IPS e.max CAD and IPS e.max Press : fracture mechanics characterization

Objective: To determine fracture toughness (KIC) and fatigue crack propagation (FCP) parameters for IPS e.max CAD and IPS e.max Press. Materials and methods: For KIC determinations, 20 (6x6x6x12mm) notchless triangular prism (NTP) specimens of IPS e.max CAD and IPS e.max Press were prepared. IPS e.max CAD blocks were cut, ground and then crystallized, while IPS e.max Press ingots were pressed into molds obtained from wax prisms. Each specimen was mounted into a holder and custom grips were used to attach the holder to a computerized universal testing machine (Instron model 4301). The assembly was loaded in tension at a crosshead speed of 0.1mm/min and KIC was calculated based on the recorded maximum load at fracture. Fractured surfaces were characterized using scanning electron microscopy (SEM). The results were statistically analyzed using Weibull statistics and t-test (⍺=0.05). For FCP characterization, a pilot test was done with three Plexiglas NTP samples. A pre-crack was initiated in one of the specimen edges. Several lines were scribed on the side of the specimen to monitor crack propagation. The specimens were mounted in the holder and then attached to custom grips on a servo hydraulic fatigue-testing machine (Instron model 8511). A strain gauge was attached to these grips to monitor crack opening displacement. Each specimen was cyclically loaded in tension (Mode I) in a load range between 1 and 20 N and crack length was monitored and filmed using a high definition video recorder (SONY HDR-XR550V) attached to a microscope (Edmund Scientific Co, Barrington, NJ). Video recording was terminated once catastrophic fracture of the specimen occurred. Cyberlink Power Director and Image J software were used in data analysis. Results: KIC values were significantly higher for IPS e.max Press than IPS e.max CAD. The pilot FCP tests on Plexiglas revealed limitations with regards to the applicability of NTP specimen KIC test to FCP studies due to the presence of a trapezoidal crack front in the specimens. Conclusion: IPS e.max Press is superior to IPS e.max CAD in KIC. Further research should be conducted to evaluate the feasibility of using a trapezoidal crack front in FCP studies.Dentistry, Faculty ofGraduat

Detecting the Second Mesiobuccal Canal in Maxillary Molars in a Saudi Arabian Population: A Micro-CT Study

The aim of this study was to determine MB2 canal detectability in maxillary first and second molars obtained from a Saudi population using micro-CT. Maxillary first (n=35) and second (n=30) molars were scanned with micro-CT technology. The number of canals was recorded, and in case of having more than one canal, the level of extracanal detection was analyzed. The presence of extracanal was categorized based on the level they were first detected. Among the maxillary first molars, 28 (80%) and six (17%) teeth had two and three MB canals, respectively. Among the maxillary second molars, 24 (80%) and four (13%) teeth had two and three MB canals, respectively. The MB2 canal was detected at the chamber floor in 70% and 61% of the maxillary first and second molars, respectively. At 1 mm depth, the MB2 canal was found in 15% and 18% of the maxillary first and second molars, respectively. At 2 mm depth, the MB2 canal was found in 3% and 18% of the maxillary first and second molars, respectively. The remaining teeth had the MB2 canal at levels deeper than 2 mm. The MB2 canal was detected in 97% and 93%% of maxillary first and second molars, respectively. Among them, the MB2 canal could be immediately detected in 70% and 61% of the maxillary first and second molars, respectively, once the pulp chamber is exposed. However, the rest of the MB2 were observed at deeper levels in the root and this requires troughing preparation in the chamber floor

Performance of Artificial Intelligence Models Designed for Diagnosis, Treatment Planning and Predicting Prognosis of Orthognathic Surgery (OGS)—A Scoping Review

The technological advancements in the field of medical science have led to an escalation in the development of artificial intelligence (AI) applications, which are being extensively used in health sciences. This scoping review aims to outline the application and performance of artificial intelligence models used for diagnosing, treatment planning and predicting the prognosis of orthognathic surgery (OGS). Data for this paper was searched through renowned electronic databases such as PubMed, Google Scholar, Scopus, Web of science, Embase and Cochrane for articles related to the research topic that have been published between January 2000 and February 2022. Eighteen articles that met the eligibility criteria were critically analyzed based on QUADAS-2 guidelines and the certainty of evidence of the included studies was assessed using the GRADE approach. AI has been applied for predicting the post-operative facial profiles and facial symmetry, deciding on the need for OGS, predicting perioperative blood loss, planning OGS, segmentation of maxillofacial structures for OGS, and differential diagnosis of OGS. AI models have proven to be efficient and have outperformed the conventional methods. These models are reported to be reliable and reproducible, hence they can be very useful for less experienced practitioners in clinical decision making and in achieving better clinical outcomes

Performance of Artificial Intelligence (AI) Models Designed for Application in Pediatric Dentistry—A Systematic Review

Oral diseases are the most prevalent chronic childhood diseases, presenting as a major public health issue affecting children of all ages in the developing and developed countries. Early detection and control of these diseases is very crucial for a child’s oral health and general wellbeing. The aim of this systematic review is to assess the performance of artificial intelligence models designed for application in pediatric dentistry. A systematic search of the literature was conducted using different electronic databases, primarily (PubMed, Scopus, Web of Science, Embase, Cochrane) and secondarily (Google Scholar and the Saudi Digital Library) for studies published from 1 January 2000, until 20 July 2022, related to the research topic. The quality of the twenty articles that satisfied the eligibility criteria were critically analyzed based on the QUADAS-2 guidelines. Artificial intelligence models have been utilized for the detection of plaque on primary teeth, prediction of children’s oral health status (OHS) and treatment needs (TN); detection, classification and prediction of dental caries; detection and categorization of fissure sealants; determination of the chronological age; determination of the impact of oral health on adolescent’s quality of life; automated detection and charting of teeth; and automated detection and classification of mesiodens and supernumerary teeth in primary or mixed dentition. Artificial intelligence has been widely applied in pediatric dentistry in order to help less-experienced clinicians in making more accurate diagnoses. These models are very efficient in identifying and categorizing children into various risk groups at the individual and community levels. They also aid in developing preventive strategies, including designing oral hygiene practices and adopting healthy eating habits for individuals

Performance of Artificial Intelligence (AI) Models Designed for Application in Pediatric Dentistry—A Systematic Review

Oral diseases are the most prevalent chronic childhood diseases, presenting as a major public health issue affecting children of all ages in the developing and developed countries. Early detection and control of these diseases is very crucial for a child’s oral health and general wellbeing. The aim of this systematic review is to assess the performance of artificial intelligence models designed for application in pediatric dentistry. A systematic search of the literature was conducted using different electronic databases, primarily (PubMed, Scopus, Web of Science, Embase, Cochrane) and secondarily (Google Scholar and the Saudi Digital Library) for studies published from 1 January 2000, until 20 July 2022, related to the research topic. The quality of the twenty articles that satisfied the eligibility criteria were critically analyzed based on the QUADAS-2 guidelines. Artificial intelligence models have been utilized for the detection of plaque on primary teeth, prediction of children’s oral health status (OHS) and treatment needs (TN); detection, classification and prediction of dental caries; detection and categorization of fissure sealants; determination of the chronological age; determination of the impact of oral health on adolescent’s quality of life; automated detection and charting of teeth; and automated detection and classification of mesiodens and supernumerary teeth in primary or mixed dentition. Artificial intelligence has been widely applied in pediatric dentistry in order to help less-experienced clinicians in making more accurate diagnoses. These models are very efficient in identifying and categorizing children into various risk groups at the individual and community levels. They also aid in developing preventive strategies, including designing oral hygiene practices and adopting healthy eating habits for individuals

Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)—A Systematic Review

Evolution in the fields of science and technology has led to the development of newer applications based on Artificial Intelligence (AI) technology that have been widely used in medical sciences. AI-technology has been employed in a wide range of applications related to the diagnosis of oral diseases that have demonstrated phenomenal precision and accuracy in their performance. The aim of this systematic review is to report on the diagnostic accuracy and performance of AI-based models designed for detection, diagnosis, and prediction of dental caries (DC). Eminent electronic databases (PubMed, Google scholar, Scopus, Web of science, Embase, Cochrane, Saudi Digital Library) were searched for relevant articles that were published from January 2000 until February 2022. A total of 34 articles that met the selection criteria were critically analyzed based on QUADAS-2 guidelines. The certainty of the evidence of the included studies was assessed using the GRADE approach. AI has been widely applied for prediction of DC, for detection and diagnosis of DC and for classification of DC. These models have demonstrated excellent performance and can be used in clinical practice for enhancing the diagnostic performance, treatment quality and patient outcome and can also be applied to identify patients with a higher risk of developing DC