Context Aware Convolutional Neural Network for Children Caries Diagnosis on Dental Panoramic Radiographs

Abstract

The objective of this study is to improve traditional convolutional neural networks for more accurate children dental caries diagnosis on panoramic radiographs. A context aware convolutional neural network (CNN) is proposed by considering information among adjacent teeth, based on the fact that caries of teeth often affects each other due to the same growing environment. Specifically, when performing caries diagnosis on a tooth, information from its adjacent teeth will be collected and adaptively fused for final classification. Children panoramic radiographs of 210 patients with one or more caries and 94 patients without caries are utilized, among which there are a total of 6028 teeth with 3039 to be caries. The proposed context aware CNN outperforms typical CNN baseline with the accuracy, precision, recall, F 1 score, and area-under-the-curve (AUC) being 0.8272, 0.8538, 0.8770, 0.8652, and 0.9005, respectively, showing potential to improve typical CNN instead of just copying them in previous works. Specially, the proposed method performs better than two five-year attending doctors for the second primary molar caries diagnosis. Considering the results obtained, it is beneficial to promote CNN based deep learning methods for assisting dentists for caries diagnosis in hospitals.

Advancing Dental Diagnostics: A Review of Artificial Intelligence Applications and Challenges in Dentistry

“…In 2022, Ref. [27] aimed to enhance the accuracy of diagnosing dental caries in children using panoramic radiographs. It utilized panoramic radiographs of 94 patients who did not have any caries and 210 patients who had one or more instances of dental caries.…”

“…A deep convolutional neural network architecture that has shown promise in analyzing dental images for various tasks, including caries diagnosis and segmentation, and consists of multiple layers of convolutional and pooling operations [27]. Figure 4 shows the original ResNet-18 architecture.…”

“…Demonstrating high accuracy, the study acknowledged drawbacks, including manual segmentation, an inadequate sample size, and lack of external validation. Additionally, Zhou et al (2022) sought to enhance traditional CNNs for more accurate identification of dental caries in children on panoramic radiographs, proposing a context-aware CNN that considers information from neighboring teeth [27].…”

Embracing the Unprecedented Pace of Change: Artificial Intelligence's Impact on Dentistry and Beyond

“…AI-powered image recognition systems can now detect and classify dental pathologies with unprecedented accuracy. 1 2 3 4 By augmenting the diagnostic process, AI empowers dentists to make informed decisions and devise personalized treatment plans, ultimately enhancing patient outcomes. 5 6 7 8 9 However, as we embark on this transformative journey, we must address concerns surrounding patient privacy, data security, and the responsible use of AI-generated insights.…”

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“…AI-powered image recognition systems can now detect and classify dental pathologies with unprecedented accuracy. [1][2][3][4] By augmenting the diagnostic process, AI empowers dentists to make informed decisions and devise personalized treatment plans, ultimately enhancing patient outcomes. [5][6][7][8][9] However, as we embark on this transformative journey, we must address concerns surrounding patient privacy, data security, and the responsible use of AI-generated insights.…”