Artificial intelligence in detection and segmentation of internal auditory canal and its nerves using deep learning techniques.

PURPOSE: Artificial intelligence (AI) in medical imaging is a burgeoning topic that involves the interpretation of complex image structures. The recent advancements in deep learning techniques increase the computational powers to extract vital features without human intervention. The automatic detection and segmentation of subtle tissue such as the internal auditory canal (IAC) and its nerves is a challenging task, and it can be improved using deep learning techniques. METHODS: The main scope of this research is to present an automatic method to detect and segment the IAC and its nerves like the facial nerve, cochlear nerve, inferior vestibular nerve, and superior vestibular nerve. To address this issue, we propose a Mask R-CNN approach driven with U-net to detect and segment the IAC and its nerves. The Mask R-CNN with its backbone network of the RESNET50 model learns a background-based localization policy to produce an actual bounding box of the IAC. Furthermore, the U-net segments the structure related information of IAC and its nerves by learning its features. RESULTS: The proposed method was experimented on clinical datasets of 50 different patients including adults and children. The localization of IAC using Mask R-CNN was evaluated using Intersection of Union (IoU), and segmentation of IAC and its nerves was evaluated using Dice similarity coefficient. CONCLUSIONS: The localization result shows that mean IoU of RESNET50, RESNET101 are 0.79 and 0.74, respectively. The Dice similarity coefficient of IAC and its nerves using region growing, PSO and U-net method scored 92%, 94%, and 96%, respectively. The result shows that the proposed method outperform better in localization and segmentation of IAC and its nerves. Thus, AI aids the radiologists in making the right decisions as the localization and segmentation of IAC is accurate.

Honey derivatized Au and Ag nanoparticles and evaluation of its antimicrobial activity.

Biomolecules hosting the synthesis of nanoparticles has achieved considerable attention in recent decades due to their abundant availability, excellent biocompatibility and low toxicity. The present study demonstrates a rapid, cost-effective and eco-friendly fabrication of gold and silver nanoparticles at room temperature using natural honey as a source of stabilizing and reducing agent. The nanoparticles obtained were unambiguously characterized by using various characterization techniques such as transmission electron microscopy (TEM), UV-Visible absorption spectroscopy, X-ray diffraction and energy dispersive (EDX) X-ray analysis. The average size of Au and Ag nanoparticles are 10 and 12 nm respectively. Ag nanoparticles capped by honey exhibited superior antimicrobial activity while Au nanoparticles revealed passable activity against pathogenic bacteria and Candida albicans, including multi-resistant strains for the first time.