Performance Evaluation of Deep Learning Models for Dental Caries Classification via Panoramic Radiograph Images

Year 2024, Volume: 14 Issue: 4, 868 - 875, 29.12.2024

Omid Mırzaeı , Bülent Bilgehan , Mohamad Abduljalil , Mhammad Saleh , Ammar Kayssoun , Ahmet İlhan

https://doi.org/10.33808/clinexphealthsci.1466714

Abstract

Objective: The purpose of this study is to evaluate the ability of deep learning models to classify mandibular molar teeth according to the presence and proximity of caries to the dental pulp. This research summarizes the progress of artificial intelligence and potential dental problems in diagnosis, treatment, and disease prediction in medicine. It discusses data limitations, computational power, ethical considerations, and their implications for dentists. This can lay the groundwork for future research in this rapidly expanding field.
Methods: The dataset used in this study consists of 1200 panoramic radiographs, which have been evaluated and classified into three categories: free of dental caries, coded as (H); enamel-dentin caries lesions treated with restorative filling, coded as (R); and deep dental caries that underwent root canal treatment, coded as (E). The images are prepared for the training-testing process using the k-fold crossevaluation technique and then fed into the pre-trained deep learning models for classification.
Results: The VGG-19 model achieved superior results compared to the other models, with macro-average scores of 0.9111 for precision, 0.9127 for recall, and 0.9115 for f1-score, respectively.
Conclusion: The promising results obtained in this study give confidence in endorsing the use of deep learning models in the dental treatments sector.

Keywords

Artificial intelligence, deep learning, dental caries, panoramic radiology

References

• Featherstone JD. The science and practice of caries prevention. J Am Dent Assoc. 2000;131(7):887-899. DOI: 10.14219/jada.archive.2000.0307
• Amrollahi P, Shah B, Seifi A, Tayebi L. Recent advancements in regenerative dentistry: A review. Mater Sci Eng C Mater Biol Appl. 2016;69:1383-1390. DOI:10.1016/j.msec.2016.08.045.
• Beltrán-Aguilar ED, Barker LK, Canto MT, Dye BA, Gooch BF, Griffin SO, Hyman J, Jaramillo F, Kingman A, Nowjack-Raymer R, Selwitz RH, Wu T. Surveillance for dental caries, dental sealants, tooth retention, edentulism, and enamel fluorosis--United States, 1988-1994 and 1999-2002. MMWR Surveill Summ. 2005;54(3):1-43.
• Centers for Disease Control and Prevention, National Health and Nutrition Examination Survey (NHANES) 1999–2002, Accessed [20 November 2023]: http://www.cdc.gov/nchs/about/major/nhanes/datalink.htm
• Ali AH, Koller G, Foschi F, Andiappan M, Bruce KD, Banerjee A, Mannocci F. Self-limiting versus conventional caries removal: A randomized clinical trial. J Dent Res. 2018;97(11):1207-1213. DOI:10.1177/0022034518769255.
• Gomez J. Detection and diagnosis of the early caries lesion. BMC Oral Health. 2015; 15 Suppl 1 (Suppl 1): S3.
• Moutselos K, Berdouses E, Oulis C, Maglogiannis I. Recognizing occlusal caries in dental intraoral images using deep learning. Annu Int Conf IEEE Eng Med Biol Soc. 2019; 2019:1617-1620. DOI:10.1109/EMBC.2019.8856553.
• 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; 77:106-111. DOI: 10.1016/j.jdent.2018.07.015.
• Choi JW. Assessment of panoramic radiography as a national oral examination tool: Review of the literature. Imaging Sci Dent. 2011;41(1):1-6. DOI:10.5624/isd.2011.41.1.1.
• Dudhia R, Monsour PA, Savage NW, Wilson RJ. Accuracy of angular measurements and assessment of distortion in the mandibular third molar region on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2011;111(4):508-516. DOI: 10.1016/j.tripleo.2010.12.005.
• Tandon D, Rajawat J. Present and future of artificial intelligence in dentistry. J Oral Biol Craniofac Res. 2020;10(4):391-396. DOI: 10.1016/j.jobcr.2020.07.015.
• Haick H, Tang N. Artificial intelligence in medical sensors for clinical decisions. ACS Nano. 2021;15(3):3557-3567. DOI:10.1021/acsnano.1c00085.
• Raheja S, Kasturia S, Cheng X, Kumar M. Machine learning-based diffusion model for prediction of coronavirus-19 outbreak. Neural Comput Appl. 2023;35(19):13755-13774. DOI:10.1007/s00521-021-06376-x.
• Pang S, Fan M, Wang X, Wang J, Song T, Wang X, Cheng X. VGG16-T: A novel deep convolutional neural network with boosting to identify pathological type of lung cancer in early stage by CT images. Int J Comput Intell Syst. 2020;13(1):771-780. DOI:10.2991/ijcis.d.200608.001.
• Sadiq M, Shi D, Guo M, Cheng X. Facial landmark detection via attention-adaptive deep network. IEEE Access, 2019;7: 181041-181050. DOI:10.1109/ACCESS.2019.2955156.
• Chowdhary CL, Acharjya DP. Segmentation and feature extraction in medical imaging: A systematic review. Procedia Comput Sci. 2020;167:26-36. DOI:10.1016/j.procs.2020.03.179.
• Muhammad K, Khan S, Ser JD, Albuquerque VHC. Deep learning for multigrade brain tumor classification in smart healthcare systems: A prospective survey. IEEE Trans Neural Netw Learn Syst. 2021;32(2):507-522. DOI:10.1109/TNNLS.2020.2995800.
• Guo J, He H, He T, Lausen L, Li M, Lin H, Shi X, Wang C, Xie J, Zha S, Zhang Z, Zhang H, Zhang Z, Zhang Z, Zheng S, Zhu Y. GluonCV and GluonNLP: Deep learning in computer vision and natural language processing. J Mach Learn Res. 2020;21(23):1-7.
• Arias-Vergara T, Klumpp P, Vasquez-Correa JC, Noeth E, Orozco-Arroyave JR, Schuster M. Multi-channel spectrograms for speech processing applications using deep learning methods. Pattern Anal Applic. 2021;24(2):423-431. DOI:10.1007/s10044-020-00921-5.
• Kuschnerov M, Schaedler M, Bluemm C, Calabro S. Advances in deep learning for digital signal processing in coherent optical modems. In 2020 Optical Fiber Communications Conference and Exhibition (OFC) 2020;(pp. 1-3). IEEE.
• Işın A, Direkoğlu C, Şah M. Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Computer Science. 2016; 102: 317-324. DOI: 10.1016/j.procs.2016.09.407.
• Isin A, Ozdalili S. Cardiac arrhythmia detection using deep learning. Procedia Computer Science. 2017;120: 268-275. DOI: 10.1016/j.procs.2017.11.238.
• Kc K, Yin Z, Wu M, Wu Z. Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images. Signal Image Video Process. 2021;15(5):959-966. DOI:10.1007/s11760-020-01820-2.
• Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med. 2018; 100:270-278. DOI: 10.1016/j.compbiomed.2017.09.017.
• Schirrmeister RT, Springenberg JT, Fiederer LDJ, Glasstetter M, Eggensperger K, Tangermann M, Hutter F, Burgard W, Ball T. Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp. 2017;38(11):5391-5420. DOI:10.1002/hbm.23730.
• Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning. 2019; (pp. 6105-6114).
• Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Rabinovich A. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2015;(pp. 1-9).
• Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2016;(pp. 2818-2826). DOI:10.48550/arXiv.1512.00567.
• He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2016;(pp. 770-778). DOI:10.1109/CVPR.2016.90.
• Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations. ICLR. 2015:1-14. DOI:10.48550/arXiv.1409.1556.
• Sebring D, Kvist T, Buhlin K, Jonasson P; EndoReCo, Lund H. Calibration improves observer reliability in detecting periapical pathology on panoramic radiographs. Acta Odontol Scand. 2021;79(7):554-561. DOI:10.1080/00016357.2021.1910728.
• Mazurowski MA, Buda M, Saha A, Bashir MR. Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI. J Magn Reson Imaging. 2019;49(4):939-954. DOI:10.1002/jmri.26534.
• Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, Schwendicke F. Deep learning for the radiographic detection of apical lesions. J Endod. 2019;45(7):917-922.e5. DOI:10.1016/j.joen.2019.03.016.
• Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, Dörfer C, Schwendicke F. Deep learning for the radiographic detection of periodontal bone loss. Sci Rep. 2019;9(1):8495. DOI:10.1038/s41598-019-44839-3.
• Hiraiwa T, Ariji Y, Fukuda M, Kise Y, Nakata K, Katsumata A, Fujita H, Ariji E. A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofac Radiol. 2019;48(3):20180218. DOI:10.1259/dmfr.20180218.
• Lee JH, Jeong SN. Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: A pilot study. Medicine (Baltimore). 2020;99(26): e20787. DOI:10.1097/MD.0000000000020787.
• Lee JH, Kim DH, Jeong SN. Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network. Oral Dis. 2020;26(1):152-158. DOI:10.1111/odi.13223.
• Fukuda M, Inamoto K, Shibata N, Ariji Y, Yanashita Y, Kutsuna S, Nakata K, Katsumata A, Fujita H, Ariji E. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiol. 2020;36(4):337-343. DOI:10.1007/s11282-019-00409-x.
• Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, Özyürek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int Endod J. 2020;53(5):680-689. DOI: 10.1111/iej.13265.
• Thomas MF, Ricketts DN, Wilson RF. Occlusal caries diagnosis in molar teeth from bitewing and panoramic radiographs. Prim Dent Care. 2001;8(2):63-9. DOI: 10.1308/135576101322647908.
• Clark HC, Curzon ME. A prospective comparison between findings from a clinical examination and results of bitewing and panoramic radiographs for dental caries diagnosis in children. Eur J Paediatr Dent. 2004;5(4):203-209.
• Kamburoğlu K, Kolsuz E, Murat S, Yüksel S, Özen T. Proximal caries detection accuracy using intraoral bitewing radiography, extraoral bitewing radiography and panoramic radiography. Dentomaxillofac Radiol. 2012;41(6):450-459. DOI:10.1259/dmfr/30526171.