AimThis investigation was conducted to assess the ability of various irrigant agitation devices to eradicate Enterococcus faecalis from the dentinal tubules of extracted teeth.MethodologyFifty roots of extracted human teeth were instrumented to size 30 k with a 0.04 taper. The roots were autoclaved and then injected with E. faecalis. The canals were assigned to one of four intervention groups and disinfected using (A) standard needle irrigation, (B) EndoUltra® Ultrasonic Activator, (C) the EndoActivator system, or (D) EDDY sonic activation and to two control groups that were (E) treated with saline and (F) not inoculated with any bacteria. The roots were split in half, dyed with a LIVE/DEAD Back Light Bacterial Viability Kit, and then scanned with a confocal laser scanning microscope (CLSM) to identify live/dead bacteria in the dentinal tubules.ResultsCLSM images revealed differences among the groups. Both the EndoUltra® Ultrasonic Activator group and the EDDY group had a combination of dead and live bacteria, while the EndoActivator group had mostly dead bacteria, in contrast to single needle irrigation which had mostly live bacteria. Activation of the irrigating solution resulted in more dead bacteria than standard needle irrigation at the coronal, middle, and apical parts of the roots. Overall, the EndoActivator system was superior to all other techniques in reducing live bacteria within the root canal.ConclusionActivation of sodium hypochlorite with sonic and ultrasonic systems dramatically reduced live bacteria contamination in the dentinal tubules of infected root canals.
Aim. This comprehensive review is aimed at evaluating the diagnostic and prognostic accuracy of artificial intelligence in endodontic dentistry. 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.
Background: Endodontic treatment failure is one of the most common problems encountered in dentistry. Aims: This study aimed to evaluate the causes of failure of endodontic treatment among patients in the Saudi Arabian city of Al-Kharj. Subjects and Methods: A total of 250 patients of both genders were involved in the study. Criteria confirming the failure of the endodontic treatment were pain, tenderness on pressure, periapical radiolucency, and sinus tract. Patients were selected by convenience sampling methods. A diagnostic chart was prepared to complete the investigation in three different hospitals, during a six-month period (October 2018 - March 2019). The results were analyzed statistically using Chi-square test and use of simple arithmetical methods to determine percentage and frequencies. Results: The main cause for endodontic failure was poor quality adjunctive treatment. 147 out of 179 male patients and 53 out of 71 female patients received poor quality treatment. There was a statistically significant difference between gender versus adjunctive treatment (P = 0.009) and between hospital versus adjunctive treatment (P = 0.005), and quality of adjunctive treatment between private hospital as compared to government hospital which was also statistically significant (P = 0.008). In quadrant wise distribution, first molars were the most commonly involved teeth. Inadequate filling of the root canal was (36.8%), missed canals (14.4), over-extension root canal fillings (12.8%), perforations (9.6%), instrumentation related (8.8%), and endodontic access preparation related (2.4%) in the decreasing order of frequency were seen as the most common causes of failure of endodontic treatment. Conclusion: First molars were the most commonly affected tooth in the failure of endodontic treatment. Poor adjunctive treatment and inadequate filling of the root canals were the most common causes of endodontic failure, more commonly seen in male than female patients and in private clinics/hospitals than government hospitals.
This study aimed to investigate variations in the root canal morphology of maxillary second premolar (MSP) teeth using microcomputed tomography (micro-CT). Sixty (N = 60) human extracted MSPs were collected and prepared for micro-CT scanning. The duration for scanning a single sample ranged between 30 and 40 min and a three-dimensional (3-D) image was obtained for all the MSPs. The images were evaluated by a single observer who recorded the canal morphology type, number of roots, canal orifices, apical foramina(s), apical delta(s), and accessory canals. The root canal configuration was categorized in agreement with Vertucci’s classification, and any configuration not in agreement with Vertucci’s classification was reported as an “additional canal configuration”. Descriptive statistics (such as mean percentages) were calculated using SPSS software. The most common types agreeing with Vertucci’s classification (in order of highest to lowest incidence) were types I, III, V, VII, II, and VI. The teeth also exhibited four additional configurations that were different from Vertucci’s classification: types 2-3, 1-2-3, 2-1-2-1, and 1-2-1-3. A single root was found in 96.7% and the majority of the samples demonstrated two canals (73.3%). Further, 80% of the teeth showed one canal orifice. The number of apical foramina’s in the teeth was variable, with 56.7% having solitary apical foramen. The accessory canal was found in 33.3%, and apical delta was found in only 20% of the samples. Variable morphology of the MSPs was detected in our study. The canal configuration most prevalent was type 1; however, the results also revealed some additional canal types.
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