According to the International Bruxism Consensus, bruxism refers to the activity of the masticatory muscles reflecting contraction disorders, regardless of whether it is during sleep (SB) or an awake (AB) state. The objective of the present study was to evaluate the activity of the masseter muscle by surface electromyographic (sEMG) recordings. This study was performed on 20 participants with self-reported “possible bruxism” (study group) and 20 participants with no self-reported bruxism (control group); all participants underwent an evaluation of the masseter muscle activity using the dia-BRUXO device, which provides numerical parameters regarding sEMG (the total duration and the type of bruxism specific events, the effort made by the masticatory muscles during the recording period, and the personal bruxism index of each participant). Participants from the study group presented more clenching events during AB, three times more frequent than the control group (p = 0.002, Mann–Whitney U test); for SB, the frequency of clenching and grinding events was comparable within the study group, being more frequent than for the control group; the mean value of the effort index was higher for AB (1.177%) than SB (0.470%) and the same for the duration index, with a mean value of 2.788% for AB and 1.054% for SB. All participants from the control group presented reduced values for all acquired parameters. Overall, the personal bruxism index in AB was approximately four times higher for the study group (2.251%) compared to the control group (0.585%) (p < 0.005, Mann–Whitney U test). Similar values were obtained for SB. All participants with “possible bruxism” from the study group presented a higher activity of the masseter muscle, which is specific for bruxism, thus being defined as “definite bruxism”.

Background: The ultrasound is one of the most used medical imaging investigations worldwide. It is non-invasive and effective in assessing liver tumors or other types of parenchymal changes. Methods: The aim of the study was to build a deep learning model for image segmentation in ultrasound video investigations. The dataset used in the study was provided by the University of Medicine and Pharmacy Craiova, Romania and contained 50 video examinations from 49 patients. The mean age of the patients in the cohort was 69.57. Regarding presence of a subjacent liver disease, 36.73% had liver cirrhosis and 16.32% had chronic viral hepatitis (5 patients: chronic hepatitis C and 3 patients: chronic hepatitis B). Frames were extracted and cropped from each examination and an expert gastroenterologist labelled the lesions in each frame. After labelling, the labels were exported as binary images. A deep learning segmentation model (U-Net) was trained with focal Tversky loss as a loss function. Two models were obtained with two different sets of parameters for the loss function. The performance metrics observed were intersection over union and recall and precision. Results: Analyzing the intersection over union metric, the first segmentation model obtained performed better compared to the second model: 0.8392 (model 1) vs. 0.7990 (model 2). The inference time for both models was between 32.15 milliseconds and 77.59 milliseconds. Conclusions: Two segmentation models were obtained in the study. The models performed similarly during training and validation. However, one model was trained to focus on hard-to-predict labels. The proposed segmentation models can represent a first step in automatically extracting time-intensity curves from CEUS examinations.

Background/Objectives: Excessive occlusal forces manifest in bruxism and have consequences on teeth and jaws. The aim of this study was to determine the association of bruxism with clinical and radiological signs of excessive occlusal forces, such as tooth wear, fatigue dental fissures and fractures, abfraction, masseter muscle hypertrophy, and bone apposition at the mandibular angle. Methods: This cross-sectional clinical study included 181 patients presented for treatment in a general dentistry clinic. For each patient, data were extracted from the dental chart, as follows: demographic data (sex, age, and smoking), clinical data (number of teeth present, Eichner edentulous score, TWI wear score, number of fractured teeth, number of teeth with abfraction, presence of masseter hypertrophy, presence of hypersensitivity), and radiological data (bone apposition at the mandibular angle). The patients were divided into two groups according to the presence or absence of bruxism. A binomial logistic regression model was run to determine the association between bruxism and clinical and radiological signs of excessive occlusal forces. The data were statistically processed in SPSS. Results: In total, 99 women and 82 men with mean age 44.87 ± 12.67 were included in the study. Compared to the group without bruxism, the group of patients with bruxism (39.78%) showed statistically significant higher tooth wear index (TWI) (p < 0.0005), a higher number of fractured teeth (p = 0.037), a higher number of teeth with abfraction lesions (p = 0.001), and a significantly higher bone apposition score (p < 0.0005). The binomial logistic regression model showed a high prediction bruxism score for masseter muscle hypertrophy (15 times, p < 0.0005), for tooth wear index (almost 7 times, p = 0.010), and for bone apposition score (almost 3 times, p = 0.044). Conclusions: Patients with bruxism showed masseter muscle hypertrophy, higher attrition-type tooth wear index, and more teeth with fatigue fractures and abfractions than those without bruxism. Bruxism clinical signs were positively correlated with a higher bone apposition score.

Background: Contrast-enhanced ultrasound (CEUS) is an important imaging modality in the diagnosis of liver tumors. By using contrast agent, a more detailed image is obtained. Time-intensity curves (TIC) can be extracted using a specialized software, and then the signal can be analyzed for further investigations. Methods: The purpose of the study was to build an automated method for extracting TICs and classifying liver lesions in CEUS liver investigations. The cohort contained 50 anonymized video investigations from 49 patients. Besides the CEUS investigations, clinical data from the patients were provided. A method comprising three modules was proposed. The first module, a lesion segmentation deep learning (DL) model, handled the prediction of masks frame-by-frame (region of interest). The second module performed dilation on the mask, and after applying colormap to the image, it extracted the TIC and the parameters from the TIC (area under the curve, time to peak, mean transit time, and maximum intensity). The third module, a feed-forward neural network, predicted the final diagnosis. It was trained on the TIC parameters extracted by the second model, together with other data: gender, age, hepatitis history, and cirrhosis history. Results: For the feed-forward classifier, five classes were chosen: hepatocarcinoma, metastasis, other malignant lesions, hemangioma, and other benign lesions. Being a multiclass classifier, appropriate performance metrics were observed: categorical accuracy, F1 micro, F1 macro, and Matthews correlation coefficient. The results showed that due to class imbalance, in some cases, the classifier was not able to predict with high accuracy a specific lesion from the minority classes. However, on the majority classes, the classifier can predict the lesion type with high accuracy. Conclusions: The main goal of the study was to develop an automated method of classifying liver lesions in CEUS video investigations. Being modular, the system can be a useful tool for gastroenterologists or medical students: either as a second opinion system or a tool to automatically extract TICs.

Background/Objectives: Chronic inflammation and psychosocial factors significantly influence cancer progression and patient behavior in seeking medical care. Understanding their interplay is essential for enhancing early detection and developing personalized treatment strategies. This study aims to develop a comprehensive patient profiling model by comparing non-melanoma skin cancer (NMSC) and colorectal cancer (CRC). The goal is to identify common and distinct patterns in inflammation and psychosocial factors that affect disease progression and clinical presentation. Methods: We conducted a comparative analysis of patients diagnosed with NMSC and CRC, integrating clinical data with sociodemographic and psychological assessments. Advanced neural network algorithms were employed to detect subtle patterns and interactions among these factors. Based on the analysis, a cancer risk assessment questionnaire was developed to stratify patients into low-, moderate-, and high-risk categories. Results: Patients with low systemic inflammation and adequate vagal tone, supported by a stable family environment, demonstrated heightened sensitivity to subclinical symptoms, enabling earlier diagnosis and timely intervention. Conversely, patients with high systemic inflammation and reduced vagal tone, often influenced by chronic stress and unstable family environments, presented at more advanced disease stages. The developed risk assessment tool effectively classified patients into distinct risk categories, facilitating targeted preventive measures and personalized therapeutic strategies. Neural network profiling revealed significant interactions between biological and psychosocial factors, enhancing our understanding of their combined impact on cancer progression. Conclusions: The integrated profiling approach and the newly developed risk assessment questionnaire have the potential to transform cancer management by improving early detection, personalizing treatment strategies, and addressing psychosocial factors. This model not only enhances clinical outcomes and patient quality of life but also offers a framework adaptable to other cancer types, promoting a holistic and patient-centered approach in oncology.

Background/Objectives: Fixed prosthetic dentures (FPD) represent a reliable treatment option to rehabilitate oral function and aesthetics. The concentration of loads at a certain level of the bridge causes overloading of the bridge components. The objectives of this study were to identify the frequency and severity of complications in FPDs associated with occlusal overload for patients prosthetically treated with metal–ceramic and metal–acrylic FPDs. Methods: This retrospective cross-sectional clinical study included 306 patients of both sexes, aged 30+, with an FPD requiring rehabilitation. The following data were collected: general demographic data of the patients, data on dental bridges, data on occlusal overloads, and complications of FPDs according to severity. Statistical processing was performed in SPSS. Results: Metal–ceramic FPDs had more abutment teeth (p = 0.035), fewer intermediaries (p < 0.0005), and less severe complications (p = 0.001). Maxillary FPDs had a higher number of abutment teeth, compared to mandible FPDs (p < 0.001). As the life duration of the FPDs increased, the number of intermediaries and the severity grade also increased (p < 0.001). Compared to intercalated and pontics, cantilever pontics of FPDs had a longer duration (p < 0.001), a lower number of intermediaries (p < 0.001), and a higher severity grade (p < 0.001). The presence of occlusal interferences was associated only with a lower FPD duration (p = 0.007). The presence of unbalanced occlusion planes was associated with a higher severity grade, while the presence of shortened arches was associated with a longer FPD duration (p < 0.001), a higher number of intermediaries (p = 0.005), and a higher severity grade (p < 0.001). Conclusions: The most severe complications of FPDs were recorded in the case of shortened arches and cantilever bridges. This study draws attention to the need for preprosthetic and postprosthetic occlusal balancing and the need for complete rehabilitation of the dental arches.