Search

Objectives: This research focuses on performing teeth segmentation with panoramic radiograph images using a denoised encoder-based residual U-Net model, which enhances segmentation techniques and has the capacity to adapt to predictions with different and new data in the dataset, making the proposed model more robust and assisting in the accurate identification of damages in individual teeth., Methods: The effective segmentation starts with pre-processing the Tufts dataset to resize images to avoid computational complexities. Subsequently, the prediction of the defect in teeth is performed with the denoised encoder block in the residual U-Net model, in which a modified identity block is provided in the encoder section for finer segmentation on specific regions in images, and features are identified optimally. The denoised block aids in handling noisy ground truth images effectively., Results: Proposed module achieved greater values of mean dice and mean IoU with 98.90075 and 98.74147 CONCLUSIONS: Proposed AI enabled model permitted a precise approach to segment the teeth on Tuffs dental dataset in spite of the existence of densed dental filling and the kind of tooth., Clinical Significance: The proposed model is pivotal for improved dental diagnostics, offering precise identification of dental anomalies. This could revolutionize clinical dental settings by facilitating more accurate treatments and safer examination processes with lower radiation exposure, thus enhancing overall patient care., Competing Interests: Declaration of Competing Interest The authors declare that there is no conflict of interest., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Favipiravir is an oral antiviral, that might treat COVID-19 by enhancing viral eradication, particularly in patients with mild-to-moderate disease. Yet, the findings on the use of favipiravir in critically ill patients with COVID-19 are inconsistent. Therefore, this study aimed to assess the effectiveness and safety of favipiravir in critically ill patients with COVID-19., Method: A multicenter retrospective cohort study includes critically ill adult patients with COVID-19 admitted to the intensive care units (ICUs) was conducted from March 2020 to July 2021. Patients were categorized based on favipiravir use (control vs. favipiravir). The primary outcome was in-hospital mortality. Secondary outcomes included mechanical ventilation (MV) duration, 30-day mortality, ICU length of stay (LOS), hospital LOS, and complications during the stay., Results: After propensity score (PS) matching (1:1 ratio), 146 patients were included in the final analysis. A higher in-hospital and 30-day mortality were observed in patients receiving favipiravir compared to the control group at crude analysis (65.3% vs. 43.8%; P-value=0.009 and 56.3% vs. 40.3; P-value=0.06, respectively); however, no differences were observed using multivariable Cox proportional hazards regression analysis (HR 1.17; 95% CI 0.73, 1.87; P-value =0.51 and HR 0.86; 95% CI 0.53, 1.39; P-value=0.53, respectively). Conversely, the MV duration and ICU LOS were longer in patients who received favipiravir than the control group (β coefficient 0.51; CI 0.09, 0.92; P-value = 0.02, β coefficient 0.41; CI 0.17, 0.64; P-value = 0.0006, respectively). Complications during the stay were comparable between the two groups., Conclusion: The use of favipiravir in critically ill patients with COVID-19 did not demonstrate a reduction in mortality; instead, it was linked with longer MV duration and ICU stay. This finding suggests limiting favipiravir use to infections where it is more effective, other than COVID-19. Further randomized clinical trials are needed to confirm these findings., Competing Interests: Declaration of Competing Interest We have no conflict of interest to declare., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)