Identification of dental implant systems from low-quality and distorted dental radiographs using AI trained on a large multi-center dataset.

Most artificial intelligence (AI) studies have attempted to identify dental implant systems (DISs) while excluding low-quality and distorted dental radiographs, limiting their actual clinical use. This study aimed to evaluate the effectiveness of an AI model, trained on a large and multi-center dataset, in identifying different types of DIS in low-quality and distorted dental radiographs. Based on the fine-tuned pre-trained ResNet-50 algorithm, 156,965 panoramic and periapical radiological images were used as training and validation datasets, and 530 low-quality and distorted images of four types (including those not perpendicular to the axis of the fixture, radiation overexposure, cut off the apex of the fixture, and containing foreign bodies) were used as test datasets. Moreover, the accuracy performance of low-quality and distorted DIS classification was compared using AI and five periodontists. Based on a test dataset, the performance evaluation of the AI model achieved accuracy, precision, recall, and F1 score metrics of 95.05%, 95.91%, 92.49%, and 94.17%, respectively. However, five periodontists performed the classification of nine types of DISs based on four different types of low-quality and distorted radiographs, achieving a mean overall accuracy of 37.2 ± 29.0%. Within the limitations of this study, AI demonstrated superior accuracy in identifying DIS from low-quality or distorted radiographs, outperforming dental professionals in classification tasks. However, for actual clinical application of AI, extensive standardization research on low-quality and distorted radiographic images is essential.

Assessment of Perception of Pain with E-Speed Film, CCD Sensor and Photostimulable Phosphor Plates for Intraoral Radiographs in Children using Three Pain Rating Scales.

BACKGROUND: Dental radiography is an integral part of intraoral evaluation. Children are often uncomfortable during the placement of film or sensor due to the impingement of the soft tissues. Thus, the perception of pain with three intraoral radiographic methods in children was evaluated using three subjective pain rating scales. AIM: To evaluate the discomfort with three different techniques, that is, intraoral periapical (IOPA) radiograph, charge-coupled device (CCD), and photostimulable phosphor (PSP) luminescence (PSPL), using the Wong-Baker Faces Pain Rating Scale (WBFPRS), numerical rating scale, and visual analog scale (VAS). MATERIALS AND METHODS: A sample of 35 children aged 6-12 years were divided into two groups: group 1 (6-8 years) and group 2 (9-12 years). For each child, simulations of the three radiological methods (IOPA, CCD, and PSPL) were performed. The meaning of each facial expression on the WBFPRS, VAS, and the numbers on the numerical rating scale was explained to each child before the procedure. STATISTICAL ANALYSIS USED: A one-way analysis of variance (ANOVA) test and paired-samples t-test are used. RESULTS: The results revealed that the CCD sensors elicited higher pain scores than those obtained with IOPA and PSPL, whereas the IOPA film showed the least pain score. Higher score values were obtained in group 1 than in group 2, indicating that children aged 6-8 years felt higher discomfort than the 9- to 12-year age group for the same procedure. This difference was statistically significant (P < 0.001). CONCLUSION: It was concluded that conventional IOPA films were tolerated better by children when compared to PSP plates and CCD sensors.

Deep learning for determining the difficulty of endodontic treatment: a pilot study.

BACKGROUND: To develop and validate a deep learning model for automated assessment of endodontic case difficulty from periapical radiographs. METHODS: A dataset of 1,386 periapical radiographs was compiled from two clinical sites. Two dentists and two endodontists annotated the radiographs for difficulty using the "simple assessment" criteria from the American Association of Endodontists' case difficulty assessment form in the Endocase application. A classification task labeled cases as "easy" or "hard", while regression predicted overall difficulty scores. Convolutional neural networks (i.e. VGG16, ResNet18, ResNet50, ResNext50, and Inception v2) were used, with a baseline model trained via transfer learning from ImageNet weights. Other models was pre-trained using self-supervised contrastive learning (i.e. BYOL, SimCLR, MoCo, and DINO) on 20,295 unlabeled dental radiographs to learn representation without manual labels. Both models were evaluated using 10-fold cross-validation, with performance compared to seven human examiners (three general dentists and four endodontists) on a hold-out test set. RESULTS: The baseline VGG16 model attained 87.62% accuracy in classifying difficulty. Self-supervised pretraining did not improve performance. Regression predicted scores with ± 3.21 score error. All models outperformed human raters, with poor inter-examiner reliability. CONCLUSION: This pilot study demonstrated the feasibility of automated endodontic difficulty assessment via deep learning models.

Efficacy of electronic apex locators in comparison with intraoral radiographs in working length determination- a systematic review and meta-analysis.

BACKGROUND: Successful endodontic treatment needs accurate determination of working length (WL). Electronic apex locators (EALs) were presented as an alternative to radiographic methods; and since then, they have evolved and gained popularity in the determination of WL. However, there is insufficient evidence on the post-operative pain, adequacy, and accuracy of EALs in determining WL. OBJECTIVE: The systematic review and meta-analysis aims to gather evidence regarding the effectiveness of EALs for WL determination when compared to different imaging techniques along with postoperative pain associated with WL determination, the number of radiographs taken during the procedure, the time taken, and the adverse effects. METHODS: For the review, clinical studies with cross-over and parallel-arm randomized controlled trials (RCTs) were searched in seven electronic databases, followed by cross-referencing of the selected studies and related research synthesis. Risk of bias (RoB) assessment was carried out with Cochrane's RoB tool and a random-effects model was used. The meta-analysis was performed with the RevMan software 5.4.1. RESULTS: Eleven eligible RCTs were incorporated into the review and eight RCTs into the meta-analysis, of which five had high RoB and the remaining six had unclear RoB. Following meta-analysis, no significant difference in postoperative pain was found among the EAL and radiograph groups (SMD 0.00, CI .29 to .28, 354 participants; P value = 0.98). Radiograph group showed better WL accuracy (SMD 0.55, CI .11 to .99, 254 participants; P value = 0.02), while the EAL group had 10% better WL adequacy (RR 1.10, CI 1.03-1.18, 573 participants; P value = 0.006). CONCLUSION: We found very low-certainty evidence to support the efficacy of different types of EAL compared to radiography for the outcomes tested. We were unable to reach any conclusions about the superiority of any type of EAL. Well-planned RCTs need to be conducted by standardizing the outcomes and outcome measurement methods.

Radiation exposure during orthodontic treatment: risk to children and adolescents.

OBJECTIVES: To estimate radiation risk to children and adolescents during orthodontic treatment by retrieving number and type of radiographs from the patient records. MATERIAL AND METHODS: Radiographs, along with justifications for radiation exposure, were obtained retrospectively from the patient records of 1,790 children and adolescents referred to two Swedish orthodontic clinics. Data were grouped according to treatment stage: treatment planning, treatment, and follow-up. Estimated risk was calculated using the concept of effective dose. RESULTS: Each patient had received around seven radiographs for orthodontic purposes. The most common exposures during treatment planning were one panoramic, one lateral, and three intraoral periapical radiographs. A small number of patients received a tomographic examination (8.2%). Few justifications for treatment planning and follow-up, but more in the actual treatment stage, had been recorded. The most common examinations were to assess root resorption and the positions of unerupted teeth, or simply carry out an unspecified control. The estimated risk of developing fatal cancer was considered low. The radiation risk from orthodontic treatment was equivalent to about 5-10 days of natural background radiation. CONCLUSIONS: Children and adolescents sometimes undergo multiple radiographic examinations, but despite the low radiation burden, accumulated radiation exposure should be considered and justified in young patients.

An Artificial Intelligence model for implant segmentation on periapical radiographs.

OBJECTIVE: To segment dental implants on PA radiographs using a Deep Learning (DL) algorithm. To compare the performance of the algorithm relative to ground truth determined by the human annotator. Methodology: Three hundred PA radiographs were retrieved from the radiographic database and consequently annotated to label implants as well as teeth on the LabelMe annotation software. The dataset was augmented to increase the number of images in the training data and a total of 1294 images were used to train, validate and test the DL algorithm. An untrained U-net was downloaded and trained on the annotated dataset to allow detection of implants using polygons on PA radiographs. RESULTS: A total of one hundred and thirty unseen images were run through the trained U-net to determine its ability to segment implants on PA radiographs. The performance metrics are as follows: accuracy of 93.8%, precision of 90%, recall of 83%, F-1 score of 86%, Intersection over Union of 86.4% and loss = 21%. CONCLUSIONS: The trained DL algorithm segmented implants on PA radiographs with high performance similar to that of the humans who labelled the images forming the ground truth.

A retrospective clinical study to compare the ability of cone-beam computed tomographic images and periapical radiographs to reveal cracked teeth, split teeth, and teeth with vertical root fractures.

BACKGROUND: This retrospective clinical study aimed to compare the sensitivity of cone-beam computed tomographic (CBCT) images and periapical (PA) radiographs to reveal cracked teeth, split teeth, and teeth with vertical root fractures (VRFs). METHODS: The authors included 98 patients (98 teeth) diagnosed with a longitudinal tooth fracture (LTF) (cracked tooth, split tooth, VRF) through direct visualization after extraction and with comprehensive clinical and radiographic records. They collected demographic, clinical, and radiographic data. The authors evaluated PA radiographs and CBCT images to identify fractures, fracture lines, and the different patterns of bone loss associated with these teeth. They used the McNemar test to compare PA radiographs and CBCT scans when assessing bone loss. They used the Fisher test to determine statistical relationships between fracture types and demographic, clinical, and radiologic traits. They used an analysis of variance test to compare patient age with fracture types. RESULTS: CBCT images were significantly more effective (P < .05) in detecting bone loss patterns associated with LTFs than with PA radiographs, with 71% of cases detected via CBCT images compared with 42% via radiographs. Mean age was significantly greater (P < .05) in patients with teeth with VRFs than in patients with split teeth. A significant relationship was observed between the type of fracture and the following variables: root canal treatment (split, VRF, P = .002), deep probing depth (≥ 5 mm) (VRF, P = .026), and having more than 8 teeth extracted from the mouth (VRF, P = .032). Overall, there was a significant difference (P < .001) between the visualization of fracture lines (45% on PA radiographs, 65% on CBCT images). CONCLUSIONS: CBCT scans provided more information on LTFs than PA radiographs, particularly in the identification of periradicular bone changes. PRACTICAL IMPLICATIONS: CBCT imaging can assist in making the clinical diagnosis of LTFs through observation of bone loss patterns, providing more information than PA radiographs.

Artificial intelligence in age and sex determination using maxillofacial radiographs: A systematic review.

In the past few years, there has been an enormous increase in the application of artificial intelligence and its adoption in multiple fields, including healthcare. Forensic medicine and forensic odontology have tremendous scope for development using AI. In cases of severe burns, complete loss of tissue, complete or partial loss of bony structure, decayed bodies, mass disaster victim identification, etc., there is a need for prompt identification of the bony remains. The mandible, is the strongest bone of the facial region, is highly resistant to undue mechanical, chemical or physical impacts and has been widely used in many studies to determine age and sexual dimorphism. Radiographic estimation of the jaw bone for age and sex is more workable since it is simple and can be applied equally to both dead and living cases to aid in the identification process. Hence, this systematic review is focused on various AI tools for age and sex determination in maxillofacial radiographs. The data was obtained through searching for the articles across various search engines, published from January 2013 to March 2023. QUADAS 2 was used for qualitative synthesis, followed by a Cochrane diagnostic test accuracy review for the risk of bias analysis of the included studies. The results of the studies are highly optimistic. The accuracy and precision obtained are comparable to those of a human examiner. These models, when designed with the right kind of data, can be of tremendous use in medico legal scenarios and disaster victim identification.

Validation of artificial intelligence application for dental caries diagnosis on intraoral bitewing and periapical radiographs.

OBJECTIVES: This study aimed to assess the reliability of AI-based system that assists the healthcare processes in the diagnosis of caries on intraoral radiographs. METHODS: The proximal surfaces of the 323 selected teeth on the intraoral radiographs were evaluated by two independent observers using an AI-based (Diagnocat) system. The presence or absence of carious lesions was recorded during Phase 1. After 4 months, the AI-aided human observers evaluated the same radiographs (Phase 2), and the advanced convolutional neural network (CNN) reassessed the radiographic data (Phase 3). Subsequently, data reflecting human disagreements were excluded (Phase 4). For each phase, the Cohen and Fleiss kappa values, as well as the sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy of Diagnocat, were calculated. RESULTS: During the four phases, the range of Cohen kappa values between the human observers and Diagnocat were κ=0.66-1, κ=0.58-0.7, and κ=0.49-0.7. The Fleiss kappa values were κ=0.57-0.8. The sensitivity, specificity and diagnostic accuracy values ranged between 0.51-0.76, 0.88-0.97 and 0.76-0.86, respectively. CONCLUSIONS: The Diagnocat CNN supports the evaluation of intraoral radiographs for caries diagnosis, as determined by consensus between human and AI system observers. CLINICAL SIGNIFICANCE: Our study may aid in the understanding of deep learning-based systems developed for dental imaging modalities for dentists and contribute to expanding the body of results in the field of AI-supported dental radiology..

Relato de caso clínico de infecção odontogênica resultando em complicação cervicofacial / Reporte de caso clínico de infección odontogênica com resultado de complicación cervicofacial / Clinical case reportof odontogenic infectionresulting cervicofacial complication

Introdução: A saúde bucal é um aspecto que não deve ser subestimado pelos pacientes, principalmente se considerar que as infecções odontogênicas podem levar a quadros graves, incluindo complicações cervicotorácicas, como Mediastinite e cervicofaciais, como Angina de Ludwig. Para tanto, é imprescindível que os profissionais da odontologia saibam reconhecer os principais sinais e sintomas dessas infecções, sua evolução, conhecer as complicações associadas e qual o manejo adequado. Objetivo: Assim, é objetivo deste trabalho, relatar, discutir um caso clínico de uma infecção odontogênica grave que acarretou em complicação cervical, com trajeto em direção ao mediastino, necessitando manejo multidisciplinar, e explorar os principais aspectos desse quadro e a conduta necessária, que exige, no mínimo, intervenção cirúrgica, antibioticoterapia e manutenção das vias aéreas. Relato de caso: O caso trata de um paciente com infecção odontogênica, iniciada como uma pericoronarite do dente 38 semieruptado, que evoluiu para a área cervical, demandando imediata drenagem nesta região pois encaminhava-se para uma mediastinite. Após a drenagem cervical e antibioticoterapia e, assim que houve redução do trismo, foi removido o dente 38, evoluindo para a cura.Conclusões:As infecções odontogênicas, principalmente as que acometem os espaços fasciais e cervicais profundos, são potencialmente graves e devem ter suas principais manifestações clínicas entre os domínios de conhecimento dos profissionais Bucomaxilofaciais, pois necessitam de diagnóstico preciso, manejo rápido e tratamento adequado e precoce, considerando a velocidade com que podem evoluir (AU).

Introduction: Oral healthis an aspect that should not be underestimated by patients, especially considering that dental infections can lead to serious symptoms, including cervicothoracic complications, such as Mediastinitis and cervicofacial complications, such as Ludwig's Angina. Therefore, it is essential that dental professionals know how to recognize the main signs and symptoms of these infections, their evolution, know the associated complications and appropriate management.Objective: Thus, this work aims to report and discuss a clinical case of a serious odontogenic infection that resulted in a cervical complication, with a path towards the mediastinum, requiring multidisciplinary management, and to explore the main aspects of this condition and the necessary conduct, which requires, at least, surgical intervention, antibiotic therapy and airway maintenance.Case report: The case concerns a patient with odontogenic infection, which began as pericoronitis of semi-erupted tooth 38, which progressed to the cervical area, requiring immediate drainage in this region as it was heading towards mediastinitis. After cervical drainage and antibiotic therapy and, as soon as the trismus was reduced, tooth 38 was removed, progressing towards healing.Conclusions: Odontogenic infections, especially those that affect the fascial and deep cervical spaces, are potentially serious and should have their main clinical manifestations among the domains of knowledge ofOral and Maxillofacial professionals, as they require accurate diagnosis, rapid management and adequate and early treatment, considering the speed at which they can evolve (AU).

Introducción: La salud bucal es un aspecto que los pacientes no deben subestimar, especialmente considerando que las infecciones odontógenas pueden derivar en afecciones graves, incluidas complicaciones cervicotorácicas, como la mediastinitis, y complicaciones cervicofaciales, como la angina de Ludwig.Para ello, es fundamental que los profesionales odontológicos sepan reconocer las principales señalesy síntomas de estas infecciones, su evolución, conocer las complicaciones asociadas y el manejo adecuado.Objetivo: Así,el objetivo de este trabajo es reportar y discutir un caso clínico de infección odontogénica grave que resultó en una complicación cervical, con trayecto hacia el mediastino, que requirió manejo multidisciplinario, y explorar los principales aspectos de esta condicióny las medidas necesarias, que requiere, como mínimo, intervención quirúrgica, terapia con antibióticos y mantenimiento de las vías respiratorias.Reporte de caso: El caso se trata de un paciente con una infección odontogénica, que comenzó como pericoronaritis del diente 38 semi-erupcionado, la cual progresó hacia la zona cervical, requiriendo drenaje inmediato en esta región ya que se encaminaba para una mediastinitis.Después del drenaje cervical y la terapia antibiótica y, una vez reducido el trismo, se extrajo el diente 38, evolucijjonando hacia la cura.Conclusiones: Las infecciones odontogénicas, especialmente aquellas que afectan los espacios fasciales y cervicales profundos, son potencialmente graves y deben tener sus principales manifestaciones clínicas entre los dominios del conocimiento de los profesionales Orales y Maxilofaciales, pues requieren de un diagnóstico certero, un manejo rápido y un tratamiento adecuado y temprano, considerando la velocidad a la que pueden evolucionar (AU).

Artificial intelligence-assisted full-mouth radiograph mounting in dental education.

OBJECTIVES: With the increasing prevalence of artificial intelligence (AI) and the significant research gap in the application of AI within dentistry, this study aimed to (1) evaluate the efficiency and accuracy of dental students in full-mouth radiograph series (FMS) mounting with and without AI assistance, and (2) assess dental students' perceptions of AI in clinical education to address the impact of AI in dental education. METHODS: An AI-based interface for mounting radiographs on FMS templates was designed and implemented in the study. Forty third-year dental students were randomly assigned to control and test groups. The control group manually mounted FMS radiographs, while the test group reviewed AI-pre-mounted radiographs for adjustments. Students' performance in efficiency and accuracy was evaluated. Pre- and post-study surveys were conducted to gauge students' confidence levels and opinions regarding the usefulness of the AI-assisted program. RESULTS: The test group (using AI) demonstrated significantly faster radiograph mounting times than the control group (manual) (p < 0.05). Accuracy was lower in the test groups, when comparing AI-assisted and manual mounting of FMS (p < 0.01). Self-confidence and confidence in AI were consistent between the control and test groups, both before and after the study. CONCLUSION: Students with AI presented with a decreased accuracy in FMS radiograph mounting. Therefore, AI automation could potentially have negative impacts in a learning environment with inexperienced clinicians.

Performance comparison of multifarious deep networks on caries detection with tooth X-ray images.

OBJECTIVES: Deep networks have been preliminarily studied in caries diagnosis based on clinical X-ray images. However, the performance of different deep networks on caries detection is still unclear. This study aims to comprehensively compare the caries detection performances of recent multifarious deep networks with clinical dentist level as a bridge. METHODS: Based on the self-collected periapical radiograph dataset in clinic, four most popular deep networks in two types, namely YOLOv5 and DETR object detection networks, and UNet and Trans-UNet segmentation networks, were included in the comparison study. Five dentists carried out the caries detection on the same testing dataset for reference. Key tooth-level metrics, including precision, sensitivity, specificity, F1-score and Youden index, were obtained, based on which statistical analysis was conducted. RESULTS: The F1-score order of deep networks is YOLOv5 (0.87), Trans-UNet (0.86), DETR (0.82) and UNet (0.80) in caries detection. A same ranking order is found using the Youden index combining sensitivity and specificity, which are 0.76, 0.73, 0.69 and 0.64 respectively. A moderate level of concordance was observed between all networks and the gold standard. No significant difference (p > 0.05) was found between deep networks and between the well-trained network and dentists in caries detection. CONCLUSIONS: Among investigated deep networks, YOLOv5 is recommended to be priority for caries detection in terms of its high metrics. The well-trained deep network could be used as a good assistance for dentists to detect and diagnose caries. CLINICAL SIGNIFICANCE: The well-trained deep network shows a promising potential clinical application prospect. It can provide valuable support to healthcare professionals in facilitating detection and diagnosis of dental caries.

Optimizing radiation safety in dentistry: Clinical recommendations and regulatory considerations.

BACKGROUND: The value of dental radiographs to oral health care decision making must be balanced with radiation safety to minimize patient exposure and occupational risk of oral health care providers. This review summarizes recommendations and regulatory guidance regarding dental radiography and cone-beam computed tomography. An expert panel presents recommendations on radiation safety, appropriate imaging practices, and reducing radiation exposure. TYPES OF STUDIES REVIEWED: A systematic search run in Ovid MEDLINE, Embase, and Cochrane Database of Systematic Reviews identified relevant topical systematic reviews, organizational guidelines, and regulatory reviews published in the peer-reviewed literature since 2010. A supplemental search of the gray literature (eg, technical reports, standards, and regulations) identified topical nonindexed publications. Inclusion criteria required relevance to primary oral health care (ie, general or pediatric dentistry). RESULTS: A total of 95 articles, guidance documents, and regulations met the inclusion criteria. Resources were characterized as applicable to all modalities, operator and occupational protection, dose reduction and optimization, and quality assurance and control. PRACTICAL IMPLICATIONS: Understanding factors affecting imaging safety and applying fundamental principles of radiation protection consistent with federal, state, and local requirements are essential for limiting patient ionizing radiation exposure, in conjunction with implementing optimal imaging procedures to support prudent use of dental radiographs and cone-beam computed tomographic imaging. The regulatory guidance and best practice recommendations summarized in this article should be followed by dentists and other oral health care providers.

Convolutional neural networks combined with classification algorithms for the diagnosis of periodontitis.

OBJECTIVES: We aim to develop a deep learning model based on a convolutional neural network (CNN) combined with a classification algorithm (CA) to assist dentists in quickly and accurately diagnosing the stage of periodontitis. MATERIALS AND METHODS: Periapical radiographs (PERs) and clinical data were collected. The CNNs including Alexnet, VGG16, and ResNet18 were trained on PER to establish the PER-CNN models for no periodontal bone loss (PBL) and PBL. The CAs including random forest (RF), support vector machine (SVM), naive Bayes (NB), logistic regression (LR), and k-nearest neighbor (KNN) were added to the PER-CNN model for control, stage I, stage II and stage III/IV periodontitis. Heat map was produced using a gradient-weighted class activation mapping method to visualize the regions of interest of the PER-Alexnet model. Clustering analysis was performed based on the ten PER-CNN scores and the clinical characteristics. RESULTS: The accuracy of the PER-Alexnet and PER-VGG16 models with the higher performance was 0.872 and 0.853, respectively. The accuracy of the PER-Alexnet + RF model with the highest performance for control, stage I, stage II and stage III/IV was 0.968, 0.960, 0.835 and 0.842, respectively. Heat map showed that the regions of interest predicted by the model were periodontitis bone lesions. We found that age and smoking were significantly related to periodontitis based on the PER-Alexnet scores. CONCLUSION: The PER-Alexnet + RF model has reached high performance for whole-case periodontal diagnosis. The CNN models combined with CA can assist dentists in quickly and accurately diagnosing the stage of periodontitis.

Exploring the Methodological Approaches of Studies on Radiographic Databases Used in Cariology to Feed Artificial Intelligence: A Systematic Review.

INTRODUCTION: A growing number of studies on diagnostic imaging show superior efficiency and accuracy of computer-aided diagnostic systems compared to those of certified dentists. This methodological systematic review aimed to evaluate the different methodological approaches used by studies focusing on machine learning and deep learning that have used radiographic databases to classify, detect, and segment dental caries. METHODS: The protocol was registered in PROSPERO before data collection (CRD42022348097). Literature research was performed in MEDLINE, Embase, IEEE Xplore, and Web of Science until December 2022, without language restrictions. Studies and surveys using a dental radiographic database for the classification, detection, or segmentation of carious lesions were sought. Records deemed eligible were retrieved and further assessed for inclusion by two reviewers who resolved any discrepancies through consensus. A third reviewer was consulted when any disagreements or discrepancies persisted between the two reviewers. After data extraction, the same reviewers assessed the methodological quality using the CLAIM and QUADAS-AI checklists. RESULTS: After screening 325 articles, 35 studies were eligible and included. The bitewing was the most commonly used radiograph (n = 17) at the time when detection (n = 15) was the most explored computer vision task. The sample sizes used ranged from 95 to 38,437, while the augmented training set ranged from 300 to 315,786. Convolutional neural network was the most commonly used model. The mean completeness of CLAIM items was 49% (SD ± 34%). The applicability of the CLAIM checklist items revealed several weaknesses in the methodology of the selected studies: most of the studies were monocentric, and only 9% of them used an external test set when evaluating the model's performance. The QUADAS-AI tool revealed that only 43% of the studies included in this systematic review were at low risk of bias concerning the standard reference domain. CONCLUSION: This review demonstrates that the overall scientific quality of studies conducted to feed artificial intelligence algorithms is low. Some improvement in the design and validation of studies can be made with the development of a standardized guideline for the reproducibility and generalizability of results and, thus, their clinical applications.

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.

AAPM Task Group Report 261: Comprehensive quality control methodology and management of dental and maxillofacial cone beam computed tomography (CBCT) systems.

Cone-beam computed tomography (CBCT) systems specifically designed and manufactured for dental, maxillofacial imaging (MFI) and otolaryngology (OLR) applications have been commercially available in the United States since 2001 and have been in widespread clinical use since. Until recently, there has been a lack of professional guidance available for medical physicists about how to assess and evaluate the performance of these systems and about the establishment and management of quality control (QC) programs. The owners and users of dental CBCT systems may have only a rudimentary understanding of this technology, including how it differs from conventional multidetector CT (MDCT) in terms of acceptable radiation safety practices. Dental CBCT systems differ from MDCT in several ways and these differences are described. This report provides guidance to medical physicists and serves as a basis for stakeholders to make informed decisions regarding how to manage and develop a QC program for dental CBCT systems. It is important that a medical physicist with experience in dental CBCT serves as a resource on this technology and the associated radiation protection best practices. The medical physicist should be involved at the pre-installation stage to ensure that a CBCT room configuration allows for a safe and efficient workflow and that structural shielding, if needed, is designed into the architectural plans. Acceptance testing of new installations should include assessment of mechanical alignment of patient positioning lasers and x-ray beam collimation and benchmarking of essential image quality performance parameters such as image uniformity, noise, contrast-to-noise ratio (CNR), spatial resolution, and artifacts. Several approaches for quantifying radiation output from these systems are described, including simply measuring the incident air-kerma (Kair) at the entrance surface of the image receptor. These measurements are to be repeated at least annually as part of routine QC by the medical physicist. QC programs for dental CBCT, at least in the United States, are often driven by state regulations, accreditation program requirements, or manufacturer recommendations.