Scholarlink

<p>Artificial intelligence (AI) and additive manufacturing are increasingly integrated into digital orthodontic workflows, but orthodontic appliance design remains less automated than manufacturing. This narrative review synthesizes the current role of AI-related methods in automated and semi-automated orthodontic appliance geometry design within three-dimensional printing workflows. PubMed, Scopus, and Web of Science were searched for publications from 2015 to 2026, with selected foundational sources included. Current AI-related methods support segmentation, anatomical recognition, surface alignment, treatment-planning support, monitoring, and computer-aided design refinement rather than autonomous geometry generation. Higher automation is most evident in standardized appliances, particularly occlusal splints, clear aligners, and selected indirect bonding tray workflows, whereas fixed functional appliances, anchorage devices, TAD guides, and individualized wire-based constructions remain largely clinician-driven. Key barriers include anatomical variability, limited biomechanical-response prediction, limited explainability, regulatory uncertainty, and lack of appliance-specific datasets. Future progress will likely depend on clinician-supervised hybrid workflows integrating computational design support with biomechanical validation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Artificial Intelligence for Automated and Semi-Automated Orthodontic Appliance Design in 3D Printing Workflows: A Narrative Review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月26日

查看全文
  • Slávka Pčolová,
  • Daniela Tichá,
  • Peter Peciar,
  • Andrej Thurzo

摘要

Artificial intelligence (AI) and additive manufacturing are increasingly integrated into digital orthodontic workflows, but orthodontic appliance design remains less automated than manufacturing. This narrative review synthesizes the current role of AI-related methods in automated and semi-automated orthodontic appliance geometry design within three-dimensional printing workflows. PubMed, Scopus, and Web of Science were searched for publications from 2015 to 2026, with selected foundational sources included. Current AI-related methods support segmentation, anatomical recognition, surface alignment, treatment-planning support, monitoring, and computer-aided design refinement rather than autonomous geometry generation. Higher automation is most evident in standardized appliances, particularly occlusal splints, clear aligners, and selected indirect bonding tray workflows, whereas fixed functional appliances, anchorage devices, TAD guides, and individualized wire-based constructions remain largely clinician-driven. Key barriers include anatomical variability, limited biomechanical-response prediction, limited explainability, regulatory uncertainty, and lack of appliance-specific datasets. Future progress will likely depend on clinician-supervised hybrid workflows integrating computational design support with biomechanical validation.