Abstract
Background: More data needed about the variations of the skull base and relationships between the structures which form the skull base and paranasal sinuses to minimize the complications during endoscopic sinus surgery.
The aim of this study was to investigate the relationships between Cribriform Plate (CP) dimensions, frontal sinus ostium sizes and frontal sinus volumes.
Materials and Methods: We measured the depth and width of the CP, the larger anteroposterior and side-to-side diameter of the frontal sinus ostia and the frontal sinus volumes on Computerized Tomography (CT) images of 120 adult subjects.
Results: The mean depth of CP was 8.43 ± 1.62 mm at the right side and 8.48 ± 1.66 at the left side. The mean frontal sinus volumes were 4.83 ± 3.58 cm3 at the right side and 5.09 ± 3.74 cm3 at the left side. There were significantly larger frontal sinus volumes in males (male/female median values: 4.50/3.54 cm3 ; p=0.03). There were significant positive correlations between CP depth, larger side-to-side and anteroposterior diameter of the frontal sinus ostium and frontal sinus volume (p<0.01). There was a significant negative correlation between larger side-to-side diameter of the frontal sinus ostium and CP width (p:0.041). The presence of ipsilateral hyperpneumatized frontal sinus and deep CP should be taken into consideration to avoid iatrogenic injury. In light of our results, surgeons should remember that the frontal recess may be narrow if the CP is wider than 4.5 mm in preoperative evaluation.
Keywords: Cribriform plate, frontal sinus, paranasal, computed tomography, frontal sinus volume, hyperpneumatized.
Graphical Abstract
Call for Papers in Thematic Issues
Intelligent Information Retrieval for Multispectral and Hyperspectral Imaging
Multispectral and hyperspectral imaging is an interdisciplinary research area, widely focuses on spectral, spatial, and temporal data. It offers a plethora of opportunities to efficiently analyze the vast area of the earth's surface. However, there are numerous factors such as dimensionality and size of the hyperspectral data, lesser training samples, ...read more
Machine and Deep Learning in Medical Image Diagnosis
Medical Image Processing regards a set of methodologies that have been developed over recent years with the purpose of improving medical image quality, improving medical data visualization, understanding, and assisting medical diagnosis, and so on. The application of machine and deep learning methods in sensing and imaging can potentially have ...read more
Quantum Machine Learning for Medical Data and Imaging
Quantum computing has promised a significant speedup in certain computationally intensive tasks that are intractable on classical computers. Researchers in quantum machine learning, such as those working in computer vision, image processing, biomedical analysis, and related topics, may play an important role in comprehending and working on complicated medical data, ...read more
14
3
1
1