted-by":"crossref","first-page":"9425","DOI":"10.1038\/s41598-017-09891-x","article-title":"Accuracy of deep learning, a machine-learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment","volume":"7","author":"Ohsugi","year":"2017","journal-title":"Scient Rep"},{"issue":"8","key":"10.1016\/j.jebdp.2025.102164_bib0022","doi-asserted-by":"crossref","first-page":"846","DOI":"10.3390\/medicina57080846","article-title":"Effect of patient clinical variables in osteoporosis classification using hip X-rays in deep learning analysis","volume":"57","author":"Yamamoto","year":"2021","journal-title":"Medicina (Kaunas, Lithuania)"},{"issue":"1","key":"10.1016\/j.jebdp.2025.102164_bib0023","doi-asserted-by":"crossref","first-page":"8495","DOI":"10.1038\/s41598-019-44839-3","article-title":"Deep learning for the radiographic detection of periodontal bone loss","volume":"9","author":"Krois","year":"2019","journal-title":"Scient Rep"},{"issue":"4","key":"10.1016\/j.jebdp.2025.102164_bib0024","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1001\/jama.2017.19163","article-title":"Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA Statement","volume":"319","author":"McInnes","year":"2018","journal-title":"JAMA"},{"issue":"8","key":"10.1016\/j.jebdp.2025.102164_bib0025","doi-asserted-by":"crossref","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","article-title":"QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies","volume":"155","author":"Whiting","year":"2011","journal-title":"Ann Intern Med"},{"issue":"10","key":"10.1016\/j.jebdp.2025.102164_bib0026","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1016\/j.jclinepi.2005.02.022","article-title":"Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews","volume":"58","author":"Reitsma","year":"2005","journal-title":"J Clin Epidemiol"},{"issue":"9","key":"10.1016\/j.jebdp.2025.102164_bib0027","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1016\/j.jclinepi.2005.01.016","article-title":"The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed","volume":"58","author":"Deeks","year":"2005","journal-title":"J Clin Epidemiol"},{"key":"10.1016\/j.jebdp.2025.102164_bib0028","doi-asserted-by":"crossref","unstructured":"Fagan TJ. Letter: Nomogram for Bayes's theorem. N Engl J Med. 1975 Jul 31;293(5):257. doi:10.1056\/NEJM197507312930513.","DOI":"10.1056\/NEJM197507312930513"},{"issue":"3","key":"10.1016\/j.jebdp.2025.102164_bib0029","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.oooo.2020.04.005","article-title":"Comparison of 3 deep learning neural networks for classifying the relationship between the mandibular third molar and the mandibular canal on panoramic radiographs","volume":"130","author":"Fukuda","year":"2020","journal-title":"Oral Surg, Oral Med, Oral Pathol Oral Radiol"},{"issue":"9","key":"10.1016\/j.jebdp.2025.102164_bib0030","first-page":"1664","article-title":"Artificial intelligence model to detect real contact relationship between mandibular third molars and inferior alveolar nerve based on panoramic radiographs","volume":"11","author":"Zhu","year":"2021","journal-title":"Diagnostics (Basel, Switzerland)"},{"issue":"8","key":"10.1016\/j.jebdp.2025.102164_bib0031","first-page":"2018","article-title":"A fused deep learning architecture for the detection of the relationship between the mandibular third Molar and the mandibular Canal","volume":"12","author":"Buyuk","year":"2022","journal-title":"Diagnostics (Basel, Switzerland)"},{"issue":"1","key":"10.1016\/j.jebdp.2025.102164_bib0032","doi-asserted-by":"crossref","first-page":"2456","DOI":"10.1038\/s41598-022-06483-2","article-title":"Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography","volume":"12","author":"Choi","year":"2022","journal-title":"Scient Rep"},{"issue":"1","key":"10.1016\/j.jebdp.2025.102164_bib0033","doi-asserted-by":"crossref","first-page":"475","DOI":"10.3390\/app12010475","article-title":"Automated prediction of extraction difficulty and inferior alveolar nerve injury for mandibular third molar using a deep neural network","volume":"12","author":"Lee","year":"2022","journal-title":"Appl Sci"},{"issue":"1","key":"10.1016\/j.jebdp.2025.102164_bib0034","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1007\/s00784-021-04082-5","article-title":"Deep learning-based evaluation of the relationship between mandibular third molar and mandibular canal on CBCT","volume":"26","author":"Liu","year":"2022","journal-title":"Clin Oral Investig"},{"issue":"1","key":"10.1016\/j.jebdp.2025.102164_bib0035","article-title":"Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography","volume":"12","author":"Sukegawa","year":"2022","journal-title":"Scient Rep"},{"issue":"13","key":"10.1016\/j.jebdp.2025.102164_bib0036","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1177\/00220345231200786","article-title":"Preinterventional third-molar assessment using robust machine learning","volume":"102","author":"Carvalho","year":"2023","journal-title":"J Dent Res"},{"issue":"4","key":"10.1016\/j.jebdp.2025.102164_bib0037","doi-asserted-by":"crossref","first-page":"702","DOI":"10.3390\/electronics13040702","article-title":"Classification of the relative position between the third molar and the inferior alveolar nerve using a convolutional neural network based on transfer learning","volume":"13","author":"Chen","year":"2024","journal-title":"Electronics"},{"issue":"1","key":"10.1016\/j.jebdp.2025.102164_bib0038","article-title":"Automatic diagnosis of true proximity between the mandibular canal and the third molar on panoramic radiographs using deep learning","volume":"13","author":"Jeon","year":"2023","journal-title":"Scient Rep"},{"key":"10.1016\/j.jebdp.2025.102164_bib0039","doi-asserted-by":"crossref","first-page":"81777","DOI":"10.1109\/ACCESS.2023.3302271","article-title":"Classification of the relationship between mandibular third molar and inferior alveolar nerve based on generated mask images","volume":"11","author":"Joo","year":"2023","journal-title":"IEEE Access"},{"issue":"6","key":"10.1016\/j.jebdp.2025.102164_bib0040","doi-asserted-by":"crossref","DOI":"10.1259\/dmfr.20230030","article-title":"The efficacy of supervised learning and semi-supervised learning in diagnosis of impacted third molar on panoramic radiographs through artificial intelligence model","volume":"52","author":"Kim","year":"2023","journal-title":"Dento Maxillo Facial Radiol"},{"issue":"7","key":"10.1016\/j.jebdp.2025.102164_bib0041","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.3390\/life13071441","article-title":"Artificial intelligence for classifying the relationship between impacted third molar and mandibular canal on panoramic radiographs","volume":"13","author":"Lo Casto","year":"2023","journal-title":"Life-Basel"},{"issue":"7","key":"10.1016\/j.jebdp.2025.102164_bib0042","doi-asserted-by":"crossref","first-page":"3759","DOI":"10.1007\/s00784-023-04992-6","article-title":"Investigation of the best effective fold of data augmentation for training deep learning models for recognition of contiguity between mandibular third molar and inferior alveolar canal on panoramic radiographs","volume":"27","author":"Papasratorn","year":"2023","journal-title":"Clin Oral Investig"},{"issue":"3","key":"10.1016\/j.jebdp.2025.102164_bib0043","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1016\/j.jds.2022.12.008","article-title":"Deep learning model for the automated evaluation of contact between the lower third molar and inferior alveolar nerve on panoramic radiography","volume":"18","author":"Takebe","year":"2023","journal-title":"J Dent Sci"},{"issue":"12","key":"10.1016\/j.jebdp.2025.102164_bib0045","doi-asserted-by":"crossref","first-page":"e23052","DOI":"10.1016\/j.heliyon.2023.e23052","article-title":"Development and validation of machine learning based prediction model for postoperative pain risk after extraction of impacted mandibular third molars","volume":"9","author":"Yu","year":"2023","journal-title":"Heliyon"},{"issue":"4","key":"10.1016\/j.jebdp.2025.102164_bib0046","first-page":"209","article-title":"The combination of adaptive convolutional neural network and bag of visual words in automatic diagnosis of third molar complications on dental X-ray images","volume":"10","author":"Ngoc","year":"2020","journal-title":"Diagnostics (Basel, Switzerland)"},{"issue":"9","key":"10.1016\/j.jebdp.2025.102164_bib0047","first-page":"1572","article-title":"Deep learning-based prediction of paresthesia after third molar extraction: a preliminary study","volume":"11","author":"Kim","year":"2021","journal-title":"Diagnostics (Basel, Switzerland)"},{"issue":"17","key":"10.1016\/j.jebdp.2025.102164_bib0048","doi-asserted-by":"crossref","first-page":"9970","DOI":"10.3390\/app13179970","article-title":"A lightweight knowledge-distillation-based model for the detection and classification of impacted mandibular third molars","volume":"13","author":"Lei","year":"2023","journal-title":"Appl Sci-Basel"},{"issue":"2","key":"10.1016\/j.jebdp.2025.102164_bib0049","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s11282-022-00634-x","article-title":"Deep learning for preliminary profiling of panoramic images","volume":"39","author":"Kohinata","year":"2023","journal-title":"Oral Radiol"},{"issue":"1","key":"10.1016\/j.jebdp.2025.102164_bib0050","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1038\/s41598-021-04603-y","article-title":"Evaluation of multi-task learning in deep learning-based positioning classification of mandibular third molars","volume":"12","author":"Sukegawa","year":"2022","journal-title":"Scient Rep"},{"issue":"1","key":"10.1016\/j.jebdp.2025.102164_bib0051","article-title":"Classification of caries in third molars on panoramic radiographs using deep learning","volume":"11","author":"Vinayahalingam","year":"2021","journal-title":"Scient Rep"},{"issue":"9","key":"10.1016\/j.jebdp.2025.102164_bib0052","doi-asserted-by":"crossref","first-page":"S96","DOI":"10.1016\/j.joms.2023.08.049","article-title":"Automatic impacted tooth segmentation using artificial intelligence model on cone-beam computed tomography images","volume":"81","author":"Vujanovic","year":"2023","journal-title":"J Oral Maxillofac Surg"},{"issue":"1","key":"10.1016\/j.jebdp.2025.102164_bib0053","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1186\/s12903-023-03496-9","article-title":"Automatic classification of 3D positional relationship between mandibular third molar and inferior alveolar canal using a distance-aware network","volume":"23","author":"Chun","year":"2023","journal-title":"BMC Oral Health"},{"issue":"4","key":"10.1016\/j.jebdp.2025.102164_bib0054","article-title":"Deep learning based detection tool for impacted mandibular third molar teeth","volume":"12","author":"Celik","year":"2022","journal-title":"Diagnostics (Basel, Switzerland)"},{"issue":"4","key":"10.1016\/j.jebdp.2025.102164_bib0055","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.jormas.2020.12.006","article-title":"Evaluation of artificial intelligence for detecting impacted third molars on cone-beam computed tomography scans","volume":"122","author":"Orhan","year":"2021","journal-title":"J Stomatol, Oral Maxillofac Surg"},{"key":"10.1016\/j.jebdp.2025.102164_bib0056","doi-asserted-by":"crossref","DOI":"10.1016\/j.jdent.2023.104519","article-title":"Positional assessment of lower third molar and mandibular canal using explainable artificial intelligence","volume":"133","author":"Kempers","year":"2023","journal-title":"J Dentistry"},{"issue":"6","key":"10.1016\/j.jebdp.2025.102164_bib0057","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1016\/j.oooo.2022.05.014","article-title":"Automatic visualization of the mandibular canal in relation to an impacted mandibular third molar on panoramic radiographs using deep learning segmentation and transfer learning techniques","volume":"134","author":"Ariji","year":"2022","journal-title":"Oral Surg, Oral Med, Oral Pathol Oral Radiol"},{"issue":"1","key":"10.1016\/j.jebdp.2025.102164_bib0058","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1038\/s41598-024-51393-0","article-title":"A retrospective longitudinal assessment of artificial intelligence-assisted radiographic prediction of lower third molar eruption","volume":"14","author":"Chopra","year":"2024","journal-title":"Scient Rep"},{"issue":"1","key":"10.1016\/j.jebdp.2025.102164_bib0059","doi-asserted-by":"crossref","first-page":"1954","DOI":"10.1038\/s41598-021-81449-4","article-title":"Deep learning based prediction of extraction difficulty for mandibular third molars","volume":"11","author":"Yoo","year":"2021","journal-title":"Scient Rep"},{"issue":"8","key":"10.1016\/j.jebdp.2025.102164_bib0060","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1016\/j.ijom.2011.02.032","article-title":"The use of cone beam CT for the removal of wisdom teeth changes the surgical approach compared with panoramic radiography: a pilot study","volume":"40","author":"Ghaeminia","year":"2011","journal-title":"Int J Oral Maxillofac Surg"},{"issue":"2","key":"10.1016\/j.jebdp.2025.102164_bib0061","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1111\/adj.12326","article-title":"Diagnostic value of panoramic radiography in predicting inferior alveolar nerve injury after mandibular third molar extraction: a meta-analysis","volume":"60","author":"Liu","year":"2015","journal-title":"Austral Dent J"},{"issue":"1","key":"10.1016\/j.jebdp.2025.102164_bib0062","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.jormas.2019.06.010","article-title":"Determining the risk relationship associated with inferior alveolar nerve injury following removal of mandibular third molar teeth: a systematic review","volume":"121","author":"Kang","year":"2020","journal-title":"J Stomatol Oral Maxillofac Surg"},{"key":"10.1016\/j.jebdp.2025.102164_bib0063","article-title":"Uses of different machine learning algorithms for diagnosis of dental caries","volume":"2022","author":"Talpur","year":"2022","journal-title":"J Healthc Engineer"},{"issue":"3","key":"10.1016\/j.jebdp.2025.102164_bib0064","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.sdentj.2023.11.025","article-title":"The diagnostic performance of impacted third molars in the mandible: a review of deep learning on panoramic radiographs","volume":"36","author":"Faadiya","year":"2024","journal-title":"Saudi Dent J"},{"key":"10.1016\/j.jebdp.2025.102164_bib0065","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"},{"issue":"10","key":"10.1016\/j.jebdp.2025.102164_bib0066","first-page":"893","article-title":"Deep learning for clinical image analyses in oral squamous cell carcinoma: a review","volume":"147","author":"Chu","year":"2021","journal-title":"JAMA Otolaryngol"}],"container-title":["Journal of Evidence-Based Dental Practice"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S153233822500079X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S153233822500079X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T08:07:25Z","timestamp":1763885245000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S153233822500079X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":65,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["S153233822500079X"],"URL":"https:\/\/doi.org\/10.1016\/j.jebdp.2025.102164","relation":{},"ISSN":["1532-3382"],"issn-type":[{"value":"1532-3382","type":"print"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"THE UTILITY OF DEEP LEARNING MODEL IN CLINICAL TREATMENT DECISION-MAKING OF MANDIBULAR THIRD MOLAR: A SYSTEMATIC REVIEW AND META-ANALYSIS","name":"articletitle","label":"Article Title"},{"value":"Journal of Evidence-Based Dental Practice","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jebdp.2025.102164","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"102164"}}