<![CDATA[Background. Malocclusion, a prevalent dental disorder, disrupts tooth alignment, mastication, speech, and aesthetics, significantly affecting self-confidence. Its high prevalence in Asian populations, particularly in Indonesia, where 80% of individuals are affected, necessitates more precise and efficient orthodontic solutions. Conventional archwire bending methods remain prone to inaccuracies, contributing to treatment failures in over 37% of cases. This study explores the integration of Convolutional Neural Networks (CNNs) into robotic archwire bending systems as a transformative innovation to enhance orthodontic precision and efficiency. Research Method. A comprehensive literature review was conducted using major scientific databases, including PubMed, ScienceDirect, SCOPUS, ProQuest, and EBSCO, focusing on studies published from 2017 onward. Ten relevant articles were analyzed to evaluate CNN applications in robotic systems and their potential to improve orthodontic appliance production accuracy. Findings. The CNN-driven robotic system operates through two primary subsystems—data processing and mechanical execution supported by 3D modeling, CAD-based stress analysis, and Finite Element Method (FEM) validation. In the mechanical phase, robotic arms guided by sEMG signals and optimized through CNN-based motion recognition ensure superior precision in archwire shaping. This system minimizes human error, enhances treatment accuracy, and significantly reduces clinical time. Conclusion. CNN-integrated robotic archwire bending represents a breakthrough in orthodontic practice, enabling highly accurate, efficient, and patient-centered treatment outcomes. This innovation aligns with Sustainable Development Goal (SDG) 3 on Good Health and Well-Being, offering a substantial leap toward the future of digital orthodontics.]]>
