{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T18:12:48Z","timestamp":1773425568323,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"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: Early childhood caries (ECC) affects a substantial proportion of young children worldwide, and timely screening is essential for early intervention and referral. While deep learning has shown promise for automated dental diagnostics, many existing approaches rely on computationally heavy models that limit deployment in community and mobile settings. This study investigates whether compact convolutional neural networks can achieve clinically meaningful performance for screening dental caries from oral photographs. Methods: We curated a dataset of 435 intraoral images from children aged 3\u201314 years, annotated by licensed dentists, and performed patient-level stratified splitting to prevent data leakage. Three convolutional neural networks (ResNet-18, MobileNetV3-Small, and EfficientNet-B0) were fine-tuned using ImageNet-pretrained weights and comparatively evaluated for the detection of dental caries from oral photographs. Models were trained with class-weighted cross-entropy loss and evaluated on a held-out test set using sensitivity, specificity, balanced accuracy, ROC-AUC, and PR-AUC with bootstrap 95% confidence intervals. Results: ResNet-18 achieved the highest balanced accuracy (0.929), weighted F1-score (0.954), and perfect sensitivity (1.00), while EfficientNet-B0 achieved the strongest threshold-independent discrimination with the highest ROC-AUC (0.978) and PR-AUC (0.990). MobileNetV3-Small maintained competitive performance (ROC-AUC 0.952; PR-AUC 0.976) with substantially lower computational complexity. Conclusions: In addition to performance evaluation, we incorporated an interpretability analysis using Grad-CAM to examine model decision behavior. The resulting attribution maps predominantly highlighted clinically relevant tooth regions associated with caries, providing evidence that the models rely on meaningful dental features rather than background artifacts. These results demonstrate that compact, deployment-friendly architectures can achieve clinically meaningful performance for ECC detection, supporting their suitability for scalable, real-world screening applications.<\/jats:p>","DOI":"10.3390\/diagnostics16060862","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T17:36:48Z","timestamp":1773423408000},"page":"862","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Lightweight Deep Learning for Automated Dental Caries Screening from Pediatric Oral Photographs"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4197-0030","authenticated-orcid":false,"given":"Nourah","family":"Alangari","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0075-3306","authenticated-orcid":false,"given":"Nouf","family":"AlShenaifi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kidd, E.A., and Fejerskov, O. (2016). Essentials of Dental Caries, Oxford University Press.","DOI":"10.1093\/oso\/9780198738268.001.0001"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1007\/s00784-017-2234-8","article-title":"Dental anxiety and oral health-related quality of life in aggressive periodontitis patients","volume":"22","author":"Levin","year":"2018","journal-title":"Clin. Oral Investig."},{"key":"ref_3","unstructured":"Centers for Disease Control and Prevention (CDC) (2026, February 10). Selected Findings from the 2024 Oral Health Surveillance Report, Available online: https:\/\/www.cdc.gov\/oral-health\/php\/2024-oral-health-surveillance-report\/selected-findings.html."},{"key":"ref_4","unstructured":"World Health Organization (2026, February 09). Oral Health Fact Sheet. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/oral-health."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1111\/ger.12432","article-title":"Application of machine learning for diagnostic prediction of root caries","volume":"36","author":"Hung","year":"2019","journal-title":"Gerodontology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1177\/0022034519871884","article-title":"Caries detection with near-infrared transillumination using deep learning","volume":"98","author":"Casalegno","year":"2019","journal-title":"J. Dent. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","article-title":"A guide to deep learning in healthcare","volume":"25","author":"Esteva","year":"2019","journal-title":"Nat. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104115","DOI":"10.1016\/j.jdent.2022.104115","article-title":"Deep learning for caries detection: A systematic review","volume":"122","author":"Motamedian","year":"2022","journal-title":"J. Dent."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1071\/AH16119","article-title":"Cost savings from a teledentistry model for school dental screening: An Australian health system perspective","volume":"42","author":"Estai","year":"2018","journal-title":"Aust. Health Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.jebdp.2014.02.008","article-title":"Teledentistry: A key component in access to care","volume":"14","author":"Daniel","year":"2014","journal-title":"J. Evid. Based Dent. Pract."},{"key":"ref_11","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."},{"key":"ref_12","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_13","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (November, January 27). Searching for mobilenetv3. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_14","first-page":"1","article-title":"Deep learning on mobile and embedded devices: State-of-the-art, challenges, and future directions","volume":"53","author":"Chen","year":"2020","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"102077","DOI":"10.1016\/j.jebdp.2024.102077","article-title":"Artificial Intelligence Platforms in Dental Caries Detection: A Systematic Review and Meta-Analysis","volume":"25","author":"Abbott","year":"2025","journal-title":"J.-Evid.-Based Dent. Pract."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1186\/s13244-022-01311-7","article-title":"Reproducibility of artificial intelligence models in computed tomography of the head: A quantitative analysis","volume":"13","author":"Gunzer","year":"2022","journal-title":"Insights Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., and King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Med., 17.","DOI":"10.1186\/s12916-019-1426-2"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.jdsr.2025.11.001","article-title":"Diagnostic accuracy and feasibility of artificial intelligence-driven smartphone imaging for dental caries detection: A systematic review","volume":"62","author":"Acosta","year":"2026","journal-title":"Jpn. Dent. Sci. Rev."},{"key":"ref_19","first-page":"140","article-title":"Automated dental caries detection system using deep learning and explainable AI","volume":"2022","author":"Bhattacharjee","year":"2022","journal-title":"AMIA Summits Transl. Sci. Proc."},{"key":"ref_20","doi-asserted-by":"crossref","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":"ref_21","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s13005-025-00496-8","article-title":"Accuracy of artificial intelligence in caries detection: A systematic review and meta-analysis","volume":"21","author":"Luke","year":"2025","journal-title":"Head Face Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"16807","DOI":"10.1038\/s41598-021-96368-7","article-title":"Deep learning for early dental caries detection in bitewing radiographs","volume":"11","author":"Lee","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_23","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. Proceedings of the International Workshop on Advances in Simplifying Medical Ultrasound, Lima, Peru, 4\u20138 October 2020, Springer.","DOI":"10.1007\/978-3-030-60334-2_23"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s44163-025-00391-w","article-title":"Application of artificial intelligence technologies for the detection of early childhood caries","volume":"5","author":"Sreekumar","year":"2025","journal-title":"Discov. Artif. Intell."},{"key":"ref_25","unstructured":"Dixit Chaudhary, S., Paygude, P., and Shah, P. (2026, February 08). Teeth or Dental Image Dataset. Available online: https:\/\/data.mendeley.com\/datasets\/6zsnhrds9t\/1."},{"key":"ref_26","unstructured":"American Academy of Pediatrics (2024). Oral Health Image Gallery, American Academy of Pediatrics."},{"key":"ref_27","unstructured":"Xue, P., Ge, J., Jiang, X., Song, S., Wu, Z., Huo, Y., Xie, W., Shen, L., Zhou, X., and Liu, X. (2025). Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking. arXiv."},{"key":"ref_28","unstructured":"Dental Cavity Detection Dataset (2026, January 02). Kaggle. Available online: https:\/\/www.kaggle.com\/datasets\/maazmakhdoom\/dental-cavity-detection-dataset."},{"key":"ref_29","unstructured":"Oral Detection Dataset (2026, January 02). Roboflow Universe (Roboflow, Inc.), 2024. Available online: https:\/\/universe.roboflow.com\/."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.-F. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_31","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201322). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Alangari, N., El Bachir Menai, M., Mathkour, H., and Almosallam, I. (2023). Exploring evaluation methods for interpretable machine learning: A survey. Information, 14.","DOI":"10.3390\/info14080469"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1038\/s42256-020-00257-z","article-title":"Shortcut learning in deep neural networks","volume":"2","author":"Geirhos","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zech, J.R., Badgeley, M.A., Liu, M., Costa, A.B., Titano, J.J., and Oermann, E.K. (2018). Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. 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