The preliminary in vitro study and application of deep learning algorithm in cone beam computed tomography image implant recognition.

To properly repair and maintain implants, which are bone tissue implants that replace natural tooth roots, it is crucial to accurately identify their brand and specification. Deep learning has demonstrated outstanding capabilities in analysis, such as image identification and classification, by learning the inherent rules and degrees of representation of data models. The purpose of this study is to evaluate deep learning algorithms and their supporting application software for their ability to recognize and categorize three dimensional (3D) Cone Beam Computed Tomography (CBCT) images of dental implants. By using CBCT technology, the 3D imaging data of 27 implants of various sizes and brands were obtained. Following manual processing, the data were transformed into a data set that had 13,500 two-dimensional data. Nine deep learning algorithms including GoogleNet, InceptionResNetV2, InceptionV3, ResNet50, ResNet50V2, ResNet101, ResNet101V2, ResNet152 and ResNet152V2 were used to perform the data. Accuracy rates, confusion matrix, ROC curve, AUC, number of model parameters and training times were used to assess the efficacy of these algorithms. These 9 deep learning algorithms achieved training accuracy rates of 100%, 99.3%, 89.3%, 99.2%, 99.1%, 99.5%, 99.4%, 99.5%, 98.9%, test accuracy rates of 98.3%, 97.5%, 94.8%, 85.4%, 92.5%, 80.7%, 93.6%, 93.2%, 99.3%, area under the curve (AUC) values of 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00. When used to identify implants, all nine algorithms perform satisfactorily, with ResNet152V2 achieving the highest test accuracy, classification accuracy, confusion matrix area under the curve, and receiver operating characteristic curve area under the curve area. The results showed that the ResNet152V2 has the best classification effect on identifying implants. The artificial intelligence identification system and application software based on this algorithm can efficiently and accurately identify the brands and specifications of 27 classified implants through processed 3D CBCT images in vitro, with high stability and low recognition cost.

Biological applications of chiral inorganic nanomaterials.

Chirality is common in nature and plays the essential role in maintaining physiological process. Chiral inorganic nanomaterials with intense optical activity have attracted more attention due to amazing properties in recent years. Over the past decades, many efforts have been paid to the preparation and chirality origin of chiral nanomaterials; furthermore, emerging biological applications have been investigated widely. This review mainly summarizes recent advances in chiral nanomaterials. The top-down and bottom-up preparation methods and chirality origin of chiral nanomaterials are introduced; besides, the biological applications, such as sensing, therapy, and catalysis, will be introduced comprehensively. Finally, we also provide a perspective on the biomedical applications of chiral nanomaterials.

Construction of Antibacterial Surface Via Layer-by-Layer Method.

Construction of antibacterial surfaces or films is of great interest in various fields including biomedicine, food, agriculture and so on. So far, a number of antibacterial agents have been used to construct antibacterial surfaces. Layer-by-Layer (LbL) assembly is a simple and versatile deposition process for fabricating multilayer thin films with great advantages to control the architecture and composition of the films. In this review, we give a brief introduction of LbL, and different materials used to fabricate antibacterial surfaces with LbL assembly approach are described as well as their drawbacks. Much attention is also paid to the recent development of multifunctional and intelligent antibacterial surfaces. Moreover, the advantages and limitations of these different types of antibacterial materials are summarized and subsequently directions for future development are proposed.