AI & Deep Learning for Orthognathic Surgery
Current Stage of Innovation
TRL


AI systems like CEFBOT achieved up to 94.4% diagnostic accuracy; show strong potential for improving surgical outcomes and precision, though challenges remain in data availability, cost, and ethics.
Manual cephalometric analysis and surgical planning have long been essential components of orthodontic and maxillofacial diagnosis, yet they remain highly time-consuming and prone to human error. The traditional process involves manually tracing anatomical landmarks on radiographs, calculating measurements, and interpreting skeletal relationships, all of which demand precision and experience. Even minor inaccuracies in landmark identification can lead to significant errors in diagnosis and treatment planning. Moreover, manual methods lack real-time adaptability, making it difficult for clinicians to instantly visualize treatment outcomes or simulate surgical modifications. This limitation often results in delayed decision-making and less personalized treatment strategies. In complex cases, especially those involving facial asymmetry or orthognathic surgery, the absence of digital integration reduces efficiency and accuracy. As patient expectations for aesthetic and functional outcomes rise, traditional methods struggle to meet modern clinical demands.

