98%
921
2 minutes
20
Objective: This study sought to analyze the correlational relationships between mastoid air cells, the area of the facial recess entrance, and the location of the vertical segment of the facial nerve.
Methods: CT scans of 60 ears from 30 patients were analysed. We used MIMICS to obtain inner ear coordinates in CT images. Mastoid air cells were analysed using CT value thresholds to classify them into three groups based on volume: Group A (< 5 mL), Group B (5-8 mL), and Group C (> 8 mL). The coordinates of the inner ear structures were entered into MATLAB to calculate the area of the entrance to the facial recess and the distance from the vertical segment of the facial nerve to the standard coronal and median sagittal planes.
Results: No significant differences in mastoid air cells volume between gender or side; the entrance area of the facial recess did not differ significantly among Groups A, B, and C; there was no significant relationship between the midpoint of the vertical segment of the facial nerve and the median sagittal plane (P > 0.05). However, the distance from the posterior point of the annulus to the midpoint pyramidal segment of the facial nerve and the distance between the midpoint of the vertical segment of the facial nerve and the standard coronal plane decreased progressively in Groups A, B, and C (P < 0.05).
Conclusion: The analyses showed no link between mastoid air cells volume and gender, side, or area of the facial recess entrance. Better mastoid pneumatisation correlates with a closer proximity of the annulus to the facial nerve and a more anterior position of the vertical segment of the facial nerve.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s00276-025-03585-0 | DOI Listing |
PLoS One
September 2025
Symbiosis Institute of Technology, Symbiosis International University, Pune, India.
With the rapid development of industrial automation and intelligent manufacturing, defect detection of electronic products has become crucial in the production process. Traditional defect detection methods often face the problems of insufficient accuracy and inefficiency when dealing with complex backgrounds, tiny defects, and multiple defect types. To overcome these problems, this paper proposes Y-MaskNet, a multi-task joint learning framework based on YOLOv5 and Mask R-CNN, which aims to improve the accuracy and efficiency of defect detection and segmentation in electronic products.
View Article and Find Full Text PDFFront Digit Health
August 2025
Architecture Laboratory, Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Japan.
Background: Microwave Doppler sensors, capable of detecting minute physiological movements, enable the measurement of biometric information, such as walking patterns, heart rate, and respiration. Unlike fingerprint and facial recognition systems, they offer authentication without physical contact or privacy concerns. This study focuses on non-contact seismocardiography using microwave Doppler sensors and aims to apply this technology for biometric authentication.
View Article and Find Full Text PDFFront Plant Sci
August 2025
Engineering Research Center of Edibleand Medicinal Fungi, Ministry of Education, Jilin Agricultural University Changchun, Changchun, China.
Traditional path planning algorithms often face problems such as local optimum traps and low monitoring efficiency in agricultural UAV operations, making it difficult to meet the operational requirements of complex environments in modern precision agriculture. Therefore, there is an urgent need to develop an intelligent path planning algorithm. To address this issue, this study proposes an improved Informed-RRT* path planning algorithm guided by domain-partitioned A* algorithm.
View Article and Find Full Text PDFJ Med Imaging (Bellingham)
September 2025
Vanderbilt University, Data Science Institute, Nashville, Tennessee, United States.
Purpose: Recent developments in computational pathology have been driven by advances in vision foundation models (VFMs), particularly the Segment Anything Model (SAM). This model facilitates nuclei segmentation through two primary methods: prompt-based zero-shot segmentation and the use of cell-specific SAM models for direct segmentation. These approaches enable effective segmentation across a range of nuclei and cells.
View Article and Find Full Text PDFFront Artif Intell
August 2025
Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada.
Precision livestock farming increasingly relies on non-invasive, high-fidelity systems capable of monitoring cattle with minimal disruption to behavior or welfare. Conventional identification methods, such as ear tags and wearable sensors, often compromise animal comfort and produce inconsistent data under real-world farm conditions. This study introduces Dairy DigiD, a deep learning-based biometric classification framework that categorizes dairy cattle into four physiologically defineda groups-young, mature milking, pregnant, and dry cows-using high-resolution facial images.
View Article and Find Full Text PDF