{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T04:13:03Z","timestamp":1791519183718,"version":"4.3.4"},"reference-count":31,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:00:00Z","timestamp":1765497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Dental caries is among the most common oral health problems, resulting from demineralization of dental hard tissues in acidic environments. Early diagnosis is essential to prevent severe tissue destruction, systemic complications and costly treatments. Conventional visual interpretation of panoramic radiographs, though widely used, remains subjective and variable. This study evaluated the effectiveness of image processing techniques and artificial intelligence (AI)-assisted models for automated detection and classification of dental caries on panoramic radiographs, emphasizing numerical image data analysis. Methods: From 1084 panoramic radiographs, 405 were selected and classified into interproximal, occlusal and secondary caries groups. Each was segmented and one representative region was analyzed using the image data representation method. Numerical descriptors\u2014brightness, contrast, entropy and histogram parameters\u2014were extracted and evaluated with several machine learning algorithms. Results: Among tested models, the Decision Tree algorithm achieved the highest classification accuracy (0.988 at the 0.2 train-test ratio), showing superior and consistent results across caries types. Random Forest also demonstrated strong performance with limited training data, while Gaussian Na\u00efve Bayes, KNN and RBFNN were less effective. Conclusions: The integration of numerical image features with AI-based models demonstrated high diagnostic accuracy and clinical interpretability, particularly with Decision Tree algorithm. These results highlight the potential of AI-assisted analysis of panoramic radiographs to enhance diagnostic reliability, reduce subjectivity and support more effective treatment planning. Further multicentre studies with larger and more diverse datasets are recommended to validate generalizability.<\/jats:p>","DOI":"10.3390\/diagnostics15243167","type":"journal-article","created":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T08:30:17Z","timestamp":1765528217000},"page":"3167","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Integrating Numerical Data with AI-Based Image Processing Techniques to Improve the Diagnostic Accuracy of Detecting Dental Caries in Panoramic Radiographs"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9280-4572","authenticated-orcid":false,"given":"Beng\u00fc","family":"Ba\u015far\u0131","sequence":"first","affiliation":[{"name":"Department of Restorative Dentistry, Faculty of Dentistry, Near East University, N. Cyprus\/Mersin 10, Nicosia 99138, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6289-3105","authenticated-orcid":false,"given":"Nuran","family":"Ulusoy","sequence":"additional","affiliation":[{"name":"Department of Restorative Dentistry, Faculty of Dentistry, Near East University, N. Cyprus\/Mersin 10, Nicosia 99138, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2751-0479","authenticated-orcid":false,"given":"Kamil","family":"Dimililer","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Applied Artificial Intelligence Research Center (AAIRC), Research Center for Science, Technology and Engineering (BILTEM), Near East University, N. Cyprus\/Mersin 10, Nicosia 99138, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,12]]},"reference":[{"key":"ref_1","unstructured":"Rathee, M., and Sapra, A. (2023, June 21). Dental Caries, StatPearls, Available online: https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK551699\/."},{"key":"ref_2","first-page":"23","article-title":"Investigations for the Early Detection of Dental Caries\u2014A Review","volume":"19","author":"Beedam","year":"2020","journal-title":"IOSR J. Dent. Med. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Izzetti, R., Nisi, M., Aringhieri, G., Crocetti, L., Graziani, F., and Nardi, C. (2021). Basic Knowledge and New Advances in Panoramic Radiography Imaging Techniques: A Narrative Review on What Dentists and Radiologists Should Know. Appl. Sci., 11.","DOI":"10.3390\/app11177858"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1016\/j.jacr.2010.03.004","article-title":"Long Radiology Workdays Reduce Detection and Accommodation Accuracy","volume":"7","author":"Krupinski","year":"2010","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"207","DOI":"10.5624\/isd.2012.42.4.207","article-title":"Positioning Errors and Quality Assessment in Panoramic Radiography","volume":"42","author":"Dhillon","year":"2012","journal-title":"Imaging Sci. Dent."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"219","DOI":"10.58711\/turkishjdentres.vi.1301420","article-title":"Di\u015f \u00c7\u00fcr\u00fc\u011f\u00fcn\u00fcn Te\u015fhisi ve Bu Ama\u00e7la Kullan\u0131lan G\u00fcncel Y\u00f6ntemler","volume":"2","author":"Bala","year":"2023","journal-title":"J. Turk. Dent. Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Al-Khalifa, K.S., Ahmed, W.M., Azhari, A.A., Qaw, M., Alsheikh, R., Alqudaihi, F., and Alfaraj, A. (2024). The Use of Artificial Intelligence in Caries Detection: A Review. Bioengineering, 11.","DOI":"10.3390\/bioengineering11090936"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4641","DOI":"10.1038\/s41598-024-84737-x","article-title":"Advanced AI-Driven Detection of Interproximal Caries in Bitewing Radiographs Using YOLOv8","volume":"15","author":"Bayati","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"8415705","DOI":"10.1155\/2022\/8415705","article-title":"A Survey of Dental Caries Segmentation and Detection Techniques","volume":"2022","author":"Majanga","year":"2022","journal-title":"Sci. World J."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ayhan, B., Ayan, E., and Ats\u00fc, S. (2025). Detection of Dental Caries under Fixed Dental Prostheses by Analyzing Digital Panoramic Radiographs with Artificial Intelligence Algorithms Based on Deep Learning Methods. BMC Oral Health, 25.","DOI":"10.1186\/s12903-025-05577-3"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Rohrer, C., Krois, J., Patel, J., Meyer-Lueckel, H., Rodrigues, J.A., and Schwendicke, F. (2022). Segmentation of Dental Restorations on Panoramic Radiographs Using Deep Learning. Diagnostics, 12.","DOI":"10.3390\/diagnostics12061316"},{"key":"ref_12","unstructured":"Haghanifar, A., Molahasani Majdabadi, M., and Ko, S.-B. (2020). PaXNet: Dental Caries Detection in Panoramic X-Ray Using Ensemble Transfer Learning and Capsule Classifier. arXiv."},{"key":"ref_13","unstructured":"Gonzalez, R.C., and Woods, R.E. (2008). Digital Image Processing, Pearson. [3rd ed.]. Available online: https:\/\/sde.uoc.ac.in\/sites\/default\/files\/sde_videos\/Digital%20Image%20Processing%203rd%20ed.%20-%20R.%20Gonzalez,%20R.%20Woods-ilovepdf-compressed.pdf."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bonny, T., Al Nassan, W., Obaideen, K., Rabie, T., AlMallahi, M.N., and Gupta, S. (2024). Primary Methods and Algorithms in Artificial-Intelligence-Based Dental Image Analysis: A Systematic Review. Algorithms, 17.","DOI":"10.3390\/a17120567"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mohammed, M., Khan, M.B., and Bashier, M.B.E. (2016). Machine Learning: Algorithms and Applications, CRC Press.","DOI":"10.1201\/9781315371658"},{"key":"ref_16","unstructured":"Han, J., Kamber, M., and Pei, J. (2011). Data Mining: Concepts and Techniques, Morgan Kaufmann. [3rd ed.]."},{"key":"ref_17","unstructured":"Witten, I.H., and Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann. [2nd ed.]."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s42979-021-00535-6","article-title":"Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7499","DOI":"10.1109\/TNNLS.2022.3229161","article-title":"Deep Neural Networks and Tabular Data: A Survey","volume":"35","author":"Borisov","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_20","unstructured":"Gonzalez, R.C., and Woods, R.E. (2018). Digital Image Processing, Pearson Education. [4th ed.]."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"453098","DOI":"10.1155\/2013\/453098","article-title":"Backpropagation Neural Network Implementation for Medical Image Compression","volume":"2013","author":"Dimililer","year":"2013","journal-title":"J. Appl. Math."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1117\/12.477378","article-title":"Measuring Colorfulness in Natural Images","volume":"5007","author":"Hasler","year":"2003","journal-title":"Proc. SPIE Electron. Imaging: Human Vision and Electronic Imaging VIII"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine Learning: Algorithms, Real-World Applications and Research Directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_25","first-page":"73","article-title":"Tree-Based Machine Learning Methods for Survey Research","volume":"13","author":"Kern","year":"2019","journal-title":"Surv. Res. Methods"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1515\/comp-2016-0005","article-title":"Radial Basis Function Neural Networks: A Topical State-of-the-Art Survey","volume":"6","author":"Dash","year":"2016","journal-title":"Open Comput. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Oztekin, F., Katar, O., Sadak, F., Yildirim, M., Cakar, H., Aydogan, M., Ozpolat, Z., Talo Yildirim, T., Yildirim, O., and Faust, O. (2023). An Explainable Deep Learning Model to Predict Dental Caries Using Panoramic Radiograph Images. Diagnostics, 13.","DOI":"10.3390\/diagnostics13020226"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8295","DOI":"10.1007\/s00521-022-08102-7","article-title":"Lightweight Deep Learning Methods for Panoramic Dental X-Ray Image Segmentation","volume":"35","author":"Lin","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Alharbi, S.S., AlRugaibah, A.A., Alhasson, H.F., and Khan, R.U. (2023). Detection of Cavities from Dental Panoramic X-Ray Images Using Nested U-Net Models. Appl. Sci., 13.","DOI":"10.3390\/app132312771"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kwiatek, J., Le\u015bna, M., Pisk\u00f3rz, W., and Kaczewiak, J. (2025). Comparison of the Diagnostic Accuracy of an AI-Based System for Dental Caries Detection and Clinical Evaluation Conducted by Dentists. J. Clin. Med., 14.","DOI":"10.3390\/jcm14051566"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.jdent.2018.07.015","article-title":"Detection and Diagnosis of Dental Caries Using a Deep Learning-Based Convolutional Neural Network Algorithm","volume":"77","author":"Lee","year":"2018","journal-title":"J. Dent."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/24\/3167\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T08:46:08Z","timestamp":1765529168000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/24\/3167"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,12]]},"references-count":31,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["diagnostics15243167"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15243167","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,12]]}}}