口腔医学影像是进行临床口腔疾病检测、筛查、诊断和治疗评估的重要工具,对口腔影像进行准确分析对于后续治疗计划的制定至关重要。常规的口腔医学影像分析依赖于医师的水平和经验,存在阅片效率低、可重复性低以及定量分析欠缺的问题。深度学习可以从大样本数据中自动学习并获取优良的特征表达,提升各类机器学习任务的效率和性能,目前被广泛应用于医学影像分析处理的各类任务之中。基于深度学习的口腔医学影像处理是目前的研究热点,但由于口腔医学领域内在的特殊性和复杂性,以及口腔医学影像数据样本量通常较小的问题,给深度学习方法在相关学习任务和场景的应用带来了新的挑战。本文从口腔医学影像领域常用的二维X射线影像、三维点云/网格影像和锥形束计算机断层扫描影像三种影像出发,介绍深度学习技术在口腔医学影像处理及分析领域应用的思路和现状,分析了各算法的优缺点及该领域所面临的问题和挑战,并对未来的研究方向和可能开展的临床应用进行展望,以助力智慧口腔建设。

Dental imaging is an essential tool for the detection, screening, diagnosis, and therapeutic evaluation of clinical oral diseases, with accurate analysis of the images being vital to the development of subsequent treatment plans. Deep learning can automatically learn and obtain superior feature expressions from large sample data to improve the efficiency and performance of various machine learning tasks, which is currently widely used in many fields such as machine translation, speech recognition, and computer vision. With the integration of artificial intelligence and various fields, smart healthcare has become an important application area of deep learning, which provides an effective way to solve the following clinical problems: 1) The shortage of experienced radiologists in the field of dentistry cannot meet the rapidly growing medical demand; 2) With sufficient medical resources, experienced physicians cannot meet the rapidly growing medical demand; 3) Different physicians have different interpretations of the same oral image, which are easily influenced by subjectivity. Deep learning-based dental image processing is a topical research topic at present. Due to the inherent specificity and complexity of the medical field, as well as the problem of insufficient dental image data samples, brings new challenges to applying deep learning methods in relevant learning tasks and scenarios. This paper mainly reviews the applications of deep learning methods in various applications using three major dental imaging (i.e., two-dimensional oral X-ray images, three-dimensional tooth point cloud/mesh images, cone beam computed tomography (CBCT)). These applications include tooth segmentation, caries detection, tumor detection, etc. The reviews on two-dimensional oral X-ray images focus on bitewings, periapical and panoramic X-ray based on deep learning methods. Bitewing X-rays usually show the contact surface from the distal end of the canine to the most distal molar. They are mainly used to diagnose proximal caries, assess the extent of caries, identify secondary caries under existing restorations, etc. For caries detection using bitewing X-rays, we mainly review deep learning methods using convolutional neural network architectures, such as full convolutional neural networks and U-Net architectures. Detection of periodontitis and caries based on periapical X-rays primarily introduces methods on the basis of convolutional neural networks and backward propagation neural network. In contrast to bitewing and periapical X-rays, panoramic x-rays show not only teeth and gums, but also jaw, skull, spine and other bones. We provide a focused review of the application of deep learning methods in panoramic radiographic images from three categories of directions. Namely 1) tooth detection and numbering. 2) tooth segmentation. 3) non-dental disease detection. The three-dimensional tooth point cloud/mesh image is a digital 3D oral model obtained by scanning and reconstructing the patient"s mouth in real-time using an intraoral scanner. The reviews on three-dimensional tooth point cloud/mesh image focus on tooth segmentation based on deep learning. Deep learning methods for tooth segmentation can be divided into two categories: 1) fully supervised methods. 2) non-full supervision methods. For fully supervised methods, we mainly introduce the hierarchical network architecture model and the end-to-end network architecture model with a large amount of labeled data and annotation. While for non-full supervision methods, we primarily review self-supervised learning and semi-supervised learning methods that require only partially annotated data, as well as methods that utilize weakly annotated ideas. Currently, cone beam computed tomography imaging is a non-invasive, low-radiation technique widely used in dental diagnosis and treatment. This paper summarizes deep learning methods on CBCT images focusing on three major areas: 1) tooth segmentation. 2) dental implants. 3) oral and maxillofacial surgery. Nevertheless, from the current application of deep learning methods, it is still not yet easy for the deep learning methods already proposed to analyze all aspects of the patient"s oral health and systemic health status together in order to develop a more personalized and high-level treatment plan for oral diseases. Deep learning has made some progress in the field of dental image processing, but still faces some serious challenges. Small sample size has been a serious problem in the field of medical image analysis, which can be effectively alleviated by non-fully supervised deep learning methods such as weakly supervised learning, self-supervised learning, along with machine learning methods including migration learning, sample less learning, and incremental learning. In addition, annotation of dental medical images is a time-consuming and laborious task that relies heavily on the experience of the practitioner, which is one of the barriers limiting the widespread and deep application of deep learning. Therefore, research focusing on automatic data annotation must be extensively conducted. At Present, the development of deep learning applications in dental imaging is still in a relatively early stage. The development of this field cannot be achieved without the cooperation of computer scientists and clinicians as well as experts in imaging equipment and software development to solve the problem of how to deploy lightweight deep learning networks into convenient medical devices. In conclusion, the combination of deep learning and dental image analysis has been a major trend, with significant results in various analysis tasks, yet further in-depth research is still needed to lead the development of dental image analysis into a new phase.