Ashraf Abu-Seida

Background: Self-adhering flowable composite (SAFC) simplified restorative procedures especially when compared to conventional techniques. Self-adhering composite revolutionized restorative dentistry by merging advances of adhesive and restorative materials to generate the so-called “8th generation”. Aims: The objective of this clinical trial was to assess the clinical performance of SAFC compared to conventional flowable composite in minimally invasive occlusal cavities. Settings and Design: A total of 18 patients with conservative occlusal cavities received randomly two types of restorations in a split mouth design. Methods and Material: Vertise™ flow or Filtek™ Z350XT Flowable were applied according to the manufacturer's instructions. All restorations were evaluated at baseline and after 24 months respectively by two blinded assessors using modified USPHS criteria. Statistical analysis used: Chi-square test was used for intragroup comparison between time points and intergroup comparison within each time point. A value of P ≤ 0.05 was considered statistically significant. Relative risk (RR) was used to determine the clinical significance. Results: The results of the current study have revealed no statistically significant difference between both materials for all tested outcomes at baseline and after 24 months. Conclusions: SAFC revealed satisfactory clinical performance in restoration of minimally invasive occlusal cavities after 24 months follow-up.

Automated grading systems using deep convolution neural networks (DCNNs) have proven their capability and potential to distinguish between different breast cancer grades using digitized histopathological images. In digital breast pathology, it is vital to measure how confident a DCNN is in grading using a machine-confidence metric, especially with the presence of major computer vision challenging problems such as the high visual variability of the images. Such a quantitative metric can be employed not only to improve the robustness of automated systems, but also to assist medical professionals in identifying complex cases. In this paper, we propose Entropy-based Elastic Ensemble of DCNN models (3E-Net) for grading invasive breast carcinoma microscopy images which provides an initial stage of explainability (using an uncertainty-aware mechanism adopting entropy). Our proposed model has been designed in a way to (1) exclude images that are less sensitive and highly uncertain to our ensemble model and (2) dynamically grade the non-excluded images using the certain models in the ensemble architecture. We evaluated two variations of 3E-Net on an invasive breast carcinoma dataset and we achieved grading accuracy of 96.15% and 99.50%.