BACKGROUND: Dental age (DA) estimation, crucial for appropriate orthodontic and paediatric treatment planning, traditionally relies on the analysis of developmental stages of teeth. Artificial intelligence (AI) has been increasingly employed for DA e...
OBJECTIVE: To evaluate and compare the performance of OpenAI's ChatGPT-4 and Microsoft Copilot in providing information on 3D-printed orthodontic appliances, with a focus on the accuracy, completeness of the content, and response generation time.
OBJECTIVE: The primary aim of the study was to evaluate the accuracy of automated artificial intelligence (AI) cephalometric landmark identification in cleft patients and compare it to landmarks identified by an expert orthodontist. The secondary obj...
INTRODUCTION: The purpose of the present study was to create a machine learning (ML) algorithm with the ability to predict the extraction/non-extraction decision in a racially and ethnically diverse sample.
OBJECTIVE: To compare the reliability of cephalometric landmark identification by an automated tracing software based on convolutional neural networks to human tracers.