{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T12:17:25Z","timestamp":1790252245102,"version":"4.1.0"},"reference-count":37,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T00:00:00Z","timestamp":1725321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Forschungsgemeinschaft Dental e.V. (FGD)","award":["01\/2023"],"award-info":[{"award-number":["01\/2023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: Early childhood caries (ECC) is a widespread and severe oral health problem that potentially affects the general health of children. Visual\u2013tactile examination remains the diagnostic method of choice to diagnose ECC, although visual examination could be automated by artificial intelligence (AI) tools in the future. The aim of this study was the external validation of a recently published and freely accessible AI-based model for detecting ECC and classifying carious lesions in dental photographs. Methods: A total of 143 anonymised photographs of anterior deciduous teeth (ECC = 107, controls = 36) were visually evaluated by the dental study group (reference test) and analysed using the AI-based model (test method). Diagnostic performance was determined statistically. Results: ECC detection accuracy was 97.2%. Diagnostic performance varied between carious lesion classes (noncavitated lesions, greyish translucency\/microcavity, cavitation, destructed tooth), with accuracies ranging from 88.9% to 98.1%, sensitivities ranging from 68.8% to 98.5% and specificities ranging from 86.1% to 99.4%. The area under the curve ranged from 0.834 to 0.964. Conclusions: The performance of the AI-based model is similar to that reported for the internal dataset used by developers. Further studies with independent image samples are required to comprehensively gauge the performance of the model.<\/jats:p>","DOI":"10.3390\/jcm13175215","type":"journal-article","created":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T04:06:41Z","timestamp":1725336401000},"page":"5215","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Validation of an Artificial Intelligence-Based Model for Early Childhood Caries Detection in Dental Photographs"],"prefix":"10.3390","volume":"13","author":[{"given":"Julia","family":"Schwarzmaier","sequence":"first","affiliation":[{"name":"Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University of Munich, 80336 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elisabeth","family":"Frenkel","sequence":"additional","affiliation":[{"name":"Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University of Munich, 80336 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5847-5838","authenticated-orcid":false,"given":"Julia","family":"Neumayr","sequence":"additional","affiliation":[{"name":"Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University of Munich, 80336 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7654-2493","authenticated-orcid":false,"given":"Nour","family":"Ammar","sequence":"additional","affiliation":[{"name":"Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University of Munich, 80336 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Kessler","sequence":"additional","affiliation":[{"name":"Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University of Munich, 80336 Munich, Germany"},{"name":"Department of Prosthetic Dentistry, Center for Dental Medicine, University Hospital Freiburg, 79106 Freiburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Falk","family":"Schwendicke","sequence":"additional","affiliation":[{"name":"Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University of Munich, 80336 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4063-2291","authenticated-orcid":false,"given":"Jan","family":"K\u00fchnisch","sequence":"additional","affiliation":[{"name":"Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University of Munich, 80336 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9829-4427","authenticated-orcid":false,"given":"Helena","family":"Dujic","sequence":"additional","affiliation":[{"name":"Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University of Munich, 80336 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"GBD 2017 Oral Disorders Collaborators, Bernabe, E., Marcenes, W., Hernandez, C.R., Bailey, J., Abreu, L.G., Alipour, V., Amini, S., Arabloo, J., and Arefi, Z. (2020). Global, Regional, and National Levels and Trends in Burden of Oral Conditions from 1990 to 2017: A Systematic Analysis for the Global Burden of Disease 2017 Study. J. Dent. Res., 99, 362\u2013373.","DOI":"10.1177\/0022034520908533"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1177\/0022034515573272","article-title":"Global burden of untreated caries: A systematic review and metaregression","volume":"94","author":"Kassebaum","year":"2015","journal-title":"J. Dent. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1111\/ipd.12783","article-title":"The global prevalence of early childhood caries: A systematic review with meta-analysis using the WHO diagnostic criteria","volume":"31","author":"Uribe","year":"2021","journal-title":"Int. J. Paediatr. Dent."},{"key":"ref_4","first-page":"200","article-title":"Prevalence and Measurement of Dental Caries in Young Children","volume":"37","author":"Dye","year":"2015","journal-title":"Pediatr. Dent."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1111\/ipd.12484","article-title":"Early childhood caries epidemiology, aetiology, risk assessment, societal burden, management, education, and policy: Global perspective","volume":"29","author":"Tinanoff","year":"2019","journal-title":"Int. J. Paediatr. Dent."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1016\/j.pcl.2018.05.004","article-title":"Early Childhood Caries","volume":"65","author":"Seow","year":"2018","journal-title":"Pediatr. Clin. N. Am."},{"key":"ref_7","unstructured":"American Academy of Pediatric Dentistry (2020). Policy on early childhood caries (ECC): Classifications, consequences, and preventive strategies. The Reference Manual of Pediatric Dentistry, American Academy of Pediatric Dentistry."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1111\/j.1600-0528.1999.tb02026.x","article-title":"Early childhood caries: Nomenclature and case definition","volume":"27","author":"Wyne","year":"1999","journal-title":"Community Dent. Oral Epidemiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"C67","DOI":"10.1177\/154405910408301s13","article-title":"Improving clinical visual detection--potential for caries clinical trials","volume":"83","author":"Ekstrand","year":"2004","journal-title":"J. Dent. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1159\/000486429","article-title":"The International Caries Detection and Assessment System\u2014ICDAS: A Systematic Review","volume":"52","author":"Ekstrand","year":"2018","journal-title":"Caries Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1159\/000262404","article-title":"Reproducibility and accuracy of three methods for assessment of demineralization depth of the occlusal surface: An in vitro examination","volume":"31","author":"Ekstrand","year":"1997","journal-title":"Caries Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2500","DOI":"10.3390\/ijerph6092500","article-title":"Development, methodology and potential of the new Universal Visual Scoring System (UniViSS) for caries detection and diagnosis","volume":"6","author":"Goddon","year":"2009","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1159\/000016526","article-title":"Reliability of a new caries diagnostic system differentiating between active and inactive caries lesions","volume":"33","author":"Nyvad","year":"1999","journal-title":"Caries Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1038\/s41746-023-00944-2","article-title":"Detection and localization of caries and hypomineralization on dental photographs with a vision transformer model","volume":"6","author":"Felsch","year":"2023","journal-title":"NPJ Digit. Med."},{"key":"ref_15","doi-asserted-by":"crossref","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":"ref_16","doi-asserted-by":"crossref","unstructured":"Thanh, M.T.G., Van Toan, N., Ngoc, V.T.N., Tra, N.T., Giap, C.N., and Nguyen, D.M. (2022). Deep Learning Application in Dental Caries Detection Using Intraoral Photos Taken by Smartphones. Appl. Sci., 12.","DOI":"10.3390\/app12115504"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Al-Jallad, N., Ly-Mapes, O., Hao, P., Ruan, J., Ramesh, A., Luo, J., Wu, T.T., Dye, T., Rashwan, N., and Ren, J. (2022). Artificial intelligence-powered smartphone application, AICaries, improves at-home dental caries screening in children: Moderated and unmoderated usability test. PLoS Digit. Health, 1.","DOI":"10.1371\/journal.pdig.0000046"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xiong, Y., Zhang, H., Zhou, S., Lu, M., Huang, J., Huang, Q., Huang, B., and Ding, J. (2024). Simultaneous detection of dental caries and fissure sealant in intraoral photos by deep learning: A pilot study. BMC Oral Health, 24.","DOI":"10.1186\/s12903-024-04254-1"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"104886","DOI":"10.1016\/j.jdent.2024.104886","article-title":"Detection of caries around restorations on bitewings using deep learning","volume":"143","author":"Chaves","year":"2024","journal-title":"J. Dent."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"20552076231216681","DOI":"10.1177\/20552076231216681","article-title":"Artificial intelligence (AI) in restorative dentistry: Performance of AI models designed for detection of interproximal carious lesions on primary and permanent dentition","volume":"9","author":"Azhari","year":"2023","journal-title":"Digit. Health"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ma, T., Zhu, J., Wang, D., Xu, Z., Bai, H., Ding, P., Chen, X., and Xia, B. (2024). Deep learning-based detection of irreversible pulpitis in primary molars. Int. J. Paediatr. Dent., online ahead of print.","DOI":"10.1111\/ipd.13200"},{"key":"ref_22","first-page":"1","article-title":"A pilot study of a deep learning approach to submerged primary tooth classification and detection","volume":"24","author":"Caliskan","year":"2021","journal-title":"Int. J. Comput. Dent."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"293","DOI":"10.22514\/1053-4625-46.4.6","article-title":"Proposing a CNN Method for Primary and Permanent Tooth Detection and Enumeration on Pediatric Dental Radiographs","volume":"46","author":"Kaya","year":"2022","journal-title":"J. Clin. Pediatr. Dent."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ahn, Y., Hwang, J.J., Jung, Y.H., Jeong, T., and Shin, J. (2021). Automated Mesiodens Classification System Using Deep Learning on Panoramic Radiographs of Children. Diagnostics, 11.","DOI":"10.3390\/diagnostics11081477"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Jeon, K.J., Ha, E.G., Choi, H., Lee, C., and Han, S.S. (2022). Performance comparison of three deep learning models for impacted mesiodens detection on periapical radiographs. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-19753-w"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"20210528","DOI":"10.1259\/dmfr.20210528","article-title":"Deep learning-based identification of mesiodens using automatic maxillary anterior region estimation in panoramic radiography of children","volume":"51","author":"Kim","year":"2022","journal-title":"Dentomaxillofac. Radiol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"h5527","DOI":"10.1136\/bmj.h5527","article-title":"STARD 2015: An updated list of essential items for reporting diagnostic accuracy studies","volume":"351","author":"Bossuyt","year":"2015","journal-title":"BMJ"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"103610","DOI":"10.1016\/j.jdent.2021.103610","article-title":"Artificial intelligence in dental research: Checklist for authors, reviewers, readers","volume":"107","author":"Schwendicke","year":"2021","journal-title":"J. Dent."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s00784-010-0390-1","article-title":"Diagnostic performance of the universal visual scoring system (UniViSS) on occlusal surfaces","volume":"15","author":"Henschel","year":"2011","journal-title":"Clin. Oral Investig."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1159\/000224208","article-title":"How the detection, assessment, diagnosis and monitoring of caries integrate with personalized caries management","volume":"21","author":"Pitts","year":"2009","journal-title":"Monogr. Oral Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"K\u00fchnisch, J., Janjic Rankovic, M., Kapor, S., Sch\u00fcler, I., Krause, F., Michou, S., Ekstrand, K., Eggmann, F., Lussi, A., and Neuhaus, K. (2021). Identifying and avoiding risk of bias in caries diagnostic studies. J. Clin. Med., 15.","DOI":"10.3390\/jcm10153223"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liao, H., Xiao, J., Jallad, N.A., Ly-Mapes, O., and Luo, J. (2020). A Smartphone-Based System for Real-Time Early Childhood Caries Diagnosis. Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-030-60334-2_23"},{"key":"ref_33","doi-asserted-by":"crossref","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":"2022","journal-title":"Oral Dis."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Park, E.Y., Cho, H., Kang, S., Jeong, S., and Kim, E.K. (2022). Caries detection with tooth surface segmentation on intraoral photographic images using deep learning. BMC Oral Health, 22.","DOI":"10.1186\/s12903-022-02589-1"},{"key":"ref_35","doi-asserted-by":"crossref","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":"ref_36","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1177\/1357633X15605225","article-title":"Accuracy of dental images for the diagnosis of dental caries and enamel defects in children and adolescents: A systematic review","volume":"21","author":"Caffery","year":"2015","journal-title":"J. Telemed. Telecare"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bottenberg, P., Jacquet, W., Behrens, C., Stachniss, V., and Jablonski-Momeni, A. (2016). Comparison of occlusal caries detection using the ICDAS criteria on extracted teeth or their photographs. BMC Oral Health, 16.","DOI":"10.1186\/s12903-016-0291-z"}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/13\/17\/5215\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:47:42Z","timestamp":1760111262000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/13\/17\/5215"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,3]]},"references-count":37,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["jcm13175215"],"URL":"https:\/\/doi.org\/10.3390\/jcm13175215","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,3]]}}}