ABSTRACT
Dental x-rays have been a standard piece of dental equipment for many years and are an indispensable diagnostic tool for dentists to detect tooth damage or disease. Recent research has focused on employing computer vision algorithms to automate analysis of dental x-rays. Our study aims to review the work done using transfer learning in dental image processing. AI solutions for dental images have been developed for many purposes, including examining tooth cavities (caries) and restorations and abnormalities in the maxillary sinuses. They have also been used to classify dental implants and determine gender in forensic studies. Transfer Learning is a new approach that is being used to solve a problem that classic deep learning and machine learning techniques could not solve: that of data limitation. Our search has investigated 80 research papers, of which 30 were relevant and analyzed in this paper. The identified studies have discussed a variety of transfer learning models to process different types of x-rays and have reported their efficacy using a variety of metrics. Transfer learning was used to solve various problems depending on the research question. Some papers compared the performance of transfer learning with that of dental experts in analyzing x-ray images, the accuracy of which were surprisingly close to equal. Although the results of the majority of dental applications performed using transfer learning models are encouraging, future research will need to solve the shortcomings highlighted in the present review.
- A. Haghanifar, M. M. Majdabadi, and S.-B. Ko, “PaXNet: Dental Caries Detection in Panoramic X-ray using Ensemble Transfer Learning and Capsule Classifier,” Dec. 2020, [Online]. Available: https://radiopaedia.orgGoogle Scholar
- Y.-C. Mao et al., “Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs,” Sensors , vol. 21, no. 13. 2021. doi: 10.3390/s21134613.Google Scholar
- U. Rashid , “A hybrid mask RCNN-based tool to localize dental cavities from real-time mixed photographic images,” PeerJ Computer Science, vol. 8, p. e888, 2022, doi: 10.7717/peerj-cs.888.Google ScholarCross Ref
- M. Mori , “A deep transfer learning approach for the detection and diagnosis of maxillary sinusitis on panoramic radiographs,” Odontology, vol. 109, no. 4, pp. 941–948, 2021, doi: 10.1007/s10266-021-00615-2.Google ScholarCross Ref
- J.-E. Kim, N.-E. Nam, J.-S. Shim, Y.-H. Jung, B.-H. Cho, and J. J. Hwang, “Transfer Learning via Deep Neural Networks for Implant Fixture System Classification Using Periapical Radiographs,” Journal of Clinical Medicine , vol. 9, no. 4. 2020. doi: 10.3390/jcm9041117.Google ScholarCross Ref
- S. Sukegawa , “Deep Neural Networks for Dental Implant System Classification,” Biomolecules , vol. 10, no. 7. 2020. doi: 10.3390/biom10070984.Google Scholar
- M. Hadj Saïd, M.-K. Le Roux, jean-hugues Catherine, and R. Lan, “Development of an Artificial Intelligence Model to Identify a Dental Implant from a Radiograph,” The International journal of oral & maxillofacial implants, vol. 35, pp. 1077–1082, Dec. 2020, doi: 10.11607/jomi.8060.Google ScholarCross Ref
- M. V Rajee and C. Mythili, “Gender classification on digital dental x-ray images using deep convolutional neural network,” Biomedical Signal Processing and Control, vol. 69, p. 102939, 2021, doi: https://doi.org/10.1016/j.bspc.2021.102939.Google ScholarCross Ref
- S. B and N. R, “Transfer Learning Based Automatic Human Identification using Dental Traits- An Aid to Forensic Odontology,” Journal of Forensic and Legal Medicine, vol. 76, p. 102066, 2020, doi: https://doi.org/10.1016/j.jflm.2020.102066.Google ScholarCross Ref
- K.-S. Lee, S.-K. Jung, J.-J. Ryu, S.-W. Shin, and J. Choi, “Evaluation of Transfer Learning with Deep Convolutional Neural Networks for Screening Osteoporosis in Dental Panoramic Radiographs,” Journal of Clinical Medicine , vol. 9, no. 2. 2020. doi: 10.3390/jcm9020392.Google ScholarCross Ref
- S. Alkaabi, S. Yussof, and S. Al-Mulla, “Evaluation of Convolutional Neural Network based on Dental Images for Age Estimation,” in 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA), 2019, pp. 1–5. doi: 10.1109/ICECTA48151.2019.8959665.Google ScholarCross Ref
- R. Nader, A. Smorodin, N. de La Fourniere, yves Amouriq, and F. Autrusseau, “Automatic teeth segmentation on panoramic X-rays using deep neural networks,” Aug. 2022. [Online]. Available: https://hal.archives-ouvertes.fr/hal-03671003Google ScholarCross Ref
- B. Yaren Tekin, C. Ozcan, A. Pekince, and Y. Yasa, “An enhanced tooth segmentation and numbering according to FDI notation in bitewing radiographs,” Computers in Biology and Medicine, vol. 146, p. 105547, 2022, doi: https://doi.org/10.1016/j.compbiomed.2022.105547.Google ScholarDigital Library
- M. Prados-Privado, J. García Villalón, A. Blázquez Torres, C. H. Martínez-Martínez, and C. Ivorra, “A Convolutional Neural Network for Automatic Tooth Numbering in Panoramic Images,” BioMed Research International, vol. 2021, p. 3625386, 2021, doi: 10.1155/2021/3625386.Google ScholarCross Ref
- B. Silva, L. Pinheiro, L. Oliveira, and M. Pithon, “A study on tooth segmentation and numbering using end-to-end deep neural networks,” in 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 2020, pp. 164–171. doi: 10.1109/SIBGRAPI51738.2020.00030.Google ScholarCross Ref
- F. P. Mahdi, N. Yagi, and S. Kobashi, “Automatic Teeth Recognition in Dental X-Ray Images Using Transfer Learning Based Faster R-CNN,” in 2020 IEEE 50th International Symposium on Multiple-Valued Logic (ISMVL), 2020, pp. 16–21. doi: 10.1109/ISMVL49045.2020.00-36.Google ScholarCross Ref
- A. Gurses and A. B. Oktay, “Tooth Restoration and Dental Work Detection on Panoramic Dental Images via CNN,” in 2020 Medical Technologies Congress (TIPTEKNO), 2020, pp. 1–4. doi: 10.1109/TIPTEKNO50054.2020.9299272.Google ScholarCross Ref
- A. Javid, U. Rashid, and A. S. Khattak, “Marking Early Lesions in Labial Colored Dental Images using a Transfer Learning Approach,” in 2020 IEEE 23rd International Multitopic Conference (INMIC), 2020, pp. 1–5. doi: 10.1109/INMIC50486.2020.9318173.Google ScholarCross Ref
- J.-H. Lee, D.-H. Kim, S.-N. Jeong, and S.-H. Choi, “Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm,” Journal of Dentistry, vol. 77, pp. 106–111, 2018, doi: https://doi.org/10.1016/j.jdent.2018.07.015.Google ScholarCross Ref
- Y. Ariji, M. Mori, M. Fukuda, A. Katsumata, and E. Ariji, “Automatic visualization of the mandibular canal in relation to an impacted mandibular third molar on panoramic radiographs using deep learning segmentation and transfer learning techniques,” Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, 2022, doi: https://doi.org/10.1016/j.oooo.2022.05.014.Google ScholarCross Ref
- W. You, A. Hao, S. Li, Y. Wang, and B. Xia, “Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments,” BMC Oral Health, vol. 20, no. 1, p. 141, 2020, doi: 10.1186/s12903-020-01114-6.Google ScholarCross Ref
- K. Ono, Y. Iwamoto, Y.-W. Chen, and M. Nonaka, “Automatic Segmentation of Infant Brain Ventricles with Hydrocephalus in MRI Based on 2.5D U-Net and Transfer Learning,” Journal of Image and Graphics, pp. 42–46, Jan. 2020, doi: 10.18178/joig.8.2.42-46.Google ScholarCross Ref
- Y.-C. Mao , “Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs,” 2021, doi: 10.3390/s21134613.Google ScholarCross Ref
- F. Umer, S. Habib, and N. Adnan, “Application of deep learning in teeth identification tasks on panoramic radiographs,” Dento maxillo facial radiology, vol. 51, no. 5, p. 20210504, 2022, doi: 10.1259/dmfr.20210504.Google Scholar
- H. Mohammad-Rahimi , “Deep learning for caries detection: A systematic review,” Journal of Dentistry, vol. 122, 2022, doi: 10.1016/j.jdent.2022.104115.Google ScholarCross Ref
- R. Nagi, K. Aravinda, N. Rakesh, R. Gupta, A. Pal, and A. K. Mann, “Clinical applications and performance of intelligent systems in dental and maxillofacial radiology: A review,” Imaging Science in Dentistry, vol. 50, no. 2, pp. 81–92, 2020, doi: 10.5624/isd.2020.50.2.81.Google ScholarCross Ref
- V. Majanga and S. Viriri, “A Survey of Dental Caries Segmentation and Detection Techniques,” The Scientific World Journal, vol. 2022, p. 8415705, 2022, doi: 10.1155/2022/8415705.Google ScholarCross Ref
- N. K. Singh and K. Raza, “Progress in deep learning-based dental and maxillofacial image analysis: A systematic review,” Expert Systems with Applications, vol. 199, p. 116968, 2022, doi: https://doi.org/10.1016/j.eswa.2022.116968.Google ScholarDigital Library
- C.-W. Li , “Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph,” Sensors , vol. 21, no. 21. 2021. doi: 10.3390/s21217049.Google Scholar
- A. Vollmer , “Artificial Intelligence-Based Prediction of Oroantral Communication after Tooth Extraction Utilizing Preoperative Panoramic Radiography,” Diagnostics , vol. 12, no. 6. 2022. doi: 10.3390/diagnostics12061406.Google Scholar
- Y. Bayraktar and E. Ayan, “Diagnosis of interproximal caries lesions with deep convolutional neural network in digital bitewing radiographs,” Clinical Oral Investigations, vol. 26, no. 1, pp. 623–632, 2022, doi: 10.1007/s00784-021-04040-1.Google ScholarCross Ref
- L. Lian, T. Zhu, F. Zhu, and H. Zhu, “Deep Learning for Caries Detection and Classification,” Diagnostics , vol. 11, no. 9. 2021. doi: 10.3390/diagnostics11091672.Google Scholar
- F. P. Mahdi, K. Motoki, and S. Kobashi, “Optimization technique combined with deep learning method for teeth recognition in dental panoramic radiographs,” Scientific Reports, vol. 10, no. 1, p. 19261, 2020, doi: 10.1038/s41598-020-75887-9.Google ScholarCross Ref
- S. A. Prajapati, R. Nagaraj, and S. Mitra, “Classification of dental diseases using CNN and transfer learning,” in 2017 5th International Symposium on Computational and Business Intelligence (ISCBI), 2017, pp. 70–74. doi: 10.1109/ISCBI.2017.8053547.Google ScholarCross Ref
- G. Jader, J. Fontineli, M. Ruiz, K. Abdalla, M. Pithon, and L. Oliveira, “Deep Instance Segmentation of Teeth in Panoramic X-Ray Images,” in 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 2018, pp. 400–407. doi: 10.1109/SIBGRAPI.2018.00058.Google ScholarCross Ref
- A. K. Ismael and A. M. Khidhir, “Evaluation of Transfer Learning with CNN to classify the Jaw Tumors,” IOP Conference Series: Materials Science and Engineering, vol. 928, no. 3, p. 32072, 2020, doi: 10.1088/1757-899x/928/3/032072.Google ScholarCross Ref
- M. Sajad, I. Shafi, and J. Ahmad, “Automatic Lesion Detection in Periapical X-rays,” in 2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 2019, pp. 1–6. doi: 10.1109/ICECCE47252.2019.8940661.Google ScholarCross Ref
Index Terms
- Analytical Overview on Transfer Learning in Processing Dental X-rays
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