Dental cavity analysis, prediction, localization, and quantification using computer vision

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Dental health assessment is a critical component of overall well-being, and advancements in computer vision and deep learning have opened new avenues for automating and enhancing this process. In this study, we present a comprehensive approach to dental cavity analysis, spanning localization, quantification, and visualization. Our methodology leveraged a diverse dataset of colored dental images that had been meticulously augmented and annotated. The You Only Look Once model was employed for precise dental cavity localization, providing bounding box predictions. Remarkably, these results were obtained based on images from standard device cameras. Subsequently, we introduced the use of the segment anything model segmentation model, known for its zero-shot generalization capabilities, to focus on the exact areas of dental cavities. This approach enhanced the granularity of our analysis, providing dental professionals with detailed visualizations for precise diagnosis. During the quantification phase, we extracted cavity areas from bounding box coordinates, enabling accurate measurement of cavity sizes. The model achieved a notable mean average precision of 0.732, an accuracy of 0.789, and a recall of 0.701. Moreover, the model converged quickly, with most metrics achieving near-optimal results after 100 iterations. This quantitative data augments traditional diagnosis methods, facilitating more informed treatment decisions.