Australian Dentist's Knowledge and Perceptions of Factors Affecting Radiographic Interpretation.

BACKGROUND: Errors of interpretation of radigraphic images, also known as interpretive errors, are a critical concern as they can have profound implications for clinical decision making. Different types of interpretive errors, including errors of omission and misdiagnosis, have been described in the literature. These errors can lead to unnecessary or harmful treat/or prolonged patient care. Understanding the nature and contributing factors of interpretive errors is important in developing solutions to minimise interpretive errors. By exploring the knowledge and perceptions of dental practitioners, this study aimed to shed light on the current understanding of interpretive errors in dentistry. METHODS: An anonymised online questionnaire was sent to dental practitioners in New South Wales (NSW) between September 2020 and March 2022. A total of 80 valid responses were received and analysed. Descriptive statistics and bivariate analysis were used to analyse the data. RESULTS: The study found that participants commonly reported interpretive errors as occurring 'occasionally', with errors of omission being the most frequently encountered type. Participants identified several factors that most likely contribute to interpretive errors, including reading a poor-quality image, lack of clinical experience and knowledge, and excessive workload. Additionally, general practitioners and specialists held different views regarding factors affecting interpretive errors. CONCLUSION: The survey results indicate that dental practitioners are aware of the common factors associated with interpretive errors. Errors of omission were identified as the most common type of error to occur in clinical practice. The findings suggest that interpretive errors result from a mental overload caused by factors associated with image quality, clinician-related, and image interpretation. Managing and identifying solutions to mitigate these factors are crucial for ensuring accurate and timely radiographic diagnoses. The findings of this study can serve as a foundation for future research and the development of targeted interventions to enhance the accuracy of radiographic interpretations in dentistry.

Factors affecting interpretation of dental radiographs.

OBJECTIVES: To identify the factors influencing errors in the interpretation of dental radiographs. METHODS: A protocol was registered on Prospero. All studies published until May 2022 were included in this review. The search of the electronic databases spanned Ovid Medline, PubMed, EMBASE, Web of Science and Scopus. The quality of the studies was assessed using the MMAT tool. Due to the heterogeneity of the included studies, a meta-analysis was not conducted. RESULTS: The search yielded 858 articles, of which eight papers met the inclusion and exclusion criteria and were included in the systematic review. These studies assessed the factors influencing the accuracy of the interpretation of dental radiographs. Six factors were identified as being significant that affected the occurrence of interpretation errors. These include clinical experience, clinical knowledge, and technical ability, case complexity, time pressure, location and duration of dental education and training and cognitive load. CONCLUSIONS: The occurrence of interpretation errors has not been widely investigated in dentistry. The factors identified in this review are interlinked. Further studies are needed to better understand the extent of the occurrence of interpretive errors and their impact on the practice of dentistry.

DEEP LEARNING ALGORITHMS SHOW SOME POTENTIAL AS AN ADJUNCTIVE TOOL IN CARIES DIAGNOSIS.

ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION: Mohammad-Rahimi H, Reza Motamedian S, Hossein Rohban M, Krois J, Uribe SE, Mahmoudinia E, Rokhshad R, Nadimi M, Schwendicke F, Deep learning for caries detection: A systematic review, J Dent, 2022,122, 104115. ISSN 0300-5712 https://doi.org/10.1016/j.jdent.2022.104115. SOURCE OF FUNDING: Information not available TYPE OF STUDY/DESIGN: Systematic review.

ARTIFACTS IN MAGNETIC RESONANCE IMAGING CAUSED BY DENTAL MATERIALS: A SYSTEMATIC REVIEW.

ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION: Bohner L, Hanisch M, Sesma N, Blanck-Lubarsch M, Kleinheinz J. Artifacts in magnetic resonance imaging caused by dental materials: a systematic review. Dentomaxillofac Radiol (2022) 10.1259/dmfr.20210450. SOURCE OF FUNDING: Non-profit, Foundations: Open access funding enabled by Projekt DEAL, Germany. TYPE OF STUDY/DESIGN: Systematic review.

Career-computer simulation increases perceived importance of learning about rare diseases.

BACKGROUND: Rare diseases may be defined as occurring in less than 1 in 2000 patients. Such conditions are, however, so numerous that up to 5.9% of the population is afflicted by a rare disease. The gambling industry attests that few people have native skill evaluating probabilities. We believe that both students and academics, under-estimate the likelihood of encountering rare diseases. This combines with pressure on curriculum time, to reduce both student interest in studying rare diseases, and academic content preparing students for clinical practice. Underestimation of rare diseases, may also contribute to unhelpful blindness to considering such conditions in the clinic. METHODS: We first developed a computer simulation, modelling the number of cases of increasingly rare conditions encountered by a cohort of clinicians. The simulation captured results for each year of practice, and for each clinician throughout the entirety of their careers. Four hundred sixty-two theoretical conditions were considered, with prevalence ranging from 1 per million people through to 64.1% of the population. We then delivered a class with two in-class on-line surveys evaluating student perception of the importance of learning about rare diseases, one before and the other after an in-class real-time computer simulation. Key simulation variables were drawn from the student group, to help students project themselves into the simulation. RESULTS: The in-class computer simulation revealed that all graduating clinicians from that class would frequently encounter rare conditions. Comparison of results of the in-class survey conducted before and after the computer simulation, revealed a significant increase in the perceived importance of learning about rare diseases (p < 0.005). CONCLUSIONS: The computer career simulation appeared to affect student perception. Because the computer simulation demonstrated clinicians frequently encounter patients with rare diseases, we further suggest this should be considered by academics during curriculum review and design.