MATERIALS AND METHODS: Sixty maxillary central incisors were divided into Group I, II, and III with 20 samples each based on luting cement used. They were OF, self-adhesive (SA) cement, and total etch (TE) cement. These groups were subdivided into "a" and "b" of ten each based on the type of veneering materials used. Veneer discs were fabricated using Ormocer restorative (O) and pressable ceramic (C). Specimens were thermocycled and loaded under universal testing machine for SBS. The statistical analysis was done using one-way ANOVA post hoc Tukey honest significant difference method.
RESULTS: A significant difference was observed between the Groups I and II (P < 0.05). The highest mean bond strength when using ormocer veneer was obtained with the Group Ia (19.11 ± 1.92 Mpa) and lowest by Group IIa (8.1 ± 1.04 Mpa), whereas the highest mean bond strength while using ceramic veneer was of similar range for Group Ib (18.04 ± 4.08 Mpa) and Group IIIb (18.07 ± 1.40 Mpa). SEM analysis revealed OF and TE presented mixed type of failure when compared with SA where failure mode was totally adhesive.
CONCLUSION: OF was found equally efficient like TE. Bond strength of ormocer as a veneer was not inferior to ceramic making it one of the promising additions in the field of dentistry.
Materials and Methods: Using the MeSH keywords: artificial intelligence (AI), dentistry, AI in dentistry, neural networks and dentistry, machine learning, AI dental imaging, and AI treatment recommendations and dentistry. Two investigators performed an electronic search in 5 databases: PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). The English language articles reporting on AI in different dental specialties were screened for eligibility. Thirty-two full-text articles were selected and systematically analyzed according to a predefined inclusion criterion. These articles were analyzed as per a specific research question, and the relevant data based on article general characteristics, study and control groups, assessment methods, outcomes, and quality assessment were extracted.
Results: The initial search identified 175 articles related to AI in dentistry based on the title and abstracts. The full text of 38 articles was assessed for eligibility to exclude studies not fulfilling the inclusion criteria. Six articles not related to AI in dentistry were excluded. Thirty-two articles were included in the systematic review. It was revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making. Artificial intelligence appeared as a reliable modality to enhance future implications in the various fields of dentistry, i.e., diagnostic dentistry, patient management, head and neck cancer, restorative dentistry, prosthetic dental sciences, orthodontics, radiology, and periodontics.
Conclusion: The included studies describe that AI is a reliable tool to make dental care smooth, better, time-saving, and economical for practitioners. AI benefits them in fulfilling patient demand and expectations. The dentists can use AI to ensure quality treatment, better oral health care outcome, and achieve precision. AI can help to predict failures in clinical scenarios and depict reliable solutions. However, AI is increasing the scope of state-of-the-art models in dentistry but is still under development. Further studies are required to assess the clinical performance of AI techniques in dentistry.
Materials and Methods: A retrospective study involved endodontically treated teeth of patients attending the Polyclinic, Kulliyyah of Dentistry, IIUM, from 2012 to 2015. Clinical and radiographic data were recorded and classified as successful or failed, and further analyzed by Fisher's exact test to measure the correlation between the variables using SPSS software version 16.0. Kappa test was used to measure the overall relationship between clinical and radiographic findings.
Results: A total of sixty teeth were evaluated clinically and radiographically, the overall success rate was 85% (n = 51). Correlation between the variables showed nonsignificant (P > 0.05) in the success rate among age, gender, and race, upper and lower arches and between anterior and posterior teeth at the time of treatment. At postendodontic fixed restorations, the variables showed statistically significant relationship with the success rate (P < 0.05).
Conclusions: Patients with no signs and symptoms and with no radiographical changes at the the time of clinical examination, showed the highest percentage of success rate (85%) of postendodontic fixed restorations. Age, gender, and race have no significant relations with the success rate of endodontically treated teeth.