Artificial intelligence based on Convolutional Neural Network for detecting dental caries on bitewing and periapical radiographs
Abstract
Objectives: This narrative review is written to describe the accuracy of caries detection and find out the clinical implications and future prospects of using Convolutional Neural Network (CNN) to determine radio-diagnosis of dental caries in bitewing and periapical radiographs.Review: The databases used for literature searching in this narrative review were PubMed, Google Scholar, and Science Direct. The inclusion criteria were original article, case report, and textbook written in English and Bahasa Indonesia, published within 2011-2021. The exclusion criteria were articles that the full text could not be accessed, research article that did not provide the methods used, and duplication articles. In this narrative review, a total of 33 literatures consisting of 30 articles and three textbooks reviewed, including four original articles on CNN for caries detection.
Conclusion: Results of the review reveal that GoogLeNet produces the best detection compared to Fully Convolutional Network (FCN) and U-Net for caries detection in bitewing and periapical radiographs. Nonetheless, the positive predictive value (PPV), recall, negative predictive value (NPV), specificity, F1-score, and accuracy values in these architectures indicate good performance. The differences of each CNN’s performances to detect caries are determined by the number of trained datasets, the architecture’s layers, and the complexity of the CNN architectures. The conclusion of this review is CNN can be used as an alternative to detect caries, increasing the diagnostic accuracy and time efficiency as well as preventing errors due to dentist fatigue. Yet the CNN is not able to substitute the expertise of a radiologist. Therefore, it is need to be revalidated by the radiologist to avoid diagnostic errors.
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Azwary F, Indriani F, Nugrahadi DT. Question Answering System Berbasis Artificial Intelligence Markup Language. Kumpul J Ilmu Komput. 2016;04(01):48–60.
Bui TH, Hamamoto K, Paing MP. Deep Fusion Feature Extraction for Caries Detection on Dental Panoramic Radiographs. Appl Sci. 2021;11(5):2005.
Indolia S, Goswami AK, Mishra SP, Asopa P. Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach. Procedia Comput Sci [Internet]. 2018;132:679–88.
Putra WSE. Klasifikasi Citra Menggunakan Convolutional Neural Network (Cnn) Pada Caltech 101. J Tek ITS [Internet]. 2016;5(1):76.
Peryanto A, Yudhana A, Umar R. Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation. J Appl Informatics Comput. 2020;4(1):45–51.
Ariji Y, Yanashita Y, Kutsuna S, Muramatsu C, Fukuda M, Kise Y, Nozawa M, Kuwada C, Fujita H, Katsumata A, Ariji E. Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique. Oral Surg Oral Med Oral Pathol Oral Radiol. 2019 Oct;128(4):424-30.
Kim D, Choi E, Jeong HG, Chang J, Youm S. Expert system for mandibular condyle detection and osteoarthritis classification in panoramic imaging using r-cnn and cnn. Appl Sci. 2020;10(21):1–10.
Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi JG, Gaudin R, et al. Detecting caries lesions of different radiographic extension on bitewings using deep learning. J Dent [Internet]. 2020;100(July):103425.
Dianawati N, Setyarini W, Widjiastuti I, Ridwan RD, Kuntaman K. The distribution of Streptococcus mutans and Streptococcus sobrinus in children with dental caries severity level. Dent J (Majalah Kedokt Gigi). 2020;53(1):36.
Bukhari OM. Dental Caries Experience and Oral Health Related Quality of Life in Working Adults. Saudi Dent J [Internet]. 2020;32(8):382–9.
Manton DJ. Diagnosis of the early carious lesion. Aust Dent J. 2013;58(SUPPL.1):35–9.
Abdinian M, Razavi SM, Faghihian R, Samety AA, Faghihian E. Accuracy of Digital Bitewing Radiography versus Different Views of Digital Panoramic Radiography for Detection of Proximal Caries. J Dent (Tehran) [Internet]. 2015;12(4):290–7.
Takahashi N, Lee C, Da Silva JD, Ohyama H, Roppongi M, Kihara H, Hatakeyama W, Ishikawa-Nagai S, Izumisawa M. A comparison of diagnosis of early stage interproximal caries with bitewing radiographs and periapical images using consensus reference. Dentomaxillofac Radiol. 2019 Feb;48(2):20170450.
White SC, Pharoah MJ. Oral radiography: Principles and Interpretation. 7th ed. St.Louis: Elsevier; 2014.
Srivastava MM, Kumar P, Pradhan L, Varadarajan S. Detection of Tooth caries in Bitewing Radiographs using Deep Learning. 2017 Nov 20;
Lee S, Oh S il, Jo J, Kang S, Shin Y, Park J won. Deep learning for early dental caries detection in bitewing radiographs. Sci Rep. 2021;11(1).
Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent. 2018 Oct 1;77:106–11.
Long J, Shelhamer E., Darrell T. Fully Convolutional Networks for Semantic Segmentation. In IEEE Transactions on Patttern Analysis and Machine Intelligence. 2017:39(4);640-651.
Gadosey PK, Li Y, Adjei Agyekum E, Zhang T, Liu Z, Yamak PT, Essaf F. SD-UNet: Stripping Down U-Net for Segmentation of Biomedical Images on Platforms with Low Computational Budgets. Diagnostics (Basel). 2020 Feb 18;10(2):110.
Suta IBLM, Sudarma M, Satya Kumara IN. Segmentasi Tumor Otak Berdasarkan Citra Magnetic Resonance Imaging Dengan Menggunakan Metode U-NET. Maj Ilm Teknol Elektro. 2020;19(2):151.
Jahandad, Sam SM, Kamardin K, Amir Sjarif NN, Mohamed N. Offline signature verification using deep learning convolutional Neural network (CNN) architectures GoogLeNet inception-v1 and inception-v3. Procedia Comput Sci [Internet]. 2019;161:475–83.
Loh TP, Lord SJ, Bell K, Bohn MK, Lim CY, Markus C, Fares Taie H, Adeli K, Lippi G, Sandberg S, Horvath A. Setting minimum clinical performance specifications for tests based on disease prevalence and minimum acceptable positive and negative predictive values: Practical considerations applied to COVID-19 testing. Clin Biochem. 2021 Feb;88:18-22.
El-Ela WHA, Farid MM, El-Din Mostafa MS. Intraoral versus extraoral bitewing radiography in detection of enamel proximal caries: An ex vivo study. Dentomaxillofacial Radiol. 2016;45(4):20150326.
Chan M, Dadul T, Langlais R, Russell D, Ahmad M. Accuracy of extraoral bite-wing radiography in detecting proximal caries and crestal bone loss. J Am Dent Assoc [Internet]. 2018;149(1):51–8.
DeVries Z, Locke E, Hoda M, Moravek D, Phan K, Stratton A, et al. Using a national surgical database to predict complications following posterior lumbar surgery and comparing the area under the curve and F1-score for the assessment of prognostic capability. Spine J [Internet]. 2021;21(7):1135–42.
Hulten G. Building Intelligent Systems. Building Intelligent Systems. New York: Apress; 2018.
Singh J, Dhiman G. A survey on machine-learning approaches: Theory and their concepts. Mater Today Proc [Internet]. 2021;(xxxx):1-7.
Fadlia N, Kosasih R. Klasifikasi Jenis Kendaraan Menggunakan Metode Convolutional Neural Network (Cnn). J Ilm Teknol dan Rekayasa. 2019;24(3):207–15.
Whaites E, Drage N. Essentials of Dental Radiography and Radiology E-Book. 5th ed. Churcill Livingstone: Elsevier Health Sciences; 2013.
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2015;07-12-June(June):1–9.
Zheng W, Zhang X, Kim JJ, Zhu X, Ye G, Ye B, et al. High Accuracy of Convolutional Neural Network for Evaluation of Helicobacter pylori Infection Based on Endoscopic Images: Preliminary Experience. Clin Transl Gastroenterol. 2019;10(12):e00109.
Müller A, Mertens SM, Göstemeyer G, Krois J, Schwendicke F. Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative Study. J Clin Med. 2021 Apr 10;10(8):1612.
Published
2022-08-31
How to Cite
ROOSANTY, Amelia; WIDYANINGRUM, Rini; DIBA, Silviana Farrah.
Artificial intelligence based on Convolutional Neural Network for detecting dental caries on bitewing and periapical radiographs.
Jurnal Radiologi Dentomaksilofasial Indonesia (JRDI), [S.l.], v. 6, n. 2, p. 89-94, aug. 2022.
ISSN 2686-1321.
Available at: <http://jurnal.pdgi.or.id/index.php/jrdi/article/view/867>. Date accessed: 23 nov. 2024.
doi: https://doi.org/10.32793/jrdi.v6i2.867.
Section
Review Article
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
