DentalSegmentator: Robust open source deep learning-based CT and CBCT image segmentation.

OBJECTIVES: Segmentation of anatomical structures on dento-maxillo-facial (DMF) computed tomography (CT) or cone beam computed tomography (CBCT) scans is increasingly needed in digital dentistry. The main aim of this research was to propose and evaluate a novel open source tool called DentalSegmentator for fully automatic segmentation of five anatomical structures on DMF CT and CBCT scans: maxilla/upper skull, mandible, upper teeth, lower teeth, and the mandibular canal. METHODS: A retrospective sample of 470 CT and CBCT scans was used as a training/validation set. The performance and generalizability of the tool was evaluated by comparing segmentations provided by experts and automatic segmentations in two hold-out test datasets: an internal dataset of 133 CT and CBCT scans acquired before orthognathic surgery and an external dataset of 123 CBCT scans randomly sampled from routine examinations in 5 institutions. RESULTS: The mean overall results in the internal test dataset (n = 133) were a Dice similarity coefficient (DSC) of 92.2 ± 6.3 % and a normalised surface distance (NSD) of 98.2 ± 2.2 %. The mean overall results on the external test dataset (n = 123) were a DSC of 94.2 ± 7.4 % and a NSD of 98.4 ± 3.6 %. CONCLUSIONS: The results obtained from this highly diverse dataset demonstrate that this tool can provide fully automatic and robust multiclass segmentation for DMF CT and CBCT scans. To encourage the clinical deployment of DentalSegmentator, the pre-trained nnU-Net model has been made publicly available along with an extension for the 3D Slicer software. CLINICAL SIGNIFICANCE: DentalSegmentator open source 3D Slicer extension provides a free, robust, and easy-to-use approach to obtaining patient-specific three-dimensional models from CT and CBCT scans. These models serve various purposes in a digital dentistry workflow, such as visualization, treatment planning, intervention, and follow-up.

The Influence of Slice Thickness, Sharpness, and Contrast Adjustments on Inferior Alveolar Canal Segmentation on Cone-Beam Computed Tomography Scans: A Retrospective Study.

This retrospective study aims to investigate the impact of cone-beam computed tomography (CBCT) viewing parameters such as contrast, slice thickness, and sharpness on the identification of the inferior alveolar nerve (IAC). A total of 25 CBCT scans, resulting in 50 IACs, were assessed by two investigators using a three-score system (good, average, and poor) on cross-sectional images. Slice thicknesses of 0.25 mm, 0.5 mm, and 1 mm were tested, along with varying sharpness (0, 6, 8, and 10) and contrast (0, 400, 800, and 1200) settings. The results were statistically analyzed to determine the optimal slice thickness for improved visibility of IAC, followed by evaluating the influence of sharpness and contrast using the optimal thickness. The identified parameters were then validated by performing semi-automated segmentation of the IACs and structure overlapping to evaluate the mean distance. Inter-rater and intra-rater reliability were assessed using Kappa statistics, and inferential statistics used Pearson's Chi-square test. Inter-rater and intra-rater reliability for all parameters were significant, ranging from 69% to 83%. A slice thickness of 0.25 mm showed consistently "good" visibility (80%). Sharpness values of zero and contrast values of 1200 also demonstrated high frequencies of "good" visibility. Overlap analysis resulted in an average mean distance of 0.295 mm and a standard deviation of 0.307 mm across all patients' sides. The study revealed that a slice thickness of 0.25 mm, zero sharpness value, and higher contrast value of 1200 improved the visibility and accuracy of IAC segmentation in CBCT scans. The individual patient's characteristics, such as anatomical variations, decreased bone density, and absence of canal walls cortication, should be considered when using these parameters.

Oral adverse events following COVID-19 and influenza vaccination in Australia.

Vaccine hesitancy, spurred by misinterpretation of Adverse Events (AEs), threatens public health. Despite sporadic reports of oral AEs post-COVID-19 vaccination, systematic analysis is scarce. This study evaluates these AEs using the Australian Database of Adverse Event Notifications (DAEN). A secondary analysis of DAEN data was conducted, with the analysis period commencing from the start of the COVID-19 vaccination rollout in February 2021 and the inception of the influenza vaccine database in 1971, both through until December 2022. The focus of the analysis was on oral AEs related to COVID-19 and influenza vaccines. Reports were extracted according to a predefined schema and then stratified by vaccine type, sex, and age. Oral paresthesia was the most common oral AE after COVID-19 vaccination (75.28 per 10,000 reports), followed by dysgeusia (73.96), swollen tongue (51.55), lip swelling (49.43), taste disorder (27.32), ageusia (25.85), dry mouth (24.75), mouth ulceration (18.97), oral hypoaesthesia (15.60), and oral herpes (12.74). While COVID-19 and influenza vaccines shared most oral AEs, taste-related AEs, dry mouth, and oral herpes were significantly more common after COVID-19 vaccination. mRNA vaccines yielded more oral AEs than other types. Females had higher oral AE incidence. Most oral AEs did not differ significantly between COVID-19 and influenza vaccination. However, specific oral AEs, particularly taste-related, dry mouth, and oral herpes, were more prevalent after COVID-19 vaccination compared with seasonal influenza, especially in females and mRNA vaccine recipients.

A New Classification of the Morphology of Complete Ponticulus Posticus on Cone Beam Computed Tomography.

The objectives of this retrospective study were to measure the prevalence of complete ponticulus posticus (CPP), to propose a new classification based on two different shapes of CPP, to compare these shapes with age and gender, and to test two different methods of measurements of the diameters of CPP on cone beam computed tomography (CBCT). MATERIAL AND METHODS: We used 2012 CBCT scans from Planmeca Promax 3D Mid and Romexis 5.1 software tools to measure the height and width of the CPP, and we measured the surface of the CPP using an ellipse tool. We classified the CPP into "thin" and "thick" shape. RESULTS: the prevalence of CPP was 9.49% with 97 male and 94 female patients. The unilateral type was found in 131 patients, while the bilateral type was found in 60 patients. Intra-observer reliability was evaluated using the intraclass correlation coefficient (ICC). The ICC was 0.875 for height, 0.872 for width, and 0.885 for the ellipse area. Both methods present very good intra-observer reproducibility. The "thin" group tended to be older and significantly more related to female patients. The "thick" group was associated with younger male patients. CONCLUSIONS: the proposed classification of CPP may be used when reporting the CBCT large field of view. There is still a need to increase the knowledge on the atlas and on its main variant, such as complete PP.

Diagnostic Test Accuracy of Artificial Intelligence in Detecting Periapical Periodontitis on Two-Dimensional Radiographs: A Retrospective Study and Literature Review.

This study aims to evaluate the diagnostic accuracy of artificial intelligence in detecting apical pathosis on periapical radiographs. A total of twenty anonymized periapical radiographs were retrieved from the database of Poznan University of Medical Sciences. These radiographs displayed a sequence of 60 visible teeth. The evaluation of the radiographs was conducted using two methods (manual and automatic), and the results obtained from each technique were afterward compared. For the ground-truth method, one oral and maxillofacial radiology expert with more than ten years of experience and one trainee in oral and maxillofacial radiology evaluated the radiographs by classifying teeth as healthy and unhealthy. A tooth was considered unhealthy when periapical periodontitis related to this tooth had been detected on the radiograph. At the same time, a tooth was classified as healthy when no periapical radiolucency was detected on the periapical radiographs. Then, the same radiographs were evaluated by artificial intelligence, Diagnocat (Diagnocat Ltd., San Francisco, CA, USA). Diagnocat (Diagnocat Ltd., San Francisco, CA, USA) correctly identified periapical lesions on periapical radiographs with a sensitivity of 92.30% and identified healthy teeth with a specificity of 97.87%. The recorded accuracy and F1 score were 96.66% and 0.92, respectively. The artificial intelligence algorithm misdiagnosed one unhealthy tooth (false negative) and over-diagnosed one healthy tooth (false positive) compared to the ground-truth results. Diagnocat (Diagnocat Ltd., San Francisco, CA, USA) showed an optimum accuracy for detecting periapical periodontitis on periapical radiographs. However, more research is needed to assess the diagnostic accuracy of artificial intelligence-based algorithms in dentistry.

Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review.

This systematic scoping review aims to map and identify the available artificial-intelligence-based techniques for imaging analysis, the characterization of stem cell differentiation, and trans-differentiation pathways. On the ninth of March 2022, data were collected from five electronic databases (PubMed, Medline, Web of Science, Cochrane, and Scopus) and manual citation searching; all data were gathered in Zotero 5.0. A total of 4422 articles were collected after deduplication; only twenty-seven studies were included in this systematic scoping review after a two-phase screening against inclusion criteria by two independent reviewers. The amount of research in this field is significantly increasing over the years. While the current state of artificial intelligence (AI) can tackle a multitude of medical problems, the consensus amongst researchers remains that AI still falls short in multiple ways that investigators should examine, ranging from the quality of images used in training sets and appropriate sample size, as well as the unexpected events that may occur which the algorithm cannot predict.

COVID-19 vaccine booster hesitancy (VBH) of healthcare professionals and students in Poland: Cross-sectional survey-based study.

Since healthcare professionals (HCPs) play a critical role in shaping their local communities' attitudes toward vaccines, HCPs' beliefs and attitudes toward vaccination are of vital importance for primary prevention strategies. The present study was designed as a cross-sectional survey-based study utilizing a self-administered questionnaire to collect data about COVID-19 vaccine booster hesitancy (VBH) among Polish HCPs and students of medical universities (MUSs). Out of the 443 included participants, 76.3% were females, 52.6% were HCPs, 31.8% were previously infected by SARS-CoV-2, and 69.3% had already received COVID-19 vaccine booster doses (VBD). Overall, 74.5% of the participants were willing to receive COVID-19 VBD, while 7.9 and 17.6% exhibited their hesitance and rejection, respectively. The most commonly found promoter for acceptance was protection of one's health (95.2%), followed by protection of family's health (81.8%) and protection of community's health (63.3%). Inferential statistics did not show a significant association between COVID-19 VBH and demographic variables, e.g., age and gender; however, the participants who had been previously infected by SARS-CoV-2 were significantly more inclined to reject the VBD. Protection from severe infection, community transmission, good safety profile, and favorable risk-benefit ratio were the significant determinants of the COVID-19 VBD acceptance and uptake. Fear of post-vaccination side effects was one of the key barriers for accepting COVID-19 VBD, which is consistent with the pre-existing literature. Public health campaigns need to highlight the postulated benefits of vaccines and the expected harms of skipping VBD.

Oral Health-Related Knowledge, Attitudes and Behaviours of Arab Dental Students: Multi-National Cross-Sectional Study and Literature Analysis 2000-2020.

Dental students are the future leaders of oral health in their respective communities; therefore, their oral health-related attitudes and behaviours are of practical value for primary disease prevention. The present study aimed to evaluate oral health-related knowledge, attitudes, and behaviours of dental students in Arab countries and explore the potential sociodemographic predictors of their oral health outcomes. A multi-centre, cross-sectional study was conducted during the academic year 2019/2020 in three Arab countries: Lebanon, Syria, and Tunisia. The study used a validated Arabic version of the Hiroshima University Dental Behavioural Inventory (HU-DBI) composed of original twenty items that assess the level of oral health-related knowledge, attitudes, and behaviours, and four additional dichotomous items related to tobacco smoking, alcohol drinking, problematic internet use, and regular dental check-up The HU-DBI score ranges between 0 and 12. A total of 1430 students took part in this study, out of which 60.8% were females, 57.8% were enrolled in clinical years, 24.5% were tobacco smokers, 7.2% were alcohol drinkers, and 87% reported internet addiction. The mean HU-DBI score was 6.31 ± 1.84, with Lebanon having the highest score (6.67 ± 1.83), followed by Syria (6.38 ± 1.83) and Tunisia (6.05 ± 1.83). Clinical students (6.78 ± 1.70) had higher HU-DBI scores than their preclinical peers (5.97 ± 1.86). The year-over-year analysis revealed that dental public health and preventive dentistry courses had significantly and positively impacted the undergraduate students' knowledge, attitudes, and behaviours. The gender-based differences were not statistically significant, with a modest trend favouring males, especially oral health behaviours. Tobacco smoking, alcohol drinking, and problematic internet use were associated with lower HU-DBI scores. In the Arab world, the economic rank of the country where the dental students live/study was weakly correlated with the students' mean HU-DBI score.

The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review.

This systematic review aims to identify the available semi-automatic and fully automatic algorithms for inferior alveolar canal localization as well as to present their diagnostic accuracy. Articles related to inferior alveolar nerve/canal localization using methods based on artificial intelligence (semi-automated and fully automated) were collected electronically from five different databases (PubMed, Medline, Web of Science, Cochrane, and Scopus). Two independent reviewers screened the titles and abstracts of the collected data, stored in EndnoteX7, against the inclusion criteria. Afterward, the included articles have been critically appraised to assess the quality of the studies using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Seven studies were included following the deduplication and screening against exclusion criteria of the 990 initially collected articles. In total, 1288 human cone-beam computed tomography (CBCT) scans were investigated for inferior alveolar canal localization using different algorithms and compared to the results obtained from manual tracing executed by experts in the field. The reported values for diagnostic accuracy of the used algorithms were extracted. A wide range of testing measures was implemented in the analyzed studies, while some of the expected indexes were still missing in the results. Future studies should consider the new artificial intelligence guidelines to ensure proper methodology, reporting, results, and validation.

COVID-19-Associated Mucormycosis (CAM): Case-Series and Global Analysis of Mortality Risk Factors.

BACKGROUND: Since the novel coronavirus disease (COVID-19) outbreak, the cases of COVID-19 co-infections have been increasingly reported worldwide. Mucormycosis, an opportunistic fungal infection caused by members of the Mucorales order, had been frequently isolated in severely and critically ill COVID-19 patients. METHODS: Initially, the anamnestic, clinical, and paraclinical features of seven COVID-19-associated mucormycosis (CAM) cases from Egypt were thoroughly reported. Subsequently, an extensive review of the literature was carried out to describe the characteristics of CAM cases globally, aiming to explore the potential risk factors of mortality in CAM patients. RESULTS: Out of the seven reported patients in the case series, five (71.4%) were males, six (85.7%) had diabetes mellitus, and three (42.9%) had cardiovascular disease. All patients exhibited various forms of facial deformities under the computed tomography scanning, and two of them tested positive for Mucorales using the polymerase chain reaction (PCR) testing. Liposomal amphotericin B (LAmB) was prescribed to all cases, and none of them died until the end of the follow-up. On reviewing the literature, 191 cases were reported worldwide, of which 74.4% were males, 83.2% were from low-middle income countries, and 51.4% were aged 55 years old or below. Diabetes mellitus (79.1%), chronic hypertension (30%), and renal disease/failure (13.6%) were the most common medical comorbidities, while steroids (64.5%) were the most frequently prescribed medication for COVID-19, followed by Remdesivir (18.2%), antibiotics (12.7%), and Tocilizumab (5.5%). CONCLUSIONS: As the majority of the included studies were observational studies, the obtained evidence needs to be interpreted carefully. Diabetes, steroids, and Remdesivir were not associated with increased mortality risk, thus confirming that steroids used to manage severe and critical COVID-19 patients should not be discontinued. Lung involvement, bilateral manifestation, and Rhizopus isolation were associated with increased mortality risk, thus confirming that proactive screening is imperative, especially for critically ill patients. Finally, surgical management and antimycotic medications, e.g., amphotericin B and posaconazole, were associated with decreased mortality risk, thus confirming their effectiveness.