College of Dentistry, University of Baghdad, Baghdad, Iraq.
Training and Workshops Center, University of Technology- Iraq, Baghdad, Iraq.
Comput Biol Med. 2024 Nov;182:109241. doi: 10.1016/j.compbiomed.2024.109241. Epub 2024 Oct 2.
The advent of precision diagnostics in pediatric dentistry is shifting towards ensuring early detection of dental diseases, a critical factor in safeguarding the oral health of the younger population. In this study, an innovative approach is introduced, wherein Discrete Wavelet Transform (DWT) and Generative Adversarial Networks (GANs) are synergized within an Image Data Fusion (IDF) framework to enhance the accuracy of dental disease diagnosis through dental diagnostic systems. Dental panoramic radiographs from pediatric patients were utilized to demonstrate how the integration of DWT and GANs can significantly improve the informativeness of dental images. In the IDF process, the original images, GAN-augmented images, and wavelet-transformed images are combined to create a comprehensive dataset. DWT was employed for the decomposition of images into frequency components to enhance the visibility of subtle pathological features. Simultaneously, GANs were used to augment the dataset with high-quality, synthetic radiographic images indistinguishable from real ones, to provide robust data training. These integrated images are then fed into an Artificial Neural Network (ANN) for the classification of dental diseases. The utilization of the ANN in this context demonstrates the system's robustness and culminates in achieving an unprecedented accuracy rate of 0.897, 0.905 precision, recall of 0.897, and specificity of 0.968. Additionally, this study explores the feasibility of embedding the diagnostic system into dental X-ray scanners by leveraging lightweight models and cloud-based solutions to minimize resource constraints. Such integration is posited to revolutionize dental care by providing real-time, accurate disease detection capabilities, which significantly reduces diagnostical delays and enhances treatment outcomes.
儿科牙科精准诊断的出现正在转向确保早期发现牙科疾病，这是保护年轻人群口腔健康的关键因素。在这项研究中，引入了一种创新方法，其中离散小波变换 (DWT) 和生成对抗网络 (GAN) 在图像数据融合 (IDF) 框架内协同工作，通过牙科诊断系统提高牙科疾病诊断的准确性。使用儿科患者的牙科全景射线照片来演示 DWT 和 GAN 的集成如何显著提高牙科图像的信息量。在 IDF 过程中，将原始图像、GAN 增强的图像和小波变换的图像结合起来创建一个综合数据集。DWT 用于将图像分解成频率分量，以增强细微病理特征的可见性。同时，GAN 用于用高质量、与真实图像难以区分的合成射线图像扩充数据集，以提供强大的数据训练。然后，将这些集成图像输入到人工神经网络 (ANN) 中进行牙科疾病分类。在这种情况下使用 ANN 展示了系统的稳健性，并最终达到了前所未有的 0.897 的准确率、0.905 的精度、0.897 的召回率和 0.968 的特异性。此外，本研究探讨了通过利用轻量级模型和基于云的解决方案将诊断系统嵌入牙科 X 射线扫描仪的可行性，以最小化资源限制。这种集成有望通过提供实时、准确的疾病检测能力来彻底改变牙科护理，从而显著减少诊断延迟并提高治疗效果。
