Endodontics

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.

The treatment of pulpal diseases in primary teeth represents a significant challenge in public health, particularly in emergency dental care settings, where factors such as limited clinical time, low child cooperation, and structural constraints directly impact treatment effectiveness. In these contexts, conventional pulpectomy using zinc oxide-eugenol (ZOE), although effective, may present operational limitations that restrict its large-scale applicability in public health systems. In this scenario, non-instrumental endodontic treatment (NIET), based on the concept of Lesion Sterilization and Tissue Repair (LSTR), has emerged as a simplified alternative. Among the available options, CTZ paste shows potential for single-visit application, reducing clinical time and technical complexity. This study aims to evaluate the clinical and radiographic non-inferiority of CTZ paste compared to conventional pulpectomy with ZOE in primary molars with pulp necrosis, with or without abscess or fistula, treated in emergency dental care settings. This is a randomized, pragmatic, controlled, non-inferiority clinical trial conducted at a single center, with clinical and radiographic follow-up at 3, 6, and 12 months. Participants will be allocated into two groups: (1) NIET with CTZ paste and (2) conventional pulpectomy with ZOE. Primary outcomes include pain resolution and absence of clinical signs of infection. Secondary outcomes include radiographic success, clinical time, child behavior (Frankl Scale), need for retreatment, and oral health-related quality of life. The findings are expected to provide robust evidence regarding the effectiveness of CTZ paste under real-world clinical conditions, contributing to the development of more accessible and effective protocols within public health systems, with the potential to expand access to conservative treatment and reduce early tooth extractions in pediatric dentistry.

This observational study aims to evaluate the prevalence, severity, and characteristics of neck musculoskeletal symptoms among Turkish endodontists. Endodontists are exposed to prolonged static postures and repetitive movements during clinical procedures, which may increase the risk of neck-related disorders. Participants will be asked to complete questionnaires regarding demographic characteristics, occupational factors, ergonomic conditions, and neck pain. In addition, physical assessments including cervical range of motion, muscle strength, endurance, posture, and proprioception will be performed using validated measurement tools. The study also aims to identify factors associated with neck pain, such as working conditions, posture, and clinical experience. The findings may help improve preventive strategies, ergonomic recommendations, and rehabilitation approaches for dental professionals.