Periodontal Disease Detection with Machine Learning Technology From Radiographic Images : An Interdisciplinary Study Of Dentistry and Computer Science
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Published:
Oct 31, 2024
Keywords:
Periodontal, Machine Learning, Convolutional, Neural Network, Image Classification, Toot Detection, Disease Identification
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Abstract
This study aims to evaluate the effectiveness of the Convolutional Neural Network (CNN) model in identifying periodontal disease using dental images. With the applied method, the CNN model was trained using a dataset consisting of 40 dental images and tested on 55 images to evaluate its ability to classify the images as healthy or periodontal. The evaluation results showed that the CNN model achieved an overall accuracy of 91.16%. The model precision for healthy images reached 92.39%, while the precision for unhealthy images was 91.05%. Recall sensitivity for healthy images is 91.16%, and for F1-Score images is 91.07%. The data shows that the model has better performance in identifying healthy images compared to periodontal images. To improve the performance of the model, data augmentation techniques such as rotation, flipping, and scaling were applied, which gave a slight improvement to the results. However, the limited size of the dataset seems to be an obstacle in achieving higher accuracy. Therefore, this study recommends expanding the dataset size and applying more complex model architectures or transfer learning techniques to improve detection performance. The conclusion of this study shows that CNN models have potential for periodontal disease detection, but need further development to improve accuracy and reliability. This research contributes to the development of medical detection technology and opens a path for further research in improving periodontal disease detection systems using CNN technology.
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[1]
FadhillahY., Ade Ismail Abdul Kodir, and Muhammad Noor Hasan Siregar, “Periodontal Disease Detection with Machine Learning Technology From Radiographic Images : An Interdisciplinary Study Of Dentistry and Computer Science”, chipset, vol. 5, no. 02, pp. 76-83, Oct. 2024.
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References
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[2] S. Vishwanathaiah, H. N. Fageeh, S. B. Khanagar, and P. C. Maganur, "Artificial Intelligence Its Uses and Application in Pediatric Dentistry: A Review," Biomedicines, vol. 11, no. 3, pp. 1-19, 2023, doi: 10.3390/biomedicines11030788.
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[12] I. Nihayatul Husna, M. Ulum, A. Kurniawan Saputro, D. Tri Laksono, and D. Neipa Purnamasari, "Design of a Detection System and Calculation of the Number of People Using the Convolutional Neural Network (CNN) Method," Semin. National. Fortei Reg., vol. 7, pp. 1-6, 2022.
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[15] T. Hu, M. Khishe, M. Mohammadi, G. R. Parvizi, S. H. Taher Karim, and T. A. Rashid, "Real-time COVID-19 diagnosis from X-Ray images using deep CNN and extreme learning machines stabilized by chimp optimization algorithm," Biomed. Signal Process. Control, vol. 68, no. April, 2021, doi: 10.1016/j.bspc.2021.102764.
[16] C. C. Chen et al., "Automatic recognition of teeth and periodontal bone loss measurement in digital radiographs using deep-learning artificial intelligence," J. Dent. Sci., vol. 18, no. 3, pp. 1301-1309, 2023, doi: 10.1016/j.jds.2023.03.020.
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[18] [J. Lim et al., "Machine learning-based evaluation of application value of traditional Chinese medicine clinical index and pulse wave parameters in the diagnosis of polycystic ovary syndrome," Eur . J. Integr. Med., vol. 64, pp. 102311, 2023, doi: 10.1016/j.eujim.2023.102311.
[19] Y. Wang et al., "Application of machine learning for composite molding process modeling," Compos. Commun., vol. 48, no. March, 2024, doi: 10.1016/j.coco.2024.101960.
[20] R. Bouali, O. Mahboub, and M. Lazaar, "Review of Dental Diagnosis by Deep Learning Models: Trends, Applications and Challenges," Procedia Comput. Sci., vol. 231, no. 2023, pp. 221-228, 2024, doi: 10.1016/j.procs.2023.12.196.
[21] C. Kumar and B. Singh, "A Comparative Study of Machine Learning Regression Approach on Dental Caries Detection," Procedia Comput. Sci., vol. 215, no. 2022, pp. 519-528, 2022, doi: 10.1016/j.procs.2022.12.054.
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[23] A. Jinan, B. H. Hayadi, and U. P. Utama, "Classification of Rice Plant Diseases Using Convolutional Neural Network Method Through Leaf Image (Multilayer Perceptron)," J. Comput. Eng. Sci., vol. 1, no. 2, pp. 37-44, 2022.
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[27] [27]R. A. Zuama, S. Rahmatullah, and Y. Yuliani, "Performance Analysis of Machine Learning Algorithms on Prediction of Cerebrovascular Accidents," J. Media Inform. Budidarma, vol. 6, no. 1, pp. 531, 2022, doi: 10.30865/mib.v6i1.3488.
[28] T. Jain, "The New Periodontal disease classification: Analysis and Review," Univ. J. Dent. Sci., vol. 7, no. 3, 2021, doi: 10.21276/ujds.2021.7.3.23.
[29] [J. Electronics and D. Computer, "Application of Serverless Computing in Detecting Oral Diseases with the Cnn Method," vol. 16, no. 2, pp. 230-238, 2023, [Online]. Available at: https://journal.stekom.ac.id/index.php/elkom.
[30] B. Yanto, L. Fimawahib, A. Supriyanto, B. H. Hayadi, and R. R. Pratama, "Classification of Sweet Orange Fruit Maturity Texture Based on Color Brightness Level with Deep Learning Convolutional Neural Network Method," INOVTEK Polbeng - Inform. Series, vol. 6, no. 2, pp. 259, 2021, doi: 10.35314/isi.v6i2.2104.
[31] B. Tokede et al., "Translating periodontal data to knowledge in a learning health system," J. Am. Dent. Assoc., vol. 153, no. 10, pp. 996-1004, 2022, doi: 10.1016/j.adaj.2022.06.007.
[32] G. Dharmarathne, M. Bogahawaththa, M. McAfee, U. Rathnayake, and D. P. P. Meddage, "On the diagnosis of chronic kidney disease using a machine learning-based interface with explainable artificial intelligence," Intell. Syst. with Appl., vol. 22, no. May, pp. 200397, 2024, doi: 10.1016/j.iswa.2024.200397.
[33] R. Valenzuela-Narváez and D. Valenzuela-Narváez, "Interaction of periodontal clinical indicators in metabolic syndrome and nonalcoholic fatty liver disease: Implications for preventive interventions," Saudi Dent. J., no. xxxx, 2024, doi: 10.1016/j.sdentj.2024.04.013.
[34] T. H. Farook, N. Bin Jamayet, J. Y. Abdullah, and M. K. Alam, "Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review," Pain Res. Manag., vol. 2021, pp. 1-9, 2021, doi: 10.1155/2021/6659133.
[35] C. Congestion and I. O. F. Ginger, "Journal of Clinical Otorhinolaryngology, Head, and Neck Surgery," J. Clin. Otorhinolaryngol. Head, Neck Surg., no. i, pp. 1281-1289, 2023.
[36] N. W. A. Sardi, N. L. P. S. M. Adnyasari, and M. T. S. Pinatih, "Periodontics in Artificial Intelligence Era: a Literature Review," Interdental J. Kedokt. Gigi, vol. 19, no. 2, pp. 80-85, 2023, doi: 10.46862/interdental.v19i2.7859.
[37] [Erlin, Y. N. Marlim, Junadhi, L. Suryati, and N. Agustina, "Early Detection of Diabetes Using Machine Learning with Logistic Regression Algorithm," J. Nas. Tech. Electro and Technol. Inf., vol. 11, no. 2, pp. 88-96, 2022.