Lobo Sofia, Argolinha Inês, Machado Vanessa, Botelho João, Rua João, Li Junying, Mendes José João
Egas Moniz Center for Interdisciplinary Research (CiiEM), Egas Moniz School of Health & Science, 2829-511 Almada, Portugal.
Department of Biologic and Materials Sciences & Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI 48109, USA.
J Clin Med. 2025 Feb 23;14(5):1495. doi: 10.3390/jcm14051495.
Precision in diagnosis is essential for achieving optimal outcomes in prosthodontics, orthodontics, and orthognathic treatments. Virtual articulators provide a sophisticated digital alternative to conventional methods, integrating intraoral scans, facial scans, and cone beam computed tomography (CBCT) to enhance treatment predictability. This review examines advancements in virtual articulator technology, including digital workflows, virtual facebow transfer, and occlusal analysis, with a focus on Artificial Intelligence (AI)-driven methodologies such as machine learning and artificial neural networks. The clinical implications, particularly in condylar guidance and sagittal condylar inclination, are investigated. By streamlining the acquisition and articulation of digital dental models, virtual articulators minimize material handling errors and optimize workflow efficiency. Advanced imaging techniques enable precise alignment of digital maxillary models within computer-aided design and computer-aided manufacturing systems (CAD/CAM), facilitating accurate occlusal simulations. However, challenges include potential distortions during digital file integration and the necessity for robust algorithms to enhance data superimposition accuracy. The adoption of virtual articulators represents a transformative advancement in digital dentistry, with promising implications for diagnostic precision and treatment outcomes. Nevertheless, further clinical validation is essential to ensure the reliable transfer of maxillary casts and refine digital algorithms. Future developments should prioritize the integration of AI to enhance predictive modeling, positioning virtual articulators as a standard tool in routine dental practice, thereby revolutionizing treatment planning and interdisciplinary collaboration. This review explores advancements in virtual articulators, focusing on their role in enhancing diagnostic precision, occlusal analysis, and treatment predictability. It examines digital workflows, AI-driven methodologies, and clinical applications while addressing challenges in data integration and algorithm optimization.
在口腔修复学、正畸学和正颌治疗中，精确诊断对于实现最佳治疗效果至关重要。虚拟牙合架为传统方法提供了一种先进的数字替代方案，它整合了口内扫描、面部扫描和锥形束计算机断层扫描（CBCT），以提高治疗的可预测性。本文综述了虚拟牙合架技术的进展，包括数字工作流程、虚拟面弓转移和咬合分析，重点关注人工智能（AI）驱动的方法，如机器学习和人工神经网络。研究了其临床意义，特别是在髁突引导和髁突矢状倾斜方面的意义。通过简化数字牙科模型的获取和咬合，虚拟牙合架将材料处理误差降至最低，并优化工作流程效率。先进的成像技术能够在计算机辅助设计和计算机辅助制造系统（CAD/CAM）中精确对齐数字上颌模型，便于进行准确的咬合模拟。然而，挑战包括数字文件整合过程中可能出现的失真，以及需要强大的算法来提高数据叠加的准确性。虚拟牙合架的应用代表了数字牙科领域的一项变革性进展，对诊断精度和治疗效果具有广阔的前景。尽管如此，进一步的临床验证对于确保上颌模型的可靠转移和完善数字算法至关重要。未来的发展应优先考虑整合人工智能以增强预测建模，将虚拟牙合架定位为常规牙科实践中的标准工具，从而彻底改变治疗计划和跨学科协作。本文综述了虚拟牙合架的进展，重点关注其在提高诊断精度、咬合分析和治疗可预测性方面的作用。它研究了数字工作流程、人工智能驱动的方法和临床应用，同时探讨了数据整合和算法优化方面的挑战。
