{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T12:52:40Z","timestamp":1792673560758,"version":"4.5.0"},"reference-count":29,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T00:00:00Z","timestamp":1754352000000},"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: The diagnosis of pediatric dental conditions from panoramic radiographs is uniquely challenging due to the dynamic nature of the mixed dentition phase, which can lead to subjective and inconsistent interpretations. This study aims to develop and rigorously validate an advanced deep learning model to enhance diagnostic accuracy and efficiency in pediatric dentistry, providing an objective tool to support clinical decision-making. Methods: An initial comparative study of four state-of-the-art YOLO variants (YOLOv8, v9, v10, and v11) was conducted to identify the optimal architecture for detecting four common findings: Dental Caries, Deciduous Tooth, Root Canal Treatment, and Pulpotomy. A stringent two-tiered validation strategy was employed: a primary public dataset (n = 644 images) was used for training and model selection, while a completely independent external dataset (n = 150 images) was used for final testing. All annotations were validated by a dual-expert team comprising a board-certified pediatric dentist and an experienced oral and maxillofacial radiologist. Results: Based on its leading performance on the internal validation set, YOLOv11x was selected as the optimal model, achieving a mean Average Precision (mAP50) of 0.91. When evaluated on the independent external test set, the model demonstrated robust generalization, achieving an overall F1-Score of 0.81 and a mAP50 of 0.82. It yielded clinically valuable recall rates for therapeutic interventions (Root Canal Treatment: 88%; Pulpotomy: 86%) and other conditions (Deciduous Tooth: 84%; Dental Caries: 79%). Conclusions: Validated through a rigorous dual-dataset and dual-expert process, the YOLOv11x model demonstrates its potential as an accurate and reliable tool for automated detection in pediatric panoramic radiographs. This work suggests that such AI-driven systems can serve as valuable assistive tools for clinicians by supporting diagnostic workflows and contributing to the consistent detection of common dental findings in pediatric patients.<\/jats:p>","DOI":"10.3390\/diagnostics15151961","type":"journal-article","created":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T07:49:58Z","timestamp":1754380198000},"page":"1961","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Detection of Dental Anomalies in Digital Panoramic Images Using YOLO: A Next Generation Approach Based on Single Stage Detection Models"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2056-9988","authenticated-orcid":false,"given":"U\u011fur","family":"\u015eevik","sequence":"first","affiliation":[{"name":"Department of Computer Science, Faculty of Science, Karadeniz Technical University, Kanuni Campus, 61080 Trabzon, Turkey"},{"name":"Retina R&D Software and Engineering Services Ltd., Trabzon Teknokent, 61080 Trabzon, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6204-5535","authenticated-orcid":false,"given":"Onur","family":"Mutlu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Science, Karadeniz Technical University, Kanuni Campus, 61080 Trabzon, Turkey"},{"name":"Retina R&D Software and Engineering Services Ltd., Trabzon Teknokent, 61080 Trabzon, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,5]]},"reference":[{"key":"ref_1","first-page":"658","article-title":"Pediatric Dentistry and General Anesthesia","volume":"19","year":"2019","journal-title":"Ank. Med. J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sivari, E., Senirkentli, G.B., Bostanci, E., Guzel, M.S., Acici, K., and Asuroglu, T. (2023). Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review. Diagnostics, 13.","DOI":"10.3390\/diagnostics13152512"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/S0140-6736(19)31146-8","article-title":"Oral Diseases: A Global Public Health Challenge","volume":"394","author":"Peres","year":"2019","journal-title":"Lancet"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1177\/0022034518769255","article-title":"Self-Limiting versus Conventional Caries Removal: A Randomized Clinical Trial","volume":"97","author":"Ali","year":"2018","journal-title":"J. Dent. Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Muresan, M.P., Barbura, A.R., and Nedevschi, S. (2020, January 3\u20135). Teeth Detection and Dental Problem Classification in Panoramic X-Ray Images using Deep Learning and Image Processing Techniques. Proceedings of the 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania.","DOI":"10.1109\/ICCP51029.2020.9266244"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"103226","DOI":"10.1016\/j.jdent.2019.103226","article-title":"Convolutional Neural Networks for Dental Image Diagnostics: A Scoping Review","volume":"91","author":"Schwendicke","year":"2019","journal-title":"J. Dent."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"53","DOI":"10.5624\/isd.2020.50.1.53","article-title":"Incidental Findings in a Consecutive Series of Digital Panoramic Radiographs","volume":"50","author":"Macdonald","year":"2020","journal-title":"Imaging Sci. Dent."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"50","DOI":"10.12816\/0059360","article-title":"Use of Artificial Intelligence in Dentistry: Current Clinical Trends and Research Advances","volume":"28","author":"Nguyen","year":"2021","journal-title":"Dent. News"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"594","DOI":"10.4103\/ejd.ejd_325_18","article-title":"History and Application of Artificial Neural Networks in Dentistry","volume":"12","author":"Park","year":"2018","journal-title":"Eur. J. Dent."},{"key":"ref_10","first-page":"26","article-title":"Artificial Intelligence In Dentistry. Sa\u011fl\u0131k Bilimlerinde Yapay Zeka Dergisi","volume":"1","author":"Saglam","year":"2021","journal-title":"J. Artif. Intell. Health Sci."},{"key":"ref_11","first-page":"113","article-title":"Automated Data Processing and Feature Engineering for Deep Learning and Big Data Applications: A Survey","volume":"3","author":"Mumuni","year":"2024","journal-title":"J. Inf. Intell."},{"key":"ref_12","first-page":"100004","article-title":"A Review: Deep Learning for Medical Image Segmentation Using Multi-Modality Fusion","volume":"3\u20134","author":"Zhou","year":"2020","journal-title":"Array"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"20220122","DOI":"10.1259\/dmfr.20220122","article-title":"The Effect of a Deep-Learning Tool on Dentists\u2019 Performances in Detecting Apical Radiolucencies on Periapical Radiographs","volume":"51","author":"Hamdan","year":"2022","journal-title":"DentoMaxillofac. Radiol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Prados-Privado, M., Villal\u00f3n, J.G., Mart\u00ednez-Mart\u00ednez, C.H., and Ivorra, C. (2020). Dental Images Recognition Technology and Applications: A Literature Review. Appl. Sci., 10.","DOI":"10.3390\/app10082856"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Naqvi, R.A., Arsalan, M., Qaiser, T., Khan, T.M., and Razzak, I. (2022). Sensor Data Fusion Based on Deep Learning for Computer Vision Applications and Medical Applications. Sensors, 22.","DOI":"10.3390\/s22208058"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Vishwanathaiah, S., Fageeh, H.N., Khanagar, S.B., and Maganur, P.C. (2023). Artificial Intelligence Its Uses and Application in Pediatric Dentistry: A Review. Biomedicines, 11.","DOI":"10.3390\/biomedicines11030788"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"\u00c7olak, H., D\u00fclgergil, \u00c7., Dalli, M., and Hamidi, M. (2013). Early Childhood Caries Update: A Review of Causes, Diagnoses, and Treatments. J. Nat. Sci. Biol. Med., 4.","DOI":"10.4103\/0976-9668.107257"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"275","DOI":"10.5624\/isd.20220050","article-title":"A Deep Learning Approach to Permanent Tooth Germ Detection on Pediatric Panoramic Radiographs","volume":"52","author":"Kaya","year":"2022","journal-title":"Imaging Sci. Dent."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"20200172","DOI":"10.1259\/dmfr.20200172","article-title":"Artificial Intelligence System for Automatic Deciduous Tooth Detection and Numbering in Panoramic Radiographs","volume":"50","author":"Bayrakdar","year":"2021","journal-title":"DentoMaxillofac. Radiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1038\/s41597-023-02237-5","article-title":"Children\u2019s Dental Panoramic Radiographs Dataset for Caries Segmentation and Dental Disease Detection","volume":"10","author":"Zhang","year":"2023","journal-title":"Sci. Data"},{"key":"ref_21","unstructured":"Viola, P., and Jones, M. (2001, January 8\u201314). Rapid Object Detection Using a Boosted Cascade of Simple Features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA."},{"key":"ref_22","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of Oriented Gradients for Human Detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2019, January 13\u201319). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. Proceedings of the IEEE International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sapkota, R., Meng, Z., Churuvija, M., Du, X., Ma, Z., and Karkee, M. (2024). Comprehensive Performance Evaluation of YOLO11, YOLOv10, YOLOv9, and YOLOv8 on Detecting and Counting Fruitlet in Complex Orchard Environments. arXiv.","DOI":"10.32388\/E9Y7XI"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"104779","DOI":"10.1016\/j.jdent.2023.104779","article-title":"A Novel Collaborative Learning Model for Mixed Dentition and Fillings Segmentation in Panoramic Radiographs","volume":"140","author":"Bumann","year":"2024","journal-title":"J. Dent."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/15\/1961\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:23:16Z","timestamp":1760034196000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/15\/1961"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,5]]},"references-count":29,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["diagnostics15151961"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15151961","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,5]]}}}