Abstract：
Computer vision falls under the broad umbrella of artificial intelligence that mimics human vision and plays a vital role in dental imaging. Dental practitioners visualize and interpret teeth, and the structure surrounding the teeth and detect abnormalities by manually examining various dental imaging modalities. Due to the complexity and cognitive difficulty of comprehending medical data, human error makes correct diagnosis difficult. Automated diagnosis may be able to help alleviate delays, hasten practitioners\' interpretation of positive cases, and lighten their workload. Several medical imaging modalities like X-rays, CT scans, color images, etc. that are employed in dentistry are briefly described in this survey. Dentists employ dental imaging as a diagnostic tool in several specialties, including orthodontics, endodontics, periodontics, etc. In the discipline of dentistry, computer vision has progressed from classic image processing to machine learning with mathematical approaches and robust deep learning techniques. Here conventional image processing techniques solely as well as in conjunction with intelligent machine learning algorithms, and sophisticated architectures of dental radiograph analysis employ deep learning techniques. This study provides a detailed summary of several tasks, including anatomical segmentation, identification, and categorization of different dental anomalies with their shortfalls as well as future perspectives in this field.
摘要：
计算机视觉属于人工智能的广阔保护伞，它模仿人类视觉，在牙科成像中起着至关重要的作用。牙科从业者可视化和解释牙齿，和牙齿周围的结构，并通过手动检查各种牙科成像方式来检测异常。由于理解医疗数据的复杂性和认知难度，人为错误使正确诊断变得困难。自动诊断可能有助于缓解延误，加快从业者对阳性案例的解释，减轻他们的工作量。几种医学成像方式，如X射线，CT扫描,彩色图像,等。在这项调查中简要描述了牙科中的应用。牙医在几个专业中使用牙科成像作为诊断工具，包括正畸,牙髓,牙周病,等。在牙科学科中，计算机视觉已经从经典的图像处理发展到具有数学方法和强大的深度学习技术的机器学习。在这里，传统的图像处理技术以及与智能机器学习算法的结合，和复杂的架构牙科射线照片分析采用深度学习技术。这项研究提供了几个任务的详细总结，包括解剖分割，identification,不同牙齿异常的分类及其不足，以及该领域的未来前景。
