Introduction: There exist conflicting electrodiagnostic reports between diagnosing mild carpal tunnel syndrome (CTS) and normal results, depending on the interpretation methods used by electrodiagnosticians. This underscores the necessity for precise clinical guidelines. This study aims to assess how the variation between mild and normal electrophysiological reports impacts (1) subsequent clinical outcomes in patients diagnosed with CTS and (2) physicians' decision-making.
View Article and Find Full Text PDFStandardized tooth numbering is crucial in dentistry for accurate recordkeeping, targeted procedures, and effective communication in both clinical and forensic contexts. However, conventional manual methods are prone to errors, time-consuming, and susceptible to inconsistencies. This study presents an artificial intelligence (AI)-powered system that uses a deep learning-based object detection approach to automate tooth numbering in bitewing radiographs (BRs).
View Article and Find Full Text PDFStatement Of Problem: Digital education using virtual-reality simulators is essential for precise learning in modern education.
Purpose Of Study: This study aimed to develop a virtual-reality dental system for tooth preparation training and assess pre-virtual-reality experiences and perceptions of its potential benefits. We evaluated the post-virtual-reality experience in terms of the effectiveness of the virtual-reality system.