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OBJECTIVE: Clinical cone-beam computed tomography (CBCT) devices are limited to imaging features of half a millimeter in size and cannot quantify the tissue microstructure. We demonstrate a robust deep-learning method for enhancing clinical CT images, only requiring a limited set of easy-to-acquire training data.
METHODS: Knee tissue from five cadavers and six total knee replacement patients, and 14 teeth from eight patients were scanned using laboratory CT as training data for the developed super-resolution (SR) technique. The method was benchmarked against ex vivo test set, 52 osteochondral samples are imaged with clinical and laboratory CT. A quality assurance phantom was imaged with clinical CT to quantify the technical image quality. To visually assess the clinical image quality, musculoskeletal and maxillofacial CBCT studies were enhanced with SR and contrasted to interpolated images. A dental radiologist and surgeon reviewed the maxillofacial images.
RESULTS: The SR models predicted the bone morphological parameters on the ex vivo test set more accurately than conventional image processing. The phantom analysis confirmed higher spatial resolution on the SR images than interpolation, but image grayscales were modified. Musculoskeletal and maxillofacial CBCT images showed more details on SR than interpolation; however, artifacts were observed near the crown of the teeth. The readers assessed mediocre overall scores for both SR and interpolation. The source code and pretrained networks are publicly available.
CONCLUSIONS: Model training with laboratory modalities could push the resolution limit beyond state-of-the-art clinical musculoskeletal and dental CBCT. A larger maxillofacial training dataset is recommended for dental applications.

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.
CONCLUSIONS: 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.

The aim of this study was to evaluate the indications of cone beam computed tomography (CBCT) in children and young individuals in a university-based dental hospital and their association with age, gender and field of view.
7131 CBCT scans, taken during 3-year period, were reviewed and a total of 649 pediatric patients (0-18 years) with complete request forms were included. Data related to gender, age, referring department, CBCT indications, field of view (FOV), region of interest (ROI), need for re-exposure and patients received more than one CBCT examination were recorded.
The mean age was 13.57 ± 3.52 years and \"malocclusion and dentofacial anomaly\" (28.7%) was the most common clinical indication. Facial trauma, dental trauma and supernumerary tooth in males; \"malocclusion and dentofacial anomaly\" and implant planning in females were recorded more frequently compared to other gender. Maxilla was the most frequently monitored ROI (35.1%) for patients. Small (≤ 10 cm) FOV was preferred in 58.1% of all patients. Large FOV was selected in the majority of patients who underwent CBCT scan for \"malocclusion and dentofacial anomaly\" (89.6%). The repeated scans constituted 2.3% of patients and 105 patients (16.2%) underwent multiple CBCT scans on different dates for mainly orthodontic follow-up.
The justification of CBCT scans was not fully compatible with current guidelines and mainly larger FOV was preferred. The number of CBCT examination in children and young individuals tends to increase.
Not applicable.

UNASSIGNED: There is information missing in the literature about the comparison of dentists vs. artificial intelligence (AI) based on diagnostic capability. The aim of this study is to evaluate the diagnostic performance based on radiological diagnoses regarding caries and periapical infection detection by comparing AI software with junior dentists who have 1 or 2 years of experience, based on the valid determinations by specialist dentists.
UNASSIGNED: In the initial stage of the study, 2 specialist dentists evaluated the presence of caries and periapical lesions on 500 digital panoramic radiographs, and the detection time was recorded in seconds. In the second stage, 3 junior dentists and an AI software performed diagnoses on the same panoramic radiographs, and the diagnostic results and durations were recorded in seconds.
UNASSIGNED: The AI and the three junior dentists, respectively, detected dental caries at a sensitivity (SEN) of 0.907, 0.889, 0.491, 0.907; a specificity (SPEC) of 0.760, 0.740, 0.454, 0.696; a positive predictive value (PPV) of 0.693, 0.470, 0.155, 0.666; a negative predictive value (NPV) of 0.505, 0.415, 0.275, 0.367 and a F1-score of 0.786, 0.615, 0.236, 0.768. The AI and the three junior dentists respectively detected periapical lesions at an SEN of 0.973, 0.962, 0.758, 0.958; a SPEC of 0.629, 0.421, 0.404, 0.621; a PPV of 0.861, 0.651, 0.312, 0.648; a NPV of 0.689, 0.673, 0.278, 0.546 and an F1-score of 0.914, 0.777, 0.442, 0.773. The AI software gave more accurate results, especially in detecting periapical lesions. On the other hand, in caries detection, the underdiagnosis rate was high for both AI and junior dentists.
UNASSIGNED: Regarding the evaluation time needed, AI performed faster, on average.

BACKGROUND: Acquiring adequate theoretical knowledge in the field of dental radiography (DR) is essential for establishing a good foundation at the prepractical stage. Currently, nonface-to-face DR education predominantly relies on two-dimensional (2D) videos, highlighting the need for developing educational resources that address the inherent limitations of this method. We developed a virtual reality (VR) learning medium using 360° video with a prefabricated head-mounted display (pHMD) for nonface-to-face DR learning and compared it with a 2D video medium.
METHODS: Forty-four participants were randomly assigned to a control group (n = 23; 2D video) and an experimental group (n = 21; 360° VR). DR was re-enacted by the operator and recorded using 360° video. A survey was performed to assess learning satisfaction and self-efficacy. The nonparametric statistical tests comparing the groups were conducted using SPSS statistical analysis software.
RESULTS: Learners in the experimental group could experience VR for DR by attaching their smartphones to the pHMD. The 360° VR video with pHMD provided a step-by-step guide for DR learning from the point of view of an operator as VR. Learning satisfaction and self-efficacy were statistically significantly higher in the experimental group than the control group (p < 0.001).
CONCLUSIONS: The 360° VR videos were associated with greater learning satisfaction and self-efficacy than conventional 2D videos. However, these findings do not necessarily substantiate the educational effects of this medium, but instead suggest that it may be considered a suitable alternative for DR education in a nonface-to-face environment. However, further examination of the extent of DR knowledge gained in a nonface-to-face setting is warranted. Future research should aim to develop simulation tools based on 3D objects and also explore additional uses of 360° VR videos as prepractical learning mediums.

BACKGROUND: Two-dimensional (2D) radiographs are the standard of care for diagnosis and treatment planning in the day-to-day practice of dentistry. With the growing popularity of cone beam computed tomography (CBCT), it is now becoming the standard of care in many areas of general dentistry due to its ability to create non-linear projections from volumetric data. The CBCT-generated non-orthogonal radiographs can serve as easy-to-use 2D and three-dimensional (3D) diagnostic tools and offer a similar experience for diagnosis as conventional 2D images. The aim of this study is to compare the accuracy of conventional radiographs and CBCT-generated projections to identify relevant anatomic landmarks and their associated variants.
METHODS: Thirty-two patients referred to the University of Connecticut School of Dental Medicine\'s Advanced Imaging Center were selected for this retrospective analysis. Nineteen anatomical landmarks were retrospectively assessed on conventional panoramic and CBCT scans generated panoramic radiographs using two different digital imaging and communications in medicine viewers. A total of 1,216 anatomical landmarks were evaluated by two oral and maxillofacial radiologists to assess the accuracy and consistency of the depiction of radiographic anatomy.
RESULTS: There was a very good agreement between the two evaluators with a Cohen\'s kappa value of 0.934. McNemar change test concluded that the anatomical assessment values compared between conventional panoramic and CBCT-generated panoramic radiographs are similar.
CONCLUSIONS: This study showed that CBCT-generated panoramic images are comparable to conventional panoramic radiographs in identifying anatomical landmarks typically evaluated using a conventional panoramic projection. In addition, they have the added advantage of having 3D information in the acquired volume to better evaluate the area of interest. In clinical situations where a mid- to large-volume CBCT scan is available, a simulated panoramic image can be generated using the CBCT volume, leaving exposure of the patient to the additional radiation of a panoramic image unnecessary.