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Open Access
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Tej Trivedi, Tanish Shah, Akhilesh Soni, Dr. Madhura Phadke,
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- Student, Department of Computer Engineering,, Student, Department of Computer Engineering,, Student, Department of Computer Engineering,, Assistant Professor, Department of Computer Engineering, K. J. Somaiya Institute of Engineering,, K. J. Somaiya Institute of Engineering,, K. J. Somaiya Institute of Engineering,, K. J. Somaiya Institute of Engineering, Sion, Mumbai, Maharashtra,, Sion, Mumbai, Maharashtra,, Sion, Mumbai, Maharashtra,, Sion, Mumbai, Maharashtra, India., India, India, India
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Abstract
nDental diseases, also known as oral diseases or dental conditions, encompass a range of health problems affecting the teeth, gums, mouth, and associated structures. These conditions can lead to pain, discomfort, and severe complications if left untreated. Early detection and accurate diagnosis are crucial for effective treatment and prevention of further complications. This comprehensive literature review aims to identify common dental problems such as Tooth Decay (Cavities), Gingivitis, Periodontitis, and other oral diseases using advanced machine learning and deep learning approaches in dental image analysis. Machine learning and deep learning techniques provide significant assistance to dentists in making informed decisions during the treatment process. X-rays and radiographic images are the primary tools used for detecting dental diseases, and advancements in imaging technologies have enhanced the quality and precision of these diagnostics. Machine learning algorithms, such as Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), and Region-based Convolutional Neural Networks (R-CNNs), have shown remarkable efficacy in automating the diagnostic process, reducing the reliance on manual interpretation, and minimizing human error. Deep learning, in particular, offers substantial promise for dental disease detection and tooth identification. Techniques such as transfer learning have enabled models to achieve high accuracy even with limited datasets, enhancing the potential for early and accurate diagnosis. The application of CNNs allows for the effective analysis of complex image data, facilitating the identification of minute details that may indicate the onset of dental conditions. GNNs and R-CNNs further contribute by enabling the analysis of relational data and improving the detection of abnormalities within dental structures. The evolution of artificial intelligence in dental healthcare signifies a transformative shift towards early disease detection, personalized treatment planning, and improved patient outcomes. This survey explores the current technological advancements in AI-driven dental diagnostics, highlighting the integration of these cutting-edge tools into routine clinical practice. By leveraging the power of AI, dental professionals can enhance diagnostic accuracy, streamline treatment processes, and ultimately provide better care to patients.
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Keywords: Dental Diseases, Panoramic Images, CNN Algorithm, Faster R-CNN Algorithm, GNN Algorithm, Transfer Learning, YOLO (You Only Look Once).
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References
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- Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Journal of dentistry. 2018 Oct 1;77:106-11.
- Joo J, Jeong S, Jin H, Lee U, Yoon JY, Kim SC. Periodontal disease detection using convolutional neural networks. In2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) 2019 Feb 11 (pp. 360-362). IEEE.
- Thumati SM, Dhanya K, Sathish H, Madan KS, Rani S. A comparative study on the working of gnn and cnn on panoramic x-rays in prediction of dental diseases. In2023 8th International Conference on Communication and Electronics Systems (ICCES) 2023 Jun 1 (pp. 755-762). IEEE.
- Thulaseedharan A, PS LP. Deep learning-based object detection algorithm for the detection of dental diseases and differential treatments. In2022 IEEE 19th India Council International Conference (INDICON) 2022 Nov 24 (pp. 1-7). IEEE.
- Oka K, Ali A, Fujita D, Kobashi S. Tooth recognition in X-ray dental panoramic images with prosthetic detection. In2022 International Conference on Machine Learning and Cybernetics (ICMLC) 2022 Sep 9 (pp. 109-114). IEEE.
- Muresan MP, Barbura AR, Nedevschi S. Teeth detection and dental problem classification in panoramic X-ray images using deep learning and image processing techniques. In2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP) 2020 Sep 3 (pp. 457-463). IEEE.
- Jader G, Fontineli J, Ruiz M, Abdalla K, Pithon M, Oliveira L. Deep instance segmentation of teeth in panoramic X-ray images. In2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) 2018 Oct 29 (pp. 400-407). IEEE.
- Mahdi FP, Yagi N, Kobashi S. Automatic teeth recognition in dental X-ray images using transfer learning based faster R-CNN. In2020 IEEE 50th International Symposium on Multiple-Valued Logic (ISMVL) 2020 Nov 9 (pp. 16-21). IEEE.
- Lakshmi MM, Chitra P. Classification of Dental Cavities from X-ray images using Deep CNN algorithm. In2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) 2020 Jun 15 (pp. 774-779). IEEE.
- Lakshmi MM, Chitra P. Tooth decay prediction and classification from X-ray images using deep CNN. In2020 International Conference on Communication and Signal Processing (ICCSP) 2020 Jul 28 (pp. 1349-1355). IEEE.
- Singh K, Abrol M. To Use a Quick R-CNN Algorithm—An Automated Strategy for Tooth Diagnostics. In2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART) 2022 Dec 16 (pp. 1160-1164). IEEE.
- Divakaran S, Vasanth K, Suja D, Swedha V. Classification of Digital Dental X-ray images using machine learning. In2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII) 2021 Mar 25 (pp. 1-3). IEEE.
- Das P, Thimmaraju MK, Ahalya N, Anandaram H, Kumar A. Design and Comparison of Transfer Learning for Dental Caries Detection. In2022 International Conference on Edge Computing and Applications (ICECAA) 2022 Oct 13 (pp. 1311-1316). IEEE.
- Prados-Privado M, García Villalón J, Blázquez Torres A, Martínez-Martínez CH, Ivorra C. A convolutional neural network for automatic tooth numbering in panoramic images. BioMed Research International. 2021;2021(1):3625386.
- Bodhe R, Sivakumar S, Raghuwanshi A. Design and development of deep learning approach for dental implant planning. In2022 International Conference on Green Energy, Computing and Sustainable Technology (GECOST) 2022 Oct 26 (pp. 269-274). IEEE.
- RATHI, M., & RATTAN, D. S . “Panoramic dental X-ray dataset. Kaggle: Your Machine Learning and Data Science Community”.
- Tufts dental database. Kaggle K. Your machine learning and data science community.
- Abdi A, Kasaei S. Panoramic dental X-rays with segmented mandibles. Mendeley Data. 2020;2.
- Prajapati SA, Nagaraj R, Mitra S. Classification of dental diseases using CNN and transfer learning. In2017 5th International Symposium on Computational and Business Intelligence (ISCBI) 2017 Aug 11 (pp. 70-74). IEEE.
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| Volume | ||
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | ||
| Received | August 2, 2024 | |
| Accepted | August 8, 2024 | |
| Published | August 20, 2024 |
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