Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, Sharjah, United Arab Emirates.
Department of Restorative Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates.
Int Dent J. 2024 Dec;74(6):1471-1482. doi: 10.1016/j.identj.2024.08.002. Epub 2024 Sep 3.
During preclinical training, dental students take radiographs of acrylic (plastic) blocks containing extracted patient teeth. With the digitisation of medical records, a central archiving system was created to store and retrieve all x-ray images, regardless of whether they were images of teeth on acrylic blocks, or those from patients. In the early stage of the digitisation process, and due to the immaturity of the data management system, numerous images were mixed up and stored in random locations within a unified archiving system, including patient record files. Filtering out and expunging the undesired training images is imperative as manual searching for such images is problematic. Hence the aim of this stidy was to differentiate intraoral images from artificial images on acrylic blocks.
An artificial intelligence (AI) solution to automatically differentiate between intraoral radiographs taken of patients and those taken of acrylic blocks was utilised in this study. The concept of transfer learning was applied to a dataset provided by a Dental Hospital.
An accuracy score, F1 score, and a recall score of 98.8%, 99.2%, and 100%, respectively, were achieved using a VGG16 pre-trained model. These results were more sensitive compared to those obtained initally using a baseline model with 96.5%, 97.5%, and 98.9% accuracy score, F1 score, and a recall score respectively.
The proposed system using transfer learning was able to accurately identify "fake" radiographs images and distinguish them from the real intraoral images.
在临床前培训期间，牙科学生对含有已拔出患者牙齿的丙烯酸（塑料）块进行 X 光拍摄。随着医疗记录的数字化，创建了一个中央归档系统，用于存储和检索所有 X 光图像，无论它们是来自丙烯酸块上的牙齿图像，还是来自患者的图像。在数字化进程的早期阶段，由于数据管理系统不够成熟，许多图像被混淆并存储在统一归档系统中的随机位置，包括患者记录文件中。过滤和删除不需要的培训图像是必要的，因为手动搜索这些图像是有问题的。因此，本研究的目的是区分口内图像和丙烯酸块上的人工图像。
本研究利用人工智能（AI）解决方案自动区分从患者和丙烯酸块上拍摄的口内射线照片。本研究应用了迁移学习的概念，该概念来自一家牙科医院提供的数据集。
使用预先训练的 VGG16 模型，分别达到了 98.8%、99.2%和 100%的准确率、F1 分数和召回率。与最初使用基线模型（准确率、F1 分数和召回率分别为 96.5%、97.5%和 98.9%）相比，这些结果更为敏感。
使用迁移学习的提出的系统能够准确识别“假”射线照片图像，并将其与真实的口内图像区分开来。
