Mrs. L. Velivizhi, S. Sujatha

DOI: https://doi.org/10.5281/zenodo.23837590

Panoramic dental radiographs, known as orthopantomograms (OPGs), give a wide view of the teeth and surrounding jaw structures. Interpreting them can be complicated by image quality, anatomical overlap and variation between imaging devices. This paper proposes an AI-based workflow for the preliminary analysis of OPG images using a convolutional neural network (CNN) or a transfer-learning model. The planned system comprises image validation, preprocessing, model inference and a simple mobile interface through which a user can upload an image and view the result. Dataset selection, annotation quality, patient privacy and class-wise evaluation are treated as core design requirements, and performance will be assessed with accuracy, precision, recall, F1-score and a confusion matrix rather than accuracy alone. The work is currently at the design stage, so no trained-model performance or clinical results are claimed. The intended outcome is a research prototype that can support preliminary review while leaving diagnostic decisions to qualified dental professionals.

Artificial intelligence, dental imaging, OPG, panoramic radiograph, convolutional neural network, transfer learning, image classification, mobile application.

This paper has presented a proposed AI-based workflow for the preliminary analysis of dental OPG images. The design links image validation, preprocessing, CNN-based inference, result presentation and a mobile interface. It also identifies dataset governance, label quality, class-wise evaluation and cautious communication of model output as essential parts of the work. The prototype is intended to support research and demonstration, not to replace clinical judgment.

[1] G. Litjens et al., “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60–88, 2017.
[2] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, 2015.
[3] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
[4] C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of Big Data, vol. 6, article 60, 2019.
[5] M. Raghu, C. Zhang, J. Kleinberg, and S. Bengio, “Transfusion: Understanding transfer learning for medical imaging,” in Advances in Neural Information Processing Systems, vol. 32, 2019.
[6] World Health Organization, Global Oral Health Status Report: Towards Universal Health Coverage for Oral Health by 2030. Geneva, Switzerland: WHO, 2022.
[7] F. Schwendicke, W. Samek, and J. Krois, “Artificial intelligence in dentistry: Chances and challenges,” Journal of Dental Research, vol. 99, no. 7, pp. 769–774, 2020.
[8] E. J. Topol, “High-performance medicine: The convergence of human and artificial intelligence,” Nature Medicine, vol. 25, pp. 44–56, 2019.
[9] National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1, 2023.

Mrs. L. Velivizhi, S. Sujatha (2026). AI-Based Dental Disease Detection Using OPG Images. International Journal of Computer Science Engineering Techniques, 10(5), 418–420. ISSN: 2455-135X. DOI: https://doi.org/10.5281/zenodo.23837590