Effect of smart endodontic motors (AI-based torque control) on file separation and dentinal damage

INTRODUCTION. Implementation of artificial intelligence (AI) in the endodontic motor technology offers improved safety due to real-time torque monitoring and adaptive control. Nevertheless, there is little comparative evidence on their clinical effectiveness in preventing instrument separation and reduction of iatrogenic dentinal damage. MATERIALS AND METHODS. Ninety human mandibular molars with mesial canals (curvature 25–35°) were randomly divided to three groups ( n = 30) Group A (AI-based smart motor with adaptive torque control), Group B (conventional motor with preset torque limits), and Group C (conventional motor with auto-reverse operation). ProTaper Next rotary files were used to prepare the standardized canals. The main variables were file separation rate and dentinal microcracks incidence evaluated through micro-computed tomography (micro-CT). Preparation time, apical transportation, canal centering ratio, and motor operational parameters were the secondary outcomes. RESULTS. Group A exhibited a much lower file separation rate (0% vs. 10.0% vs. 13.3% in Group B vs. Group C, p = 0.038) and fewer dentinal microcracks formed (16.7% vs. 43.3% vs. 50.0% p = 0.006). Group A recorded significantly lower peak of mean torque (2.1 + 0.4 Ncm compared to 2.8 + 0.6 Ncm in Group B compared to 2.9 + 0.7 Ncm in Group C, p < 0.001). Apical transportation ( p = 0.284) and canal centering ratio ( p = 0.412) showed no significant differences. Time of preparation did not differ among groups ( p = 0.156). CONCLUSIONS. Smart endodontic motors with adaptive torque control powered by AI allow a significantly lower file separation and dentinal microcrack formation than using conventional motors and offer similar shaping efficiency and preparation quality. Based on these findings, AI-based motor technology can be clinically adopted to improve the safety of the procedures.

Hali tarjima qilinmagan