{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:46:06Z","timestamp":1775144766246,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T00:00:00Z","timestamp":1625443200000},"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-109-2410-H-197-002-MY3"],"award-info":[{"award-number":["MOST-109-2410-H-197-002-MY3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST-107-2218-E-131-002"],"award-info":[{"award-number":["MOST-107-2218-E-131-002"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST-107-2221-E-033-057"],"award-info":[{"award-number":["MOST-107-2221-E-033-057"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST-107-2622-E-131-007-CC3"],"award-info":[{"award-number":["MOST-107-2622-E-131-007-CC3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST-106-2622-E-033-014-CC2"],"award-info":[{"award-number":["MOST-106-2622-E-033-014-CC2"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST-106-2221-E-033-072"],"award-info":[{"award-number":["MOST-106-2221-E-033-072"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST-106-2119-M-033-001"],"award-info":[{"award-number":["MOST-106-2119-M-033-001"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 107-2112-M-131-001"],"award-info":[{"award-number":["MOST 107-2112-M-131-001"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early, the treatment will be relatively easy, which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However, the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu\u2019s threshold image enhancement technology, this research solves the problem that the original cutting technology cannot extract certain single teeth, and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN), which can identify caries and restorations from the bitewing images. Moreover, it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image, which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization, (2) a dental image cropping procedure to obtain individually separated tooth samples, and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks, namely, AlexNet, GoogleNet, Vgg19, and ResNet50, experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%, respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film.<\/jats:p>","DOI":"10.3390\/s21134613","type":"journal-article","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T02:59:47Z","timestamp":1625540387000},"page":"4613","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":75,"title":["Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs"],"prefix":"10.3390","volume":"21","author":[{"given":"Yi-Cheng","family":"Mao","sequence":"first","affiliation":[{"name":"Department of General Dentistry, Chang Gung Memorial Hospital, Taoyuan City 33305, Taiwan"}]},{"given":"Tsung-Yi","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan"}]},{"given":"He-Sheng","family":"Chou","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan"}]},{"given":"Szu-Yin","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Ilan University, Yilan City 26047, Taiwan"}]},{"given":"Sheng-Yu","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan"}]},{"given":"Yu-An","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan"}]},{"given":"Yu-Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7605-5214","authenticated-orcid":false,"given":"Chiung-An","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City 243303, Taiwan"}]},{"given":"Yen-Cheng","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of General Dentistry, Chang Gung Memorial Hospital, Taoyuan City 33305, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4079-9350","authenticated-orcid":false,"given":"Shih-Lun","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan"}]},{"given":"Chun-Wei","family":"Li","sequence":"additional","affiliation":[{"name":"Department of General Dentistry, Chang Gung Memorial Hospital, Taoyuan City 33305, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8848-6644","authenticated-orcid":false,"given":"Patricia Angela R.","family":"Abu","sequence":"additional","affiliation":[{"name":"Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City 1108, Philippines"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5158-0031","authenticated-orcid":false,"given":"Wei-Yuan","family":"Chiang","sequence":"additional","affiliation":[{"name":"National Synchrotron Radiation Research Center, Hsinchu City 30076, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1621","DOI":"10.1007\/s10103-020-03021-2","article-title":"Clinical performance of clinical-visual examination, digital bitewing radiography, laser fluorescence, and near-infrared light transillumination for detection of non-cavitated proximal enamel and dentin caries","volume":"35","author":"Kocak","year":"2020","journal-title":"Lasers Med. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1177\/0022034515586763","article-title":"Visual Inspection for Caries Detection: A Systematic Review and Meta-analysis","volume":"94","author":"Gimenez","year":"2015","journal-title":"J. Dent. Res."},{"key":"ref_3","unstructured":"Mallya, S., and Lam, E. (2018). White and Pharoah\u2019s Oral Radiology, Elsevier. [8th ed.]. Available online: https:\/\/www.elsevier.com\/books\/white-and-pharoahs-oral-radiology\/mallya\/978-0-323-54383-5."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/sj.bdj.4807325","article-title":"A reappraisal of the value of the bitewing radiograph in the diagnosis of posterior approximal caries","volume":"169","author":"Kidd","year":"1990","journal-title":"Br. Dent. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"34","DOI":"10.2341\/14-048-L","article-title":"Detection of Caries Around Amalgam Restorations on Approximal Surfaces","volume":"41","author":"Diniz","year":"2016","journal-title":"Oper. Dent."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xia, H., Wang, C., Yan, L., Dong, X., and Wang, Y. (2019, January 18\u201321). Machine Learning Based Medicine Distribution System. Proceedings of the 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Metz, France.","DOI":"10.1109\/IDAACS.2019.8924236"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1109\/JPROC.2015.2494198","article-title":"Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets","volume":"104","author":"Leung","year":"2016","journal-title":"Proc. IEEE"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Baig, M.M., Hua, N., Zhang, E., Robinson, R., Armstrong, D., Whittaker, R., and Ullah, E. (2019, January 23\u201327). Machine Learning-based Risk of Hospital Readmissions: Predicting Acute Readmissions within 30 Days of Discharge. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8856646"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, G., Teoh, J.Y.-C., and Choi, K.-S. (2018, January 18\u201321). Diagnosis of prostate cancer in a Chinese population by using machine learning methods. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8513365"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Khanagar, S.B., Al-Ehaideb, A., Maganur, P.C., Vishwanathaiah, S., Patil, S., Baeshen, H.A., and Bhandi, S. (2021). Developments, application, and performance of artificial intelligence in dentistry\u2014A systematic review. J. Dent. Sci., 16.","DOI":"10.1016\/j.jds.2020.06.019"},{"key":"ref_11","unstructured":"(2021, March 29). Diagnosis and Prediction of Periodontally Compromised Teeth Using a Deep Learning-Based Convolutional Neural Network Algorithm\u2014PubMed, Available online: https:\/\/pubmed.ncbi.nlm.nih.gov\/29770240\/."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"22","DOI":"10.4258\/hir.2018.24.1.22","article-title":"Bayesian-Based Decision Support System for Assessing the Needs for Orthodontic Treatment","volume":"24","author":"Thanathornwong","year":"2018","journal-title":"Healthc. Inform. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"20180218","DOI":"10.1259\/dmfr.20180218","article-title":"A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography","volume":"48","author":"Hiraiwa","year":"2019","journal-title":"Dentomaxillofac. Radiol."},{"key":"ref_14","first-page":"42","article-title":"An automated technique to stage lower third molar development on panoramic radiographs for age estimation: A pilot study","volume":"35","author":"Radesh","year":"2017","journal-title":"J. Forensic Odontostomatol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103425","DOI":"10.1016\/j.jdent.2020.103425","article-title":"Detecting caries lesions of different radiographic extension on bitewings using deep learning","volume":"100","author":"Cantu","year":"2020","journal-title":"J. Dent."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"SPrajapati, A., Nagaraj, R., and Mitra, S. (2017, January 11\u201314). Classification of dental diseases using CNN and transfer learning. Proceedings of the 2017 5th International Symposium on Computational and Business Intelligence (ISCBI), Dubai, United Arab Emirates.","DOI":"10.1109\/ISCBI.2017.8053547"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Aberin, S.T.A., and de Goma, J.C. (December, January 29). Detecting Periodontal Disease Using Convolutional Neural Networks. Proceedings of the 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Baguio City, Philippines.","DOI":"10.1109\/HNICEM.2018.8666389"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Singh, P., and Sehgal, P. (2017, January 3\u20135). Automated caries detection based on Radon transformation and DCT. Proceedings of the 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi, India.","DOI":"10.1109\/ICCCNT.2017.8204030"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/JBHI.2019.2919916","article-title":"A Smart Dental Health-IoT Platform Based on Intelligent Hardware, Deep Learning, and Mobile Terminal","volume":"24","author":"Liu","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Moran, M.B.H., Faria, M., Giraldi, G., Bastos, L., da Silva Inacio, B., and Conci, A. (2020, January 16\u201319). On using convolutional neural networks to classify periodontal bone destruction in periapical radiographs. Proceedings of the 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, Korea.","DOI":"10.1109\/BIBM49941.2020.9313501"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1016\/j.patcog.2004.12.010","article-title":"A system for human identification from X-ray dental radiographs","volume":"38","author":"Nomir","year":"2005","journal-title":"Pattern Recognit."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cui, J., and Zhang, M. (2008, January 21\u201324). Time-Domain versus frequency-domain approach for an accurate simulation of phased arrays. Proceedings of the 2008 International Conference on Microwave and Millimeter Wave Technology, Nanjing, China.","DOI":"10.1109\/ICMMT.2008.4540416"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"707","DOI":"10.13005\/bpj\/545","article-title":"Image Sharpening by Gaussian And Butterworth High Pass Filter","volume":"7","author":"Dogra","year":"2015","journal-title":"Biomed. Pharmacol. J."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Siddique, M.A.B., Arif, R.B., and Khan, M.M.R. (2018, January 27\u201328). Digital Image Segmentation in Matlab: A Brief Study on OTSU\u2019s Image Thresholding. Proceedings of the 2018 International Conference on Innovation in Engineering and Technology (ICIET), Dhaka, Bangladesh.","DOI":"10.1109\/CIET.2018.8660942"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Devi, M.P.A., Latha, T., and Sulochana, C.H. (2015, January 23\u201324). Iterative thresholding based image segmentation using 2D improved Otsu algorithm. Proceedings of the 2015 Global Conference on Communication Technologies (GCCT), Thuckalay, India.","DOI":"10.1109\/GCCT.2015.7342641"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kido, S., Hirano, Y., and Hashimoto, N. (2018, January 7\u20139). Detection and classification of lung abnormalities by use of convolutional neural network (CNN) and regions with CNN features (R-CNN). Proceedings of the 2018 International Workshop on Advanced Image Technology (IWAIT), Chiang Mai, Thailand.","DOI":"10.1109\/IWAIT.2018.8369798"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liawatimena, S., Heryadi, Y., Trisetyarso, A., Wibowo, A., Abbas, B.S., and Barlian, E. (2018, January 7\u20138). A Fish Classification on Images using Transfer Learning and Matlab. Proceedings of the 2018 Indonesian Association for Pattern Recognition International Conference (INAPR), Tangerang, Indonesia.","DOI":"10.1109\/INAPR.2018.8627007"},{"key":"ref_29","unstructured":"Oktay, A.B. (2017, January 12\u201314). Tooth detection with Convolutional Neural Networks. Proceedings of the 2017 Medical Technologies National Congress (TIPTEKNO), Trabzon, Turkey."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1166\/jmihi.2018.2354","article-title":"Teeth Detection Algorithm and Teeth Condition Classification Based on Convolutional Neural Networks for Dental Panoramic Radiographs","volume":"8","author":"Lin","year":"2018","journal-title":"J. Med. Imaging Health Inform."},{"key":"ref_31","first-page":"1","article-title":"A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films","volume":"9","author":"Chen","year":"2019","journal-title":"Sci. Rep."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4613\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:26:22Z","timestamp":1760163982000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4613"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,5]]},"references-count":31,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21134613"],"URL":"https:\/\/doi.org\/10.3390\/s21134613","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,5]]}}}