{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T17:56:11Z","timestamp":1775066171545,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:00:00Z","timestamp":1673308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Predicting dental development in individuals, especially children, is important in evaluating dental maturity and determining the factors that influence the development of teeth and growth of jaws. Dental development can be accelerated in patients with an accelerated skeletal growth rate and can be related to the skeletal growth pattern as a child. The dental age (DA) of an individual is essential to the dentist for planning treatment in relation to maxillofacial growth. A deep-learning-based regression model was developed in this study using panoramic radiograph images to predict DA. The dataset included 529 samples of panoramic radiographs collected from the dental hospital at Imam Abdulrahman Bin Faisal university in Saudi Arabia. Different deep learning methods were applied to implement the model, including Xception, VGG16, DenseNet121, and ResNet50. The results indicated that the Xception model had the best performance, with an error rate of 1.417 for the 6\u201311 age group. The proposed model can assist the dentist in determining the appropriate treatment for patients based on their DA rather than their chronological age.<\/jats:p>","DOI":"10.3390\/bdcc7010008","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T04:59:58Z","timestamp":1673413198000},"page":"8","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Predictive Artificial Intelligence Model for Detecting Dental Age Using Panoramic Radiograph Images"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8246-4658","authenticated-orcid":false,"given":"Sumayh S.","family":"Aljameel","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lujain","family":"Althumairy","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Basmah","family":"Albassam","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ghoson","family":"Alsheikh","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lama","family":"Albluwi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reem","family":"Althukair","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhanad","family":"Alhareky","sequence":"additional","affiliation":[{"name":"Department of Preventive Dental Science, College of Dentistry, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdulaziz","family":"Alamri","sequence":"additional","affiliation":[{"name":"Department of Preventive Dental Science, College of Dentistry, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Afnan","family":"Alabdan","sequence":"additional","affiliation":[{"name":"Department of Preventive Dental Science, College of Dentistry, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suliman Y.","family":"Shahin","sequence":"additional","affiliation":[{"name":"Department of Preventive Dental Science, College of Dentistry, Imam Abdulrahman Bin Faisal University, P.O. 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Front., 1\u201319.","DOI":"10.1007\/s10796-021-10146-4"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100918","DOI":"10.1016\/j.imu.2022.100918","article-title":"Canine Impaction Classification from Panoramic Dental Radiographic Images Using Deep Learning Models","volume":"30","author":"Aljabri","year":"2022","journal-title":"Inform. Med. Unlocked"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Thurzo, A., Urbanov\u00e1, W., Nov\u00e1k, B., Czako, L., Siebert, T., Stano, P., Marekov\u00e1, S., Fountoulaki, G., Kosn\u00e1\u010dov\u00e1, H., and Varga, I. (2022). Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis. Healthcare, 10.","DOI":"10.3390\/healthcare10071269"},{"key":"ref_6","doi-asserted-by":"crossref","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":"ref_7","first-page":"591","article-title":"Dental Age Estimation Based on X-Ray Images","volume":"62","author":"Mualla","year":"2020","journal-title":"Comput. Mater. Contin."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"19","DOI":"10.5624\/isd.2019.49.1.19","article-title":"Dental Age Estimation Using the Pulp-to-Tooth Ratio in Canines by Neural Networks","volume":"49","author":"Farhadian","year":"2019","journal-title":"Imaging Sci. Dent."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1007\/s00414-020-02489-5","article-title":"Comparison of Different Machine Learning Approaches to Predict Dental Age Using Demirjian\u2019s Staging Approach","volume":"135","author":"Galibourg","year":"2021","journal-title":"Int. J. Leg. Med."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tao, J., Wang, J., Wang, A., Xie, Z., Wang, Z., Wu, S., Hassanien, A.E., and Xiao, K. (2020). Dental Age Estimation: A Machine Learning Perspective. International Conference on Advanced Machine Learning Technologies and Applications, Springer.","DOI":"10.1007\/978-3-030-14118-9_71"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tao, J., Chen, M., Wang, J., Liu, L., Hassanien, A.E., and Xiao, K. (2018). Dental Age Estimation in East Asian Population with Least Squares Regression. International Conference on Advanced Machine Learning Technologies and Applications, Springer.","DOI":"10.1007\/978-3-319-74690-6_64"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"111245","DOI":"10.1016\/j.forsciint.2022.111245","article-title":"Dental Age Assessment Based on CBCT Images Using Machine Learning Algorithms","volume":"334","author":"Saric","year":"2022","journal-title":"Forensic. Sci. Int."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s41044-016-0014-0","article-title":"Big Data Preprocessing: Methods and Prospects","volume":"1","author":"Luengo","year":"2016","journal-title":"Big Data Anal."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of Deep Learning: Concepts, CNN Architectures, Challenges, Applications, Future Directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Xie, W., Li, Z., Xu, Y., Gardoni, P., and Li, W. (2022). Evaluation of Different Bearing Fault Classifiers in Utilizing CNN Feature Extraction Ability. Sensors, 22.","DOI":"10.3390\/s22093314"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Daradkeh, M., Abualigah, L., Atalla, S., and Mansoor, W. (2022). Scientometric Analysis and Classification of Research Using Convolutional Neural Networks: A Case Study in Data Science and Analytics. Electronics, 11.","DOI":"10.3390\/electronics11132066"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kong, J., Wang, H., Yang, C., Jin, X., Zuo, M., and Zhang, X. (2022). A Spatial Feature-Enhanced Attention Neural Network with High-Order Pooling Representation for Application in Pest and Disease Recognition. Agriculture, 12.","DOI":"10.3390\/agriculture12040500"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Novac, O.-C., Chirodea, M.C., Novac, C.M., Bizon, N., Oproescu, M., Stan, O.P., and Gordan, C.E. (2022). Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network. Sensors, 22.","DOI":"10.3390\/s22228872"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108116","DOI":"10.1016\/j.measurement.2020.108116","article-title":"Convolutional Neural Network Architecture for Beam Instabilities Identification in Synchrotron Radiation Systems as an Anomaly Detection Problem","volume":"165","author":"Piekarski","year":"2020","journal-title":"Measurement"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2483","DOI":"10.1007\/s12652-020-02386-0","article-title":"Multi-Modality Medical Image Fusion Technique Using Multi-Objective Differential Evolution Based Deep Neural Networks","volume":"12","author":"Kaur","year":"2021","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2241","DOI":"10.1049\/iet-ipr.2018.6656","article-title":"Accurate Leukocoria Predictor Based on Deep VGG-Net CNN Technique","volume":"14","author":"Rao","year":"2020","journal-title":"IET Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ewe, E.L.R., Lee, C.P., Kwek, L.C., and Lim, K.M. (2022). Hand Gesture Recognition via Lightweight VGG16 and Ensemble Classifier. Appl. Sci., 12.","DOI":"10.3390\/app12157643"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"14588","DOI":"10.1109\/ACCESS.2019.2961260","article-title":"RDense: A Protein-RNA Binding Prediction Model Based on Bidirectional Recurrent Neural Network and Densely Connected Convolutional Networks","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ogundokun, R.O., Maskeli\u016bnas, R., Misra, S., and Damasevicius, R. (2022). A Novel Deep Transfer Learning Approach Based on Depth-Wise Separable CNN for Human Posture Detection. Information, 13.","DOI":"10.3390\/info13110520"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"11417","DOI":"10.1109\/JSTARS.2021.3117975","article-title":"Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Roumpakias, E., and Stamatelos, T. (2022). Prediction of a Grid-Connected Photovoltaic Park\u2019s Output with Artificial Neural Networks Trained by Actual Performance Data. Appl. Sci., 12.","DOI":"10.3390\/app12136458"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/7\/1\/8\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:05:44Z","timestamp":1760119544000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/7\/1\/8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,10]]},"references-count":26,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["bdcc7010008"],"URL":"https:\/\/doi.org\/10.3390\/bdcc7010008","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,10]]}}}