Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan.
Sci Rep. 2024 Apr 2;14(1):7699. doi: 10.1038/s41598-024-57632-8.
Nasopalatine duct cysts are difficult to detect on panoramic radiographs due to obstructive shadows and are often overlooked. Therefore, sensitive detection using panoramic radiography is clinically important. This study aimed to create a trained model to detect nasopalatine duct cysts from panoramic radiographs in a graphical user interface-based environment. This study was conducted on panoramic radiographs and CT images of 115 patients with nasopalatine duct cysts. As controls, 230 age- and sex-matched patients without cysts were selected from the same database. The 345 pre-processed panoramic radiographs were divided into 216 training data sets, 54 validation data sets, and 75 test data sets. Deep learning was performed for 400 epochs using pretrained-LeNet and pretrained-VGG16 as the convolutional neural networks to classify the cysts. The deep learning system's accuracy, sensitivity, and specificity using LeNet and VGG16 were calculated. LeNet and VGG16 showed an accuracy rate of 85.3% and 88.0%, respectively. A simple deep learning method using a graphical user interface-based Windows machine was able to create a trained model to detect nasopalatine duct cysts from panoramic radiographs, and may be used to prevent such cysts being overlooked during imaging.
由于存在阻塞性阴影，腭鼻管囊肿在全景片上难以检测，因此经常被忽视。因此，在临床上使用全景片进行敏感检测非常重要。本研究旨在创建一个在基于图形用户界面的环境中从全景片中检测腭鼻管囊肿的训练模型。本研究对 115 例腭鼻管囊肿患者的全景片和 CT 图像进行了研究。作为对照，从同一数据库中选择了 230 名年龄和性别匹配、无囊肿的患者。对 345 张预处理后的全景片进行划分，其中 216 个训练数据集、54 个验证数据集和 75 个测试数据集。使用预训练的 LeNet 和预训练的 VGG16 作为卷积神经网络，对 400 个 epoch 进行深度学习，以对囊肿进行分类。计算 LeNet 和 VGG16 使用的深度学习系统的准确率、灵敏度和特异性。LeNet 和 VGG16 的准确率分别为 85.3%和 88.0%。使用基于图形用户界面的 Windows 机器的简单深度学习方法能够创建一个从全景片中检测腭鼻管囊肿的训练模型，并且可以用于防止在成像过程中忽略这些囊肿。
