In the field of dentistry, there is a growing demand for increased precision in diagnostic tools, with a specific focus on advanced imaging techniques such as computed tomography, cone beam computed tomography, magnetic resonance imaging, ultrasound, and traditional intra-oral periapical X-rays. Deep learning has emerged as a pivotal tool in this context, enabling the implementation of automated segmentation techniques crucial for extracting essential diagnostic data. This integration of cutting-edge technology addresses the urgent need for effective management of dental conditions, which, if left undetected, can have a significant impact on human health. The impressive track record of deep learning across various domains, including dentistry, underscores its potential to revolutionize early detection and treatment of oral health issues. Objective: Having demonstrated significant results in diagnosis and prediction, deep convolutional neural networks (CNNs) represent an emerging field of multidisciplinary research. The goals of this study were to provide a concise overview of the state of the art, standardize the current debate, and establish baselines for future research. Method: In this study, a systematic literature review is employed as a methodology to identify and select relevant studies that specifically investigate the deep learning technique for dental imaging analysis. This study elucidates the methodological approach, including the systematic collection of data, statistical analysis, and subsequent dissemination of outcomes. Conclusion: This work demonstrates how Convolutional Neural Networks (CNNs) can be employed to analyze images, serving as effective tools for detecting dental pathologies. Although this research acknowledged some limitations, CNNs utilized for segmenting and categorizing teeth exhibited their highest level of performance overall.

翻译：在牙科领域，对诊断工具精度的需求日益增长，尤其关注计算机断层扫描、锥形束计算机断层扫描、磁共振成像、超声及传统口内根尖X光片等先进影像技术。深度学习已成为该领域的关键工具，能够实现自动化分割技术，这对提取关键诊断数据至关重要。这一尖端技术的整合满足了有效管理牙科疾病的迫切需求——若未及时发现，此类疾病可能对人类健康产生重大影响。深度学习在包括牙科在内的多个领域取得的卓越成效，凸显了其彻底革新口腔健康问题早期检测与治疗的潜力。目的：深度卷积神经网络（CNN）在诊断和预测中已展现显著成果，正成为多学科研究的新兴领域。本研究旨在简要概述当前技术发展水平，规范当前学术讨论，并为未来研究建立基线。方法：本研究采用系统性文献综述作为方法学，筛选并确定专门探讨深度学习技术用于牙科影像分析的相关研究。文章阐明了方法学路径，包括系统性数据收集、统计分析及成果传播。结论：本研究证明卷积神经网络（CNN）可有效用于图像分析，成为检测牙科病变的有效工具。尽管本研究承认存在一定局限性，但用于牙齿分割与分类的CNN模型整体上展现了其最优性能。