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Saliva has emerged as a promising diagnostic fluid for viral infections, enabling the direct analysis of viral genetic material and the detection of infection markers such as proteins, metabolites, microRNAs, and immunoglobulins. This comprehensive review aimed to explore the use of saliva as a diagnostic tool for viral infections, emphasizing its advantages and limitations. Saliva stands out due to its simplicity and safety in collection, along with the convenience of self-collection without the need for healthcare supervision, while potentially being comparable to urine and blood in terms of effectiveness. Herein, we highlighted the significant potential of saliva in assessing viral loads and diagnosing viral infections, such as herpesviruses, HPV, PyV, TTV, SARS-CoV-2, and MPXV. The detection of viral shedding in saliva underscores its utility in early diagnosis, the monitoring of infection progression, and evaluating treatment responses. The non-invasive nature of saliva collection makes it an appealing alternative to more invasive methods, promoting better patient compliance and facilitating large-scale screening and surveillance. As such, we further highlight current evidence on the use of saliva as a prognostic tool. Although a significant amount of data is already available, further investigations are warranted to more comprehensively assess the added benefit from the utilization of salivary biomarkers in the clinics. Salivary biomarkers show great promise for the early detection and prevention of viral infection complications, potentially improving disease management and control at the population level. Integrating these non-invasive tools into routine clinical practice could enhance personalized healthcare strategies and patient outcomes. Future studies should focus on establishing standardization protocols, validating the accuracy of salivary diagnostics, and expanding clinical research to enhance the diagnostic and monitoring capabilities of salivary biomarkers.

Background. Torquetenovirus (TTV) is a small DNA virus constituting the human virome. High levels of TTV-DNA have been shown to be associated with immunosuppression and inflammatory chronic disorders. Aim. To assess the possible association between the salivary viral load of TTV-DNA in patients hospitalised due to COVID-19 and disease severity. Methods. Saliva samples collected from 176 patients infected with SARS-CoV-2 were used to investigate the presence of SARS-CoV-2 and TTV-DNA by use of real-time RT-PCR. Results. The majority of patients were male with severe COVID-19. Presence of SARS-CoV-2 was observed in the saliva of 64.77% of patients, showing TTV-DNA in 55.68% of them. Patients with impaired clinical conditions (p < 0.001), which evolved to death (p = 0.003), showed a higher prevalence of TTV-DNA. The median viral load in patients with severe condition was 4.99 log10 copies/mL, in which those who were discharged and those evolving to death had values of 3.96 log10 copies/mL and 6.27 log10 copies/mL, respectively. A statistically significant association was found between the distribution of TTV-DNA viral load in saliva samples and severity of COVID-19 (p = 0.004) and disease outcomes (p < 0.001). Conclusions. These results indicate that TTV-DNA in saliva could be a useful biomarker of COVID-19 severity and prognosis.

Introduction Onodi cells (OCs) are posterior ethmoid cells that are located above the sphenoid sinus, close to or even surrounding the carotid artery and optic nerve.

This study aims to compare computed tomography (CT) with magnetic resonance imaging (MRI) of the temporomandibular joint (TMJ) by using texture analysis (TA) to detect condylar bone marrow changes associated with the flattening and erosion of cortical bone. A total of 47 patients from the Dentomaxillofacial Radiology Division at São Paulo State University were evaluated. Images from 250 CT and 250 MRI images were assessed by experienced radiologists employing OnDemand3D software. Texture parameters were extracted with MaZda software (version 4.6), and we focused on regions of interest within the condyles. Statistical analysis revealed significant differences in texture parameters between the affected and control groups. CT images showed higher correlation values in cases of flattening, whereas MRI images demonstrated substantial changes in texture parameters for both flattening and erosion. These findings suggest that the texture analysis of CT and MRI images can effectively detect early and advanced degenerative changes in the TMJ, thus providing valuable insights into the underlying pathophysiology and aiding in early intervention and treatment planning.

The purpose of this study was to identify changes in the temporomandibular joint disc affected by effusion by using texture analysis of magnetic resonance images (MRIs). Methods: A total of 223 images of the TMJ, 42 with joint effusion and 181 without, were analyzed. Three consecutive slices were then exported to MaZda software, in which two oval ROIs (one in the anterior band and another in the intermediate zone of the joint disc) were determined in each slice and eleven texture parameters were calculated by using a gray-level co-occurrence matrix. Spearman’s correlation coefficient test was used to assess the correlation between texture variables and to select variables for analysis. The Mann–Whitney test was used to compare the groups. Results: The significance level was set at 5%, with the results demonstrating that there was no high correlation between the parameter directions. It was possible to observe a trend between the average parameters, in which the group with effusion always had smaller values than the group without effusion, except for the parameter measuring the difference in entropy. Conclusion: The trend towards lower overall values for the texture parameters suggested a different behavior between TMJ discs affected by effusion and those not affected, indicating that there may be intrinsic changes.

Abstract The differentiation between ameloblastoma (AB) and odontogenic keratocyst (OKC) is essential for the formulation of the surgical plan, especially considering the biological behavior of these two pathological entities. Therefore, developing means to increase the accuracy of the diagnostic process is extremely important for a safe treatment. The aim of this study was to use magnetic resonance imaging (MRI) based on texture analysis (TA) as an aid in differentiating AB from OKC. This study comprised 18 patients; eight patients with AB and ten with OKC. All diagnoses were determined through incisional biopsy and later through histological examination of the surgical specimen. MRI was performed using a 3 T scanner with a neurovascular coil according to a specific protocol. All images were exported to segmentation software in which the volume of interest (VOI) was determined by a radiologist, who was blind to the histopathological results. Next, the textural parameters were computed by using the MATLAB software. Spearman's correlation coefficient was used to assess the correlation between texture parameters and the selected variables. Differences in TA parameters were compared between AB and OKC by using the Mann–Whitney test. Mann–Whitney test showed a statistically significant difference between AB and OKC for the parameters entropy (P = 0.033) and sum average (P = 0.033). MRI texture analysis has the potential to discriminate between AB and OKC as a noninvasive method. MRI texture analysis can be an additional tool to differentiate ameloblastoma from odontogenic keratocyst.

In the present study, we aimed to assess frontal ethmoidal cells by using segmentation 3D software to establish a possible correlation between volume variation and both gender and age, as well as a relationship with Keros classification. Helical computed tomography images were obtained from 71 patients for analysis, in which the agger nasi cell, supra agger cell, supra agger frontal cell, supra bulla frontal cell, supraorbital ethmoidal cell, supra bulla cell, and frontal septal cell were identified and segmented to obtain their volume. Significant differences in volume were found for age and gender regarding agger nasi cells (p-value = 0.017), supra agger cells (p-value < 0.001), and frontal septal cells (p-value = 0.049). In the frontal septal cells, an increase of one year in age reduced the volume by 0.309, on average. According to Keros classification, the mean volumes were 10.07 and 25.64, respectively, for types II and III, both being greater than that of type I. Extracting volumetric data by using segmentation software in agger nasi cells, supra agger cells, and frontal septal cells may be useful for obtaining additional information related to age, in addition to possibly contributing to elucidating the anatomical variations in the region and an identification forensic tool.

This study aims to investigate the effect of using an artificial intelligence (AI) system (Diagnocat, Inc., San Francisco, CA, USA) for caries detection by comparing cone-beam computed tomography (CBCT) evaluation results with and without the software. 500 CBCT volumes are scored by three dentomaxillofacial radiologists for the presence of caries separately on a five-point confidence scale without and with the aid of the AI system. After visual evaluation, the deep convolutional neural network (CNN) model generated a radiological report and observers scored again using AI interface. The ground truth was determined by a hybrid approach. Intra- and inter-observer agreements are evaluated with sensitivity, specificity, accuracy, and kappa statistics. A total of 6008 surfaces are determined as ‘presence of caries’ and 13,928 surfaces are determined as ‘absence of caries’ for ground truth. The area under the ROC curve of observer 1, 2, and 3 are found to be 0.855/0.920, 0.863/0.917, and 0.747/0.903, respectively (unaided/aided). Fleiss Kappa coefficients are changed from 0.325 to 0.468, and the best accuracy (0.939) is achieved with the aided results. The radiographic evaluations performed with aid of the AI system are found to be more compatible and accurate than unaided evaluations in the detection of dental caries with CBCT images.

This study aims to develop an algorithm for the automatic segmentation of the parotid gland on CT images of the head and neck using U-Net architecture and to evaluate the model’s performance. In this retrospective study, a total of 30 anonymized CT volumes of the head and neck were sliced into 931 axial images of the parotid glands. Ground truth labeling was performed with the CranioCatch Annotation Tool (CranioCatch, Eskisehir, Turkey) by two oral and maxillofacial radiologists. The images were resized to 512 × 512 and split into training (80%), validation (10%), and testing (10%) subgroups. A deep convolutional neural network model was developed using U-net architecture. The automatic segmentation performance was evaluated in terms of the F1-score, precision, sensitivity, and the Area Under Curve (AUC) statistics. The threshold for a successful segmentation was determined by the intersection of over 50% of the pixels with the ground truth. The F1-score, precision, and sensitivity of the AI model in segmenting the parotid glands in the axial CT slices were found to be 1. The AUC value was 0.96. This study has shown that it is possible to use AI models based on deep learning to automatically segment the parotid gland on axial CT images.