METHODS: This study enrolled patients (N = 36) who required root canal retreatment (RCR) on mandibular molar teeth, presented with periapical lesions with periapical index scores of 2 or 3, and had a pain visual analog scale (VAS) <50 and a percussion pain VAS <50. The participants were divided into 2 groups: (1) patients scheduled for RCR followed by LLLT (n = 18) and (2) patients scheduled for RCR followed by a mock LLLT (placebo) (n = 18). Postoperative pain was assessed using the VAS. Data were collected and statistically analyzed with the chi-square test, the independent sample t test, and the Mann-Whitney U test (P = .05).
RESULTS: On the first 4 days, postoperative pain significantly reduced in the LLLT group compared with the placebo group (P .05). The number of patients who needed analgesics was lower in the LLLT group than in the placebo group (P
Materials and Methods: An audit at the department of endodontics at dental specialty centre kingdom of Saudi Arabia was carried out. The audit was conducted by developing endodontics treatment and success predictors based on evidence, that can be measured for endodontic care. A total of 12 months' data was examined from the previous dental records. Ten clinical cards were which included root canal treatment were selected. The audit was carried out for a minimum of 50 teeth and a maximum of 200 teeth. The radiographs of record cards were studied and a single dentist completed the audit tool.
Results: The vitality test was performed in 1.98% cases, intra-canal medicament was used and named in 3.96% cases, 3.96% the teeth were extracted due to endodontic failure. Further, in 6.93% of the cases that were identified had certain spaces but overall root canal filling was evaluated as satisfactory.
Conclusion: The vitality test, type of intracanal medicament, and assessment of root canal filling were not done, but there was an overall performance of predictors for endodontic treatment.
INTRODUCTION: Artificial intelligence (AI) is a relatively new technology that has widespread use in dentistry. The AI technologies have primarily been used in dentistry to diagnose dental diseases, plan treatment, make clinical decisions, and predict the prognosis. AI models like convolutional neural networks (CNN) and artificial neural networks (ANN) have been used in endodontics to study root canal system anatomy, determine working length measurements, detect periapical lesions and root fractures, predict the success of retreatment procedures, and predict the viability of dental pulp stem cells. Methodology. The literature was searched in electronic databases such as Google Scholar, Medline, PubMed, Embase, Web of Science, and Scopus, published over the last four decades (January 1980 to September 15, 2021) by using keywords such as artificial intelligence, machine learning, deep learning, application, endodontics, and dentistry.
RESULTS: The preliminary search yielded 2560 articles relevant enough to the paper's purpose. A total of 88 articles met the eligibility criteria. The majority of research on AI application in endodontics has concentrated on tracing apical foramen, verifying the working length, projection of periapical pathologies, root morphologies, and retreatment predictions and discovering the vertical root fractures.
CONCLUSION: In endodontics, AI displayed accuracy in terms of diagnostic and prognostic evaluations. The use of AI can help enhance the treatment plan, which in turn can lead to an increase in the success rate of endodontic treatment outcomes. The AI is used extensively in endodontics and could help in clinical applications, such as detecting root fractures, periapical pathologies, determining working length, tracing apical foramen, the morphology of root, and disease prediction.