Facial paralysis can lead to dysfunctions in eyelid closure, which is called lagophthalmos. A number of surgical procedures, both dynamic and static, have been described to restore the innervation of the orbicularis oculi muscle that closes the eyelids. This cadaver-based anatomical study aimed to evaluate the anatomy of the anterior, middle, and posterior deep temporal nerves; nerves to the temporalis muscle; and their availability for direct muscle neurotization of the orbicularis oculi. A total of 10 hemisectioned head specimens from 5 adult cadavers (2 men and 3 women) were used in this study. The adequacy of the length of the anterior deep temporal nerve was assessed for direct neorotization of the orbicularis oculi muscle. The mean distances between the originating point of the deep temporal nerves from the mandibular nerve in the infratemporal fossa and their terminal entry points into the muscle were 46.4 (42-51 mm), 42.2 (38-46 mm), and 33.4 mm (26-40 mm) for the anterior, middle and posterior branches of the nerves, respectively. We conclude that the anterior deep temporal nerve is a versatile nerve that can be used for direct muscle neurotization, nerve transfer, and babysitter procedures in selective blinking restoration. Before proceeding with any further clinical use, an anatomical study should be performed with fresh specimens from cadavers.

Download full-text PDF

Source
http://dx.doi.org/10.1097/SAP.0000000000000552DOI Listing

Publication Analysis

Top Keywords

deep temporal
20
anterior deep
12
temporal nerve
12
orbicularis oculi
12
anatomy anterior
8
blinking restoration
8
facial paralysis
8
oculi muscle
8
anatomical study
8
anterior middle
8

Similar Publications

Groundwater monitoring is a crucial part of groundwater remediation that produces data from various strategically placed wells to maintain a water quality standard. Using the United States Department of Energy's Hanford 100-HRD area well data, recurrent neural networks are trained in the form of one-dimensional Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM) networks, and Dual-stage Attention-based LSTM (DA-LSTM) networks to reduce monitoring costs and increase data sampling responsiveness that is subject to laboratory analysis delays, with the best network being DA-LSTM achieving an R score of 0.82.

View Article and Find Full Text PDF

Air pollution monitoring and modeling are the most important focus of climate and environment decision-making organizations. The development of new methods for air quality prediction is one of the best strategies for understanding weather contamination. In this research, different air quality parameters were forecasted, including Carbon Monoxide (CO), Nitrogen Monoxide (NO), Nitrogen Dioxide (NO), Ozone (O), Sulphur Dioxide (SO), Fine Particles Matter (PM), Coarse Particles Matter (PM), and Ammonia (NH).

View Article and Find Full Text PDF

Human motion similarity evaluation based on deep metric learning.

Sci Rep

December 2024

College of Sports, Beihua University, Jilin, 132000, China.

In order to eliminate the impact of camera viewpoint factors and human skeleton differences on the action similarity evaluation and to address the issue of human action similarity evaluation under different viewpoints, a method based on deep metric learning is proposed in this article. The method trains an automatic encoder-decoder deep neural network model by means of a homemade synthetic dataset, which maps the 2D human skeletal key point sequence samples extracted from motion videos into three potential low-dimensional dense spaces. Action feature vectors independent of camera viewpoint and human skeleton structure are extracted in the low-dimensional dense spaces, and motion similarity metrics are performed based on these features, thereby effectively eliminating the effects of camera viewpoint and human skeleton size differences on motion similarity evaluation.

View Article and Find Full Text PDF

In the field of rehabilitation, although deep learning have been widely used in multitype gesture recognition via surface electromyography (sEMG), their higher algorithmic complexity often leads to low computationally inefficient, which compromise their practicality. To achieve more efficient multitype recognition, We propose the Residual-Inception-Efficient (RIE) model, which integrates Inception and efficient channel attention (ECA). The Inception, which is a multiscale fusion convolutional module, is adopted to enhance the ability to extract sEMG features.

View Article and Find Full Text PDF

The eruption in Fagradalsfjall Volcano, located in Reykjanes Peninsula, Iceland, from several centuries' dormant states, occurred for the first time on March 19, 2021. Observations of Fagradalsfjall Volcano were conducted in 2021, and the eruption period lasted for six months until 18 September 2021. Six days pair of interferograms were generated from ninety synthetic aperture radar (SAR) data.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!