Fetal orientation determines the mode of delivery. It is also important for sequence planning in fetal MRI. This abstract proposes Fet-Net, a deep-learning algorithm, which uses a novel convolutional neural network (CNN) architecture, to automatically detect fetal orientation from a 2-dimensional (2D) magnetic resonance imaging (MRI) slice. 6,120 2D MRI slices displaying vertex, breech, oblique and transverse fetal orientations were used for training, validation and testing. Fet-Net achieved an average accuracy and F1 score of 97.68%, and a loss of 0.06828. Fet-Net was able to detect and classify fetal orientation, which may serve to accelerate fetal MRI acquisition.

Identifying fetal orientation is essential for determining the mode of delivery and for sequence planning in fetal magnetic resonance imaging (MRI). This manuscript describes a deep learning algorithm named Fet-Net, composed of convolutional neural networks (CNNs), which allows for the automatic detection of fetal orientation from a two-dimensional (2D) MRI slice. The architecture consists of four convolutional layers, which feed into a simple artificial neural network. Compared with eleven other prominent CNNs (different versions of ResNet, VGG, Xception, and Inception), Fet-Net has fewer architectural layers and parameters. From 144 3D MRI datasets indicative of vertex, breech, oblique and transverse fetal orientations, 6120 2D MRI slices were extracted to train, validate and test Fet-Net. Despite its simpler architecture, Fet-Net demonstrated an average accuracy and F1 score of 97.68% and a loss of 0.06828 on the 6120 2D MRI slices during a 5-fold cross-validation experiment. This architecture outperformed all eleven prominent architectures (p < 0.05). An ablation study proved each component's statistical significance and contribution to Fet-Net's performance. Fet-Net demonstrated robustness in classification accuracy even when noise was introduced to the images, outperforming eight of the 11 prominent architectures. Fet-Net's ability to automatically detect fetal orientation can profoundly decrease the time required for fetal MRI acquisition.

ABSTRACT Nanomaterials observe specialized properties relative to gross materials. Due to their small size, specialized nanomaterial properties include decreased reactivity, an increased surface area to volume ratio, heightened structural properties, and in some cases, antimicrobial and antibacterial effects. Current researchers are looking to use nanoparticle/nanomaterial properties to solve prevalent dental issues that cannot be addressed with traditionally used materials. This paper will serve as an extensive review of current nanomaterial applications as they pertain to dental fillings and dental filling processes. Comparative assessments of traditional materials used in dental fillings will be made as well as comparative assessments of currently used nanomaterials in dental fillings. Material comparisons are based on criteria pertaining to biocompatibility, toxicity, reactivity, cost, and antimicrobial/antibacterial properties. When comparing the three most currently used dental filling nanomaterials – Carbon-Based Nanotubes, Silica Nanoparticles and Silver-Coated Nanoparticles – it was observed that Silica Nanoparticles demonstrated the greatest material advantage and should be recommended for continued use. Issues regarding future developmental dental filling applications of graphene nanoparticles, organic nanoparticles and gold nanoparticles will also be discussed. Keywords: Nanomaterials, antibacterial, dental fillings, silica resins, biocompatibility.