EARLY DETECTION OF PERIAPICAL LESIONS USING DEEP LEARNING IN CBCT IMAGING
DOI:
https://doi.org/10.29121/shodhkosh.v5.i3.2024.5830Keywords:
Periapical Lesions, Cone Beam Computed Tomography (CBCT), Deep Learning, Convolutional Neural Networks (CNN), Dental ImagingAbstract [English]
Timely diagnosis of periapical lesions are vital to the overall management of the patients in endodontics. Cone Beam Computed Tomography (CBCT) offers clear, three-dimensional radiographs with higher resolution than standard radiographs for detection of such lesions. In this paper, a deep learning based framework using a CNN is proposed for automatic periapical lesion detection from CBCT scans. With pre-training on the well-analyzed dataset covering multiple ethnicity and annotations, the model achieved superior accuracy, sensitivity and specificity, indicating its potential to provide assistance to the clinicians for projective diagnosis process while reducing diagnostic time and subjective variation. The findings indicate the promise of AI-based tools in improving workflow of diagnoses in dental radiology.
References
J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 3431–3440, 2015. DOI: https://doi.org/10.1109/CVPR.2015.7298965
O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional networks for biomedical image segmentation," in Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. (MICCAI), pp. 234–241, 2015. DOI: https://doi.org/10.1007/978-3-319-24574-4_28
M. Miki, T. Muramatsu, H. Kobayashi, et al., "Classification of periapical lesions in dental panoramic radiographs using deep convolutional neural networks," Diagnostics, vol. 10, no. 12, pp. 1–14, 2020.
M. J. White, and B. J. Pharoah, Oral Radiology: Principles and Interpretation, 7th ed., St. Louis, MO, USA: Mosby, 2014.
A. Esteva et al., "A guide to deep learning in healthcare," Nat. Med., vol. 25, no. 1, pp. 24–29, Jan. 2019. DOI: https://doi.org/10.1038/s41591-018-0316-z
A. Ariji, E. Yanashita, and Y. Kise, "Deep learning for detecting periapical lesions in dental CBCT images," Oral Radiol., vol. 37, pp. 291–297, 2021.
K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 770–778, 2016. DOI: https://doi.org/10.1109/CVPR.2016.90
D. L. G. Hill, C. Studholme, and D. J. Hawkes, "Medical image registration," Phys. Med. Biol., vol. 46, no. 3, pp. R1–R45, 2001. DOI: https://doi.org/10.1088/0031-9155/46/3/201
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Copyright (c) 2024 Dr. Rais Allauddin Mulla
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