Clinical Research Directory

Development and Validation of a Deep Learning Model to Predict Endodontic Retreatment Difficulty From Periapical Radiographs

The aim of this study is to develop and evaluate an artificial intelligence-based model capable of analyzing periapical radiographs of maxillary and mandibular molars to predict the difficulty level of non-surgical root canal retreatment. By integrating deep learning techniques with routinely acquired periapical radiographs, this study aims to enhance diagnostic support, improve clinical decision-making, and facilitate appropriate case selection or referral in endodontic practice.

Effect of Integrated Apex Locator Motor Modes on Postoperative Pain in Endodontic Retreatment

This randomized controlled clinical trial aimed to evaluate the effect of different integrated electronic apex locator (EAL) modes on postoperative pain following nonsurgical endodontic retreatment. A total of 144 patients requiring retreatment of mandibular premolar teeth with periapical lesions were randomly assigned to four groups. In the control group, working length was determined using a conventional electronic apex locator. In the experimental groups, an integrated EAL motor was used with three different modes: apical reverse, apical slow down, and apical stop. All procedures were performed under standardized conditions. Postoperative pain was assessed using a numeric rating scale (NRS) at 6 and 12 hours and on days 1, 2, 3, 5, and 7 after treatment. The study aimed to determine whether simultaneous working length control using integrated EAL modes influences postoperative pain compared with conventional working length determination.