{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T10:36:32Z","timestamp":1793356592700,"version":"4.5.0"},"reference-count":69,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019286","name":"Ajman University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100019286","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Dent. Med."],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aimed to evaluate the diagnostic reliability of a prototype artificial intelligence (AI)-assisted software for automated detection of clinical features from dental photographs, using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) as metrics. Targeted features included caries, gingival recession, calculus, retained roots, bleeding, and staining.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A cross-sectional study was conducted on 34 patients at Ajman University's College of Dentistry, generating 306 standardized intraoral photographs. Ground truth was established by clinical examiners and compared against the prototype software output. Diagnostic performance was analyzed using Stata version 15.0 (StataCorp).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The prototype demonstrated variable diagnostic performance. The prototype demonstrated higher sensitivity in detection of bleeding (71.43%; 95% CI: 68.74\u201374.11) and retained roots (72.73%; 95% CI: 66.48\u201378.98) with the lowest values reported for the detection of staining (24.38%; 95% CI: 21.63\u201327.13). The prototype demonstrated high specificity for detection of staining (98.77%; 95% CI: 98.11\u201399.42) and bleeding (98.88%; 95% CI: 98.15\u201399.45). The highest PPV was reported for the detection of staining (94.78%; 95% CI: 93.45\u201396.10), whereas bleeding (99.81%; 95% CI: 99.56\u2013100.00) and retained roots (99.72%; 95% CI: 99.48\u2013100.00) reported NPVs above 99%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The AI software demonstrates strong specificity and NPV, indicating reliable disease exclusion. Lower sensitivity and PPV suggest the need for algorithmic refinement to enhance detection performance and reduce false positives.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdmed.2026.1814876","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T05:52:05Z","timestamp":1778219525000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Evaluation of the diagnostic reliability of an AI prototype in detecting clinical features from dental photographs\u2014original research"],"prefix":"10.3389","volume":"7","author":[{"given":"Saif","family":"Abdelaziz","sequence":"first","affiliation":[{"name":"College of Dentistry, Ajman University","place":["Ajman, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Islam","family":"Yasser","sequence":"additional","affiliation":[{"name":"College of Dentistry, Ajman University","place":["Ajman, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mazen","family":"Amgad","sequence":"additional","affiliation":[{"name":"College of Dentistry, Ajman University","place":["Ajman, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mutaz","family":"Al Qudah","sequence":"additional","affiliation":[{"name":"College of Dentistry, Ajman University","place":["Ajman, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohit","family":"Kunnath Menon","sequence":"additional","affiliation":[{"name":"College of Dentistry, Ajman University","place":["Ajman, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e27405","DOI":"10.7759\/cureus.27405","article-title":"Artificial intelligence in dentistry: past, present, and future","volume":"14","author":"Agrawal","year":"2022","journal-title":"Cureus"},{"key":"B2","doi-asserted-by":"publisher","first-page":"e9751564","DOI":"10.1155\/2021\/9751564","article-title":"Artificial intelligence techniques: analysis, application, and outcome in dentistry\u2014a systematic review","volume":"2021","author":"Ahmed","year":"2021","journal-title":"Biomed Res Int"},{"key":"B3","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.3389\/froh.2025.1473877","article-title":"Artificial intelligence-produced radiographic enhancements in dental clinical care: provider and patient perspectives","volume":"6","author":"Slashcheva","year":"2025","journal-title":"Front Oral Health"},{"key":"B4","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/s00056-019-00203-8","article-title":"Artificial intelligence in orthodontics","volume":"81","author":"Kunz","year":"2019","journal-title":"J Orofac Orthop"},{"key":"B5","doi-asserted-by":"publisher","first-page":"815","DOI":"10.3390\/biom11060815","article-title":"Multi-task deep learning model for classification of dental implant brand and treatment stage using dental panoramic radiograph images","volume":"11","author":"Sukegawa","year":"2021","journal-title":"Biomolecules"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1800","DOI":"10.3390\/diagnostics13101800","article-title":"Assessing the effectiveness of artificial intelligence models for detecting alveolar bone loss in periodontal disease: a panoramic radiograph study","volume":"13","author":"Uzun Saylan","year":"2023","journal-title":"Diagnostics"},{"key":"B7","doi-asserted-by":"publisher","first-page":"17615","DOI":"10.1038\/s41598-019-53758-2","article-title":"DeNTNet: deep neural transfer network for the detection of periodontal bone loss using panoramic dental radiographs","volume":"9","author":"Kim","year":"2019","journal-title":"Sci Rep"},{"key":"B8","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1186\/s40729-020-00250-6","article-title":"Identification of dental implants using deep learning\u2014pilot study","volume":"6","author":"Takahashi","year":"2020","journal-title":"Int J Implant Dent"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.11607\/jomi.8060","article-title":"Development of an artificial intelligence model to identify a dental implant from a radiograph","volume":"35","author":"Hadj Sa\u00efd","year":"2020","journal-title":"Int J Oral Maxillofac Implants"},{"key":"B10","doi-asserted-by":"publisher","first-page":"984","DOI":"10.3390\/biom10070984","article-title":"Deep neural networks for dental implant system classification","volume":"10","author":"Sukegawa","year":"2020","journal-title":"Biomolecules"},{"key":"B11","doi-asserted-by":"publisher","first-page":"70","DOI":"10.15644\/asc57\/1\/8","article-title":"Artificial intelligence in medicine and dentistry","volume":"57","author":"Vodanovi\u0107","year":"2023","journal-title":"Acta Stomatol Croat"},{"key":"B12","doi-asserted-by":"publisher","first-page":"224","DOI":"10.3390\/diagnostics12010224","article-title":"Artificial intelligence application in assessment of panoramic radiographs","volume":"12","author":"Zadro\u017cny","year":"2022","journal-title":"Diagnostics"},{"key":"B13","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1186\/s12903-023-03027-6","article-title":"Artificial intelligence in the diagnosis of dental diseases on panoramic radiographs: a preliminary study","volume":"23","author":"Zhu","year":"2023","journal-title":"BMC Oral Health"},{"key":"B14","doi-asserted-by":"publisher","first-page":"78","DOI":"10.5577\/intdentres.2019.vol9.no2.7","article-title":"The use of micro-computed tomography in dental applications","volume":"9","author":"Erpa\u00e7al","year":"2019","journal-title":"Int Dent Res"},{"key":"B15","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1186\/s12880-021-00656-7","article-title":"An artif\u0131cial intelligence approach to automatic tooth detection and numbering in panoramic radiographs","volume":"21","author":"Bilgir","year":"2021","journal-title":"BMC Med Imaging"},{"key":"B16","doi-asserted-by":"publisher","first-page":"100669","DOI":"10.1016\/j.eclinm.2020.100669","article-title":"Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: a systematic review and meta-analysis","volume":"31","author":"Zheng","year":"2021","journal-title":"EClinicalMedicine"},{"key":"B17","doi-asserted-by":"publisher","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":"B18","doi-asserted-by":"publisher","first-page":"4437","DOI":"10.1038\/s41598-024-55109-2","article-title":"Performance evaluation of a deep learning model for automatic detection and localization of idiopathic osteosclerosis on dental panoramic radiographs","volume":"14","author":"Tassoker","year":"2024","journal-title":"Sci Rep"},{"key":"B19","doi-asserted-by":"publisher","first-page":"103705","DOI":"10.1016\/j.jdent.2021.103705","article-title":"Automatic segmentation of the pharyngeal airway space with convolutional neural network","volume":"111","author":"Shujaat","year":"2021","journal-title":"J Dent"},{"key":"B20","doi-asserted-by":"publisher","first-page":"103865","DOI":"10.1016\/j.jdent.2021.103865","article-title":"A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. a validation study","volume":"115","author":"Shaheen","year":"2021","journal-title":"J Dent"},{"key":"B21","doi-asserted-by":"publisher","first-page":"104069","DOI":"10.1016\/j.jdent.2022.104069","article-title":"Influence of dental fillings and tooth type on the performance of a novel artificial intelligence-driven tool for automatic tooth segmentation on CBCT images \u2013 a validation study","volume":"119","author":"Fontenele","year":"2022","journal-title":"J Dent"},{"key":"B22","doi-asserted-by":"publisher","first-page":"103786","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":"B23","doi-asserted-by":"publisher","first-page":"103891","DOI":"10.1016\/j.jdent.2021.103891","article-title":"Development and validation of a novel artificial intelligence driven tool for accurate mandibular canal segmentation on CBCT","volume":"116","author":"Lahoud","year":"2022","journal-title":"J Dent"},{"key":"B24","doi-asserted-by":"publisher","first-page":"103849","DOI":"10.1016\/j.jdent.2021.103849","article-title":"Artificial intelligence for caries detection: randomized trial","volume":"115","author":"Mertens","year":"2021","journal-title":"J Dent"},{"key":"B25","doi-asserted-by":"publisher","first-page":"104115","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":"B26","first-page":"1617","article-title":"Recognizing occlusal caries in dental intraoral images using deep learning","author":"Moutselos","year":"2019"},{"key":"B27","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1186\/s12903-024-04847-w","article-title":"Diagnostic accuracy of artificial intelligence-assisted caries detection: a clinical evaluation","volume":"24","author":"Zhang","year":"2024","journal-title":"BMC Oral Health"},{"key":"B28","doi-asserted-by":"publisher","first-page":"110960","DOI":"10.1016\/j.dib.2024.110960","article-title":"A dental intraoral image dataset of gingivitis for image captioning","volume":"57","author":"Duy","year":"2024","journal-title":"Data Brief"},{"key":"B29","doi-asserted-by":"publisher","first-page":"105906","DOI":"10.1016\/j.jdent.2025.105906","article-title":"Dental caries detection in children using intraoral scans and deep learning","volume":"160","author":"Jones","year":"2025","journal-title":"J Dent"},{"key":"B30","doi-asserted-by":"publisher","first-page":"1115.e1","DOI":"10.1016\/j.prosdent.2025.06.020","article-title":"Development and evaluation of an artificial intelligence (AI) model for detecting dental caries from 3D intraoral scans","volume":"134","author":"Ahmed","year":"2025","journal-title":"J Prosthet Dent"},{"key":"B31","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.jebdp.2016.08.003","article-title":"Diagnostic accuracy of teledentistry in the detection of dental caries: a systematic review","volume":"16","author":"Estai","year":"2016","journal-title":"J Evid Based Dent Pract"},{"key":"B32","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.3390\/diagnostics12051083","article-title":"Application and performance of artificial intelligence technology in detection, diagnosis and prediction of dental caries (DC)\u2014a systematic review","volume":"12","author":"Khanagar","year":"2022","journal-title":"Diagnostics"},{"key":"B33","doi-asserted-by":"publisher","first-page":"1765","DOI":"10.1111\/odi.14659","article-title":"Detecting dental caries on oral photographs using artificial intelligence: a systematic review","volume":"30","author":"Moharrami","year":"2023","journal-title":"Oral Dis"},{"key":"B34","doi-asserted-by":"publisher","first-page":"100883","DOI":"10.1016\/j.identj.2025.100883","article-title":"Artificial intelligence in detecting periodontal disease from intraoral photographs: a systematic review","volume":"75","author":"Mao","year":"2025","journal-title":"Int Dent J"},{"key":"B35","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/1472-6831-13-6","article-title":"Comparison of caries detection methods using varying numbers of intra-oral digital photographs with visual examination for epidemiology in children","volume":"13","author":"Boye","year":"2013","journal-title":"BMC Oral Health"},{"key":"B36","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1111\/odi.13735","article-title":"Development and evaluation of deep learning for screening dental caries from oral photographs","volume":"28","author":"Zhang","year":"2020","journal-title":"Oral Dis"},{"key":"B37","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1016\/j.ajodo.2025.01.017","article-title":"Diagnostic accuracy of artificial intelligence for dental and occlusal parameters using standardized clinical photographs","volume":"167","author":"Vaughan","year":"2025","journal-title":"Am J Orthod Dentofacial Orthop"},{"key":"B38","doi-asserted-by":"publisher","first-page":"146045822110075","DOI":"10.1177\/14604582211007530","article-title":"Automated caries detection with smartphone color photography using machine learning","volume":"27","author":"Duong","year":"2021","journal-title":"Health Informatics J"},{"key":"B39","doi-asserted-by":"publisher","first-page":"104821","DOI":"10.1016\/j.jdent.2023.104821","article-title":"AI-based dental caries and tooth number detection in intraoral photos: model development and performance evaluation","volume":"141","author":"Yoon","year":"2024","journal-title":"J Dent"},{"key":"B40","doi-asserted-by":"publisher","first-page":"1329","DOI":"10.6026\/973206300191329","article-title":"Artificial intelligence in oral health surveillance among under-served communities","volume":"19","author":"Tiwari","year":"2023","journal-title":"Bioinformation"},{"key":"B41","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1007\/s00414-021-02542-x","article-title":"Accurate age classification using manual method and deep convolutional neural network based on orthopantomogram images","volume":"135","author":"Guo","year":"2021","journal-title":"Int J Leg Med"},{"key":"B42","author":"Rana","year":""},{"key":"B43","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1089\/tmj.2016.0122","article-title":"Comparison of a smartphone-based photographic method with face-to-face caries assessment: a mobile teledentistry model","volume":"23","author":"Estai","year":"2017","journal-title":"Telemed J E Health"},{"key":"B44","doi-asserted-by":"publisher","first-page":"104301","DOI":"10.1016\/j.jdent.2022.104301","article-title":"Evaluation of an artificial intelligence web-based software to detect and classify dental structures and treatments in panoramic radiographs","volume":"126","author":"Bonfanti-Gris","year":"2022","journal-title":"J Dent"},{"key":"B45","doi-asserted-by":"publisher","first-page":"45","DOI":"10.4103\/0301-4738.37595","article-title":"Understanding and using sensitivity, specificity and predictive values","volume":"56","author":"Parikh","year":"2008","journal-title":"Indian J Ophthalmol"},{"key":"B46","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1038\/s41598-020-80182-8","article-title":"Age-group determination of living individuals using first molar images based on artificial intelligence","volume":"11","author":"Kim","year":"2021","journal-title":"Sci Rep"},{"key":"B47","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1186\/s12903-024-04254-1","article-title":"Simultaneous detection of dental caries and fissure sealant in intraoral photos by deep learning: a pilot study","volume":"24","author":"Xiong","year":"2024","journal-title":"BMC Oral Health"},{"key":"B48","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1177\/00220345211032524","article-title":"Caries detection on intraoral images using artificial intelligence","volume":"101","author":"K\u00fchnisch","year":"2021","journal-title":"J Dent Res"},{"key":"B49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jobcr.2025.10.027","article-title":"Diagnostic efficiency of digital photography and AI-assisted image interpretation in dental caries examination: an umbrella review","volume":"16","author":"Madan Kumar","year":"2025","journal-title":"J Oral Biol Craniofac Res"},{"key":"B50","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.3389\/froh.2025.1649715","article-title":"Innovations on the horizon: teledentistry, artificial intelligence, and hybrid models to improve oral health for vulnerable communities","volume":"6","author":"Drafta","year":"2025","journal-title":"Front Oral Health"},{"key":"B51","doi-asserted-by":"publisher","first-page":"106506","DOI":"10.1016\/j.jdent.2026.106506","article-title":"Performance of smartphone cameras for measuring and analyzing dental shade: a systematic review","volume":"166","author":"Alhumoud","year":"2026","journal-title":"J Dent"},{"key":"B52","doi-asserted-by":"publisher","first-page":"91","DOI":"10.25259\/jgoh_30_2025","article-title":"Deep learning in dental diagnostics: caries detection through smartphone photographs \u2013 a systematic review","volume":"8","author":"Krothapalli","year":"2025","journal-title":"J Glob Oral Health"},{"key":"B53","doi-asserted-by":"publisher","first-page":"e41","DOI":"10.1111\/cdoe.12025","article-title":"International caries detection and assessment system (ICDAS) and its international caries classification and management system (ICCMS) - methods for staging of the caries process and enabling dentists to manage caries","volume":"41","author":"Pitts","year":"2013","journal-title":"Community Dent Oral Epidemiol"},{"key":"B54","first-page":"8","article-title":"A classification of marginal tissue recession","volume":"5","author":"Miller","year":"1985","journal-title":"Int J Periodontics Restorative Dent"},{"key":"B55","doi-asserted-by":"publisher","first-page":"7","DOI":"10.14219\/jada.archive.1964.0034","article-title":"The simplified oral hygiene index","volume":"68","author":"Greene","year":"1964","journal-title":"J Am Dent Assoc"},{"key":"B56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1902\/jop.1998.69.1.1","article-title":"A comparison of the site-specificity of supragingival and subgingival calculus deposition","volume":"69","author":"Corbett","year":"1998","journal-title":"J Periodontol"},{"key":"B57","doi-asserted-by":"publisher","first-page":"1715","DOI":"10.1177\/00220345950740101401","article-title":"The site-specificity of supragingival calculus deposition on the lingual surfaces of the six permanent lower anterior teeth in humans and the effects of age, sex, gum-chewing habits, and the time since the last prophylaxis on calculus scores","volume":"74","author":"Macpherson","year":"1995","journal-title":"J Dent Res"},{"key":"B58","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1186\/s12903-025-06020-3","article-title":"Prevalence, etiology and clinical characteristics of gingival recession in a sample of adult Egyptian dental patients: a cross sectional study","volume":"25","author":"Sarhan","year":"2025","journal-title":"BMC Oral Health"},{"key":"B59","first-page":"188","article-title":"Student classroom behavior detection and classification: Swin transformer and mask RCNN","author":"Huang","year":"2025"},{"key":"B60","doi-asserted-by":"crossref","DOI":"10.1109\/ICCV48922.2021.00986","article-title":"Swin transformer: hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"B61","doi-asserted-by":"publisher","first-page":"3420","DOI":"10.3390\/s23073420","article-title":"High-resolution Swin transformer for automatic medical image segmentation","volume":"23","author":"Wei","year":"2023","journal-title":"Sensors"},{"key":"B62","doi-asserted-by":"publisher","first-page":"503","DOI":"10.3390\/medicina57050503","article-title":"Foundational statistical principles in medical research: sensitivity, specificity, positive predictive value, and negative predictive value","volume":"57","author":"Monaghan","year":"2021","journal-title":"Medicina"},{"key":"B63","doi-asserted-by":"publisher","first-page":"1824","DOI":"10.1007\/s10278-024-01054-5","article-title":"A systematic review on caries detection, classification, and segmentation from x-ray images: methods, datasets, evaluation, and open opportunities","volume":"37","author":"Zanini","year":"2024","journal-title":"J Imaging Inform Med"},{"key":"B64","doi-asserted-by":"publisher","first-page":"768","DOI":"10.3390\/medicina59040768","article-title":"Diagnostic test accuracy of artificial intelligence in detecting periapical periodontitis on two-dimensional radiographs: a retrospective study and literature review","volume":"59","author":"Issa","year":"2023","journal-title":"Medicina"},{"key":"B65","doi-asserted-by":"publisher","first-page":"20210435","DOI":"10.1259\/bjr.20210435","article-title":"Improving reference standards for validation of AI-based radiography","volume":"94","author":"Duggan","year":"2021","journal-title":"Br J Radiol"},{"key":"B66","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1007\/s10140-025-02353-2","article-title":"Comparative accuracy of two commercial AI algorithms for musculoskeletal trauma detection in emergency radiographs","volume":"32","author":"Huhtanen","year":"2025","journal-title":"Emerg Radiol"},{"key":"B67","doi-asserted-by":"crossref","DOI":"10.1109\/ICCV.2017.322","article-title":"Mask R-CNN","author":"He","year":"2017"},{"key":"B68","doi-asserted-by":"publisher","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":"Sci Rep"},{"key":"B69","doi-asserted-by":"publisher","first-page":"351","DOI":"10.5624\/isd.20220078","article-title":"Deep learning-based apical lesion segmentation from panoramic radiographs","volume":"52","author":"Song","year":"2022","journal-title":"Imaging Sci Dent"}],"container-title":["Frontiers in Dental Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdmed.2026.1814876\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T05:52:06Z","timestamp":1778219526000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdmed.2026.1814876\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,8]]},"references-count":69,"alternative-id":["10.3389\/fdmed.2026.1814876"],"URL":"https:\/\/doi.org\/10.3389\/fdmed.2026.1814876","relation":{},"ISSN":["2673-4915"],"issn-type":[{"value":"2673-4915","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,8]]},"article-number":"1814876"}}