A Narrative Review: Dental Radiology with Deep Learning
Abstract
In this paper, we explore the transformative potential of deep learning in dental radiology, focusing on its applications in disease detection, image segmentation, and treatment planning. By utilizing convolutional neural networks (CNNs), deep learning models have demonstrated remarkable success in diagnosing dental conditions such as caries, periodontal disease, and oral cancer from radiographs and cone-beam computed tomography (CBCT) scans. Additionally, we highlight the role of deep learning in automating the segmentation of dental structures and improving accuracy in procedures such as implant placement and orthodontic evaluations. However, this paper also addresses key challenges, including the limited availability of large, annotated datasets, the black-box nature of deep learning models, and the need for generalizability across diverse clinical settings. Ethical considerations, including data privacy and model bias, are also discussed. Finally, we outline future directions for the field, such as the integration of deep learning with advanced imaging technologies, the adoption of federated learning for collaborative model development, and the advancement of explainable AI to improve model interpretability. Through these developments, deep learning has the potential to revolutionize dental radiology, offering more precise diagnoses, personalized treatment plans, and ultimately, better patient outcomes.
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https://www.kaggle.com/datasets/killa92/dental-x-ray-images-dataset
