Anatomical Deep Learning Analysis for Automated Detection of Dental Caries: A Novel Approach

Dental caries remains a prevalent oral health concern, necessitating accurate and efficient diagnostic methods for timely intervention. This paper proposes a novel approach utilizing deep learning techniques to automatically classify dental caries severity based on anatomical CT images. Convolutional Neural Networks (CNNs) are employed to analyse these images, providing a robust framework for precise classification. The study leverages a dataset comprising CT scans of dental structures, annotated with varying degrees of caries severity. The dataset is prepared to train and evaluate the CNN model effectively through extensive preprocessing and augmentation. The proposed model architecture is designed to extract intricate features from the CT images, enabling the classification of caries into low, mid, and high-severity categories. Experimental results demonstrate the efficacy of the proposed approach, showcasing high accuracy in distinguishing between different levels of dental caries severity. Moreover, the model exhibits robustness against noise and variations inherent in clinical CT scans, thus affirming its practical applicability in real-world scenarios. In conclusion, the developed deep learning framework offers a promising solution for automating the assessment of dental caries severity from CT images. By providing accurate and objective classification, this technology has the potential to assist clinicians in making informed treatment decisions and improving patient outcomes in dental healthcare settings.