背景:在临床前培训期间,牙科学生拍摄包含拔除患者牙齿的丙烯酸(塑料)块的X射线照片。随着医疗记录的数字化,创建了一个中央归档系统来存储和检索所有X射线图像,不管它们是否是丙烯酸块上牙齿的图像,或者来自病人的。在数字化进程的早期阶段,由于数据管理系统的不成熟,许多图像被混合在一起,并存储在一个统一的归档系统中的随机位置,包括病人记录文件.过滤并清除不期望的训练图像是必要的,因为手动搜索这样的图像是有问题的。因此,此技巧的目的是将口内图像与丙烯酸块上的人工图像区分开。
方法:本研究采用了一种人工智能(AI)解决方案,可以自动区分患者的口腔X光片和丙烯酸块的口腔X光片。迁移学习的概念被应用于牙科医院提供的数据集。
结果:准确性评分,F1得分,召回得分为98.8%,99.2%,100%,分别,是使用VGG16预训练模型实现的。与最初使用96.5%的基线模型获得的结果相比,这些结果更敏感,97.5%,和98.9%的准确率,F1得分,和召回得分分别。
结论:所提出的使用迁移学习的系统能够准确地识别“假”射线照片图像,并将其与真实的口内图像区分开。
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: The proposed system using transfer learning was able to accurately identify \"fake\" radiographs images and distinguish them from the real intraoral images.