{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T04:15:18Z","timestamp":1789791318096,"version":"4.0.1"},"reference-count":32,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 110-2221-E-259-010-MY2"],"award-info":[{"award-number":["MOST 110-2221-E-259-010-MY2"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Dental X-ray images are important and useful for dentists to diagnose dental diseases. Utilizing deep learning in dental X-ray images can help dentists quickly and accurately identify common dental diseases such as periodontitis and dental caries. This paper applies image processing and deep learning technologies to dental X-ray images to propose a simultaneous recognition method for periodontitis and dental caries. The single-tooth X-ray image is detected by the YOLOv7 object detection technique and cropped from the periapical X-ray image. Then, it is processed through contrast-limited adaptive histogram equalization to enhance the local contrast, and bilateral filtering to eliminate noise while preserving the edge. The deep learning architecture for classification comprises a pre-trained EfficientNet-B0 and fully connected layers that output two labels by the sigmoid activation function for the classification task. The average precision of tooth detection using YOLOv7 is 97.1%. For the recognition of periodontitis, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve is 98.67%, and the AUC of the precision-recall (PR) curve is 98.38%. For the recognition of dental caries, the AUC of the ROC curve is 98.31%, and the AUC of the PR curve is 97.55%. Different from the conventional deep learning-based methods for a single disease such as periodontitis or dental caries, the proposed approach can provide the recognition of both periodontitis and dental caries simultaneously. This recognition method presents good performance in the identification of periodontitis and dental caries, thus facilitating dental diagnosis.<\/jats:p>","DOI":"10.3390\/bioengineering10080911","type":"journal-article","created":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T09:32:35Z","timestamp":1690882355000},"page":"911","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["Deep Learning-Based Recognition of Periodontitis and Dental Caries in Dental X-ray Images"],"prefix":"10.3390","volume":"10","author":[{"given":"Ivane Delos Santos","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, National Dong Hwa University, Hualien 97401, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6614-4635","authenticated-orcid":false,"given":"Chieh-Ming","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Dong Hwa University, Hualien 97401, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mei-Juan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Dong Hwa University, Hualien 97401, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming-Chin","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Dong Hwa University, Hualien 97401, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ro-Min","family":"Weng","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Dong Hwa University, Hualien 97401, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chia-Hung","family":"Yeh","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Taiwan Normal University, Taipei 10610, Taiwan"},{"name":"Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1109\/JBHI.2020.2991043","article-title":"AI in medical imaging informatics: Current challenges and future directions","volume":"24","author":"Panayides","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.2186\/jpr.JPR_D_20_00139","article-title":"Application of artificial intelligence in the dental field: A literature review","volume":"66","author":"Kishimoto","year":"2022","journal-title":"J. Prosthodont. Res."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Suhail, Y., Upadhyay, M., Chhibber, A. (2020). Machine learning for the diagnosis of orthodontic extractions: A computational analysis using ensemble learning. Bioengineering, 7.","DOI":"10.3390\/bioengineering7020055"},{"key":"ref_4","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_5","doi-asserted-by":"crossref","unstructured":"Rao, R.S., Shivanna, D.B., Lakshminarayana, S., Mahadevpur, K.S., Alhazmi, Y.A., Bakri, M.M.H., Alharbi, H.S., Alzahrani, K.J., Alsharif, K.F., and Banjer, H.J. (2022). Ensemble deep-learning-based prognostic and prediction for recurrence of sporadic odontogenic keratocysts on hematoxylin and eosin stained pathological images of incisional biopsies. J. Pers. Med., 12.","DOI":"10.3390\/jpm12081220"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s11282-018-0363-7","article-title":"Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography","volume":"35","author":"Murata","year":"2019","journal-title":"Oral Radiol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Celik, M.E. (2022). Deep learning based detection tool for impacted mandibular third molar teeth. Diagnostics, 12.","DOI":"10.3390\/diagnostics12040942"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chuo, Y., Lin, W.M., Chen, T.Y., Chan, M.L., Chang, Y.S., Lin, Y.R., Lin, Y.J., Shao, Y.H., Chen, C.A., and Chen, S.L. (2022). A high-accuracy detection system: Based on transfer learning for apical lesions on periapical radiograph. Bioengineering, 9.","DOI":"10.3390\/bioengineering9120777"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, Y.C., Chen, M.Y., Chen, T.Y., Chan, M.L., Huang, Y.Y., Liu, Y.L., Lee, P.T., Lin, G.J., Li, T.F., and Chen, C.A. (2023). Improving dental implant outcomes: CNN-based system accurately measures degree of peri-implantitis damage on periapical film. Bioengineering, 10.","DOI":"10.3390\/bioengineering10060640"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1111\/prd.12249","article-title":"A review of the influence of periodontal treatment in systemic diseases","volume":"79","author":"Falcao","year":"2019","journal-title":"Periodontol. 2000"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1177\/0022034515622198","article-title":"London charter on oral health inequalities","volume":"95","author":"Watt","year":"2016","journal-title":"J. Dent. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"114","DOI":"10.5051\/jpis.2018.48.2.114","article-title":"Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm","volume":"48","author":"Lee","year":"2018","journal-title":"J. Periodontal Implant Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"655556","DOI":"10.3389\/fphys.2021.655556","article-title":"An interpretable computer-aided diagnosis method for periodontitis from panoramic radiographs","volume":"12","author":"Li","year":"2021","journal-title":"Front. Physiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6629","DOI":"10.1007\/s00784-022-04617-4","article-title":"Application of deep machine learning for the radiographic diagnosis of periodontitis","volume":"26","author":"Chang","year":"2022","journal-title":"Clin. Oral Investig."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Sornam, M., and Prabhakaran, M. (2017, January 21\u201322). A new linear adaptive swarm intelligence approach using back propagation neural network for dental caries classification. Proceedings of the 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, Chennai, India.","DOI":"10.1109\/ICPCSI.2017.8392208"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1007\/s13755-019-0096-y","article-title":"Dental caries diagnosis in digital radiographs using back-propagation neural network","volume":"8","author":"Geetha","year":"2020","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jusman, Y., Anam, M.K., Puspita, S., and Saleh, E. (2021, January 18\u201319). Machine learnings of dental caries images based on Hu moment invariants features. Proceedings of the 2021 International Seminar on Application for Technology of Information and Communication, Semarangin, Indonesia.","DOI":"10.1109\/iSemantic52711.2021.9573208"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"18320","DOI":"10.1109\/ACCESS.2022.3150358","article-title":"Dental caries detection using score-based multi-input deep convolutional neural network","volume":"10","author":"Imak","year":"2022","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bui, T.H., Hamamoto, K., and Paing, M.P. (2022). Automated caries screening using ensemble deep learning on panoramic radiographs. Entropy, 24.","DOI":"10.3390\/e24101358"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1007\/s11548-021-02319-y","article-title":"Dental disease detection on periapical radiographs based on deep convolutional neural networks","volume":"16","author":"Chen","year":"2021","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"104107","DOI":"10.1016\/j.jdent.2022.104107","article-title":"Artificial intelligence or caries and periapical periodontitis detection","volume":"122","author":"Li","year":"2022","journal-title":"J. Dent."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 18\u201322). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1186\/s40537-021-00434-w","article-title":"Comparative analysis of deep learning image detection algorithms","volume":"8","author":"Srivastava","year":"2021","journal-title":"J. Big Data"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/S0734-189X(87)80186-X","article-title":"Adaptive histogram equalization and its variations","volume":"39","author":"Pizer","year":"1987","journal-title":"Comput. Vis. Graph. Image Process."},{"key":"ref_26","unstructured":"Pizer, S.M., Johnston, R.E., Ericksen, J.P., Yankaskas, B.C., and Muller, K.E. (1990, January 22\u201325). Contrast-limited adaptive histogram equalization: Speed and effectiveness. Proceedings of the First Conference on Visualization in Biomedical Computing, Atlanta, GA, USA."},{"key":"ref_27","unstructured":"Tomasi, C., and Manduchi, R. (1998, January 4\u20137). Bilateral filtering for gray and color images. Proceedings of the Sixth International Conference on Computer Vision, Bombay, India."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Pandey, P., Bhan, A., Dutta, M.K., and Travieso, C.M. (2017, January 10\u201312). Automatic image processing based dental image analysis using automatic gaussian fitting energy and level sets. Proceedings of the 2017 International Conference and Workshop on Bioinspired Intelligence, Funchal, Portugal.","DOI":"10.1109\/IWOBI.2017.7985529"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_31","unstructured":"Tan, M., and Le, Q.V. (2019, January 10\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the 36th Interna-tional Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_32","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 2017 IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/10\/8\/911\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:23:50Z","timestamp":1760127830000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/10\/8\/911"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,1]]},"references-count":32,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["bioengineering10080911"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering10080911","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,1]]}}}