Anil Sukumaran, Porwal Priyanka, Porwal Amit
Dentistry, Hamad Medical Corporation, Doha, QAT.
Dentistry, Pushpagiri Institute of Medical Sciences and Research Centre, Tiruvalla, IND.
Cureus. 2023 Jul 11;15(7):e41694. doi: 10.7759/cureus.41694. eCollection 2023 Jul.
Diagnosing dental caries plays a pivotal role in preventing and treating tooth decay. However, traditional methods of diagnosing caries often fall short in accuracy and efficiency. Despite the endorsement of radiography as a diagnostic tool, the identification of dental caries through radiographic images can be influenced by individual interpretation. Incorporating artificial intelligence (AI) into diagnosing dental caries holds significant promise, potentially enhancing the precision and efficiency of diagnoses. This review introduces the fundamental concepts of AI, including machine learning and deep learning algorithms, and emphasizes their relevance and potential contributions to the diagnosis of dental caries. It further explains the process of gathering and pre-processing radiography data for AI examination. Additionally, AI techniques for dental caries diagnosis are explored, focusing on image processing, analysis, and classification models for predicting caries risk and severity. Deep learning applications in dental caries diagnosis using convolutional neural networks are presented. Furthermore, the integration of AI systems into dental practice is discussed, including the challenges and considerations for implementation as well as ethical and legal aspects. The breadth of AI technologies and their prospective utility in clinical scenarios for diagnosing dental caries from dental radiographs is presented. This review outlines the advancements of AI and its potential in revolutionizing dental caries diagnosis, encouraging further research and development in this rapidly evolving field.
诊断龋齿在预防和治疗蛀牙方面起着关键作用。然而,传统的龋齿诊断方法在准确性和效率方面往往存在不足。尽管放射成像被认可为一种诊断工具,但通过放射图像识别龋齿可能会受到个人解读的影响。将人工智能(AI)应用于龋齿诊断具有重大前景,有望提高诊断的准确性和效率。本文综述介绍了人工智能的基本概念,包括机器学习和深度学习算法,并强调了它们与龋齿诊断的相关性及潜在贡献。还进一步解释了为人工智能检查收集和预处理放射成像数据的过程。此外,探讨了用于龋齿诊断的人工智能技术,重点是用于预测龋齿风险和严重程度的图像处理、分析及分类模型。介绍了使用卷积神经网络在龋齿诊断中的深度学习应用。此外,还讨论了人工智能系统在牙科实践中的整合,包括实施过程中的挑战和注意事项以及伦理和法律方面。展示了人工智能技术的广度及其在从牙科X光片诊断龋齿的临床场景中的潜在效用。本文综述概述了人工智能的进展及其在彻底改变龋齿诊断方面的潜力,鼓励在这个快速发展的领域进行进一步的研究和开发。