Oral and Maxillofacial Surgery Department, Faculty of Dentistry, Mersin University, Mersin, Turkey.
Department of Electrical and Electronic Engineering, Faculty of Engineering, Munzur University, Tunceli, Turkey.
Oral Surg Oral Med Oral Pathol Oral Radiol. 2024 Jul;138(1):149-161. doi: 10.1016/j.oooo.2023.06.001. Epub 2023 Jun 10.
This study, which uses artificial intelligence-based methods, aimed to determine the limits of pathologic conditions and infections related to the maxillary sinus in cone beam computed tomography (CBCT) images to facilitate the work of dentists.
A new UNet architecture based on a state-of-the-art Swin transformer called Res-Swin-UNet was developed to detect the sinus. The encoder part of the proposed network model consists of a pre-trained ResNet architecture, and the decoder part consists of Swin transformer blocks. Swin transformers achieve powerful global context properties with self-attention mechanisms. Because the output of the Swin transformer generates sectorized features, the patch expanding layer was used in this section instead of the traditional upsampling layer. In the last layer of the decoder, sinus diagnosis was conducted through classical convolution and sigmoid function. In experimental works, we used a data set including 298 CBCT images.
The Res-Swin-UNet model achieved more success, with a 91.72% F1-score, 99% accuracy, and 84.71% IoU, outperforming the state-of-the-art models.
The deep learning-based model proposed in the present study can assist dentists in automatically detecting the boundaries of pathologic conditions and infections within the maxillary sinus based on CBCT images.
本研究利用基于人工智能的方法，旨在确定锥形束计算机断层扫描（CBCT）图像中与上颌窦相关的病理状况和感染的界限，以方便牙医的工作。
开发了一种新的基于最先进的 Swin 变压器的 UNet 架构，称为 Res-Swin-UNet，用于检测窦。所提出的网络模型的编码器部分由预训练的 ResNet 架构组成，解码器部分由 Swin 变压器块组成。Swin 变压器通过自注意力机制实现强大的全局上下文属性。由于 Swin 变压器的输出生成扇形特征，因此在本节中使用了补丁扩展层，而不是传统的上采样层。在解码器的最后一层，通过经典卷积和 sigmoid 函数进行窦诊断。在实验工作中，我们使用了一个包含 298 个 CBCT 图像的数据集。
Res-Swin-UNet 模型取得了更大的成功，其 F1 得分为 91.72%，准确率为 99%，IoU 为 84.71%，优于最先进的模型。
本研究提出的基于深度学习的模型可以帮助牙医根据 CBCT 图像自动检测上颌窦内病理状况和感染的边界。
