Figure 17 - available via license: Creative Commons Attribution 3.0 Unported
Content may be subject to copyright.
(a) Coronal, (b) axial, and (c) sagittal CT of an inverted papilloma originating from the ethmoid sinus and filling the middle part of the nasal cavity and choana. The site of origin often shows hyperkeratosis. In this case the origin could be from beneath the ethmoid cells that shows slight keratosis at the sagittal CT.
Source publication
Computed tomography (CT) is the “working horse” in sinonasal imaging and should always be the first choice. Magnetic resonance imaging (MRI) is comple- mentary to CT when complications to rhinosinusitis or neoplasm are suspected. Imaging of the paranasal sinuses is common due to stuffy nose. In order to correct interpretation, proper imaging techni...
Similar publications
Chronic rhinosinusitis (CRS) can have a significant impact on quality of life. With persistent symptoms and the failure of initial medical treatments, surgical management is indicated. Despite the excellent results of endoscopic sinus surgery for persistent CRS, it is quite a challenging procedure for frontal sinusitis given the complex anatomy and...
Citations
... CT scans allow the details of the osseous anatomy to be seen, and MRI scans give the details of the soft tissues. Although both provide essential information for the interpretation and treatment of various paranasal sinus pathologies, CT scan should always be considered the first choice in sinonasal imaging, and MRI scan is treated as supplementary to CT scan [5]. ...
Deep neural network-based diagnostic tools have gained state-of-the-art performance in the medical field in recent years. Diagnostic accuracy has become very critical for medical treatments. This paper proposes a simple and novel deep learning-based system for the analysis of paranasal sinuses conditions. In this work, we focus on analysing the paranasal sinuses on CT images automatically, providing physicians with high-accuracy diagnosis. The proposed system enables one to reduce the number of images to be searched in a CT scan for a patient automatically, and also it provides automatic segmentation for marking and cropping the paranasal sinuses region. Thus, the proposed system significantly decreases the data required in the training phase with a gain in computational efficiency while maintaining high-accuracy performance. The proposed algorithm also makes the required segmentation automatically without manual cropping and yields outstanding performance on detecting abnormalities in the sinuses. The proposed approach has been tested on real CT images and achieved an accuracy rate of 98.52 % with a sensitivity of 100 %.
... Dental sinusitis remains a common cause for maxillary sinusitis, therefore CT sinuses should always include the upper jaw teeth to allow for complete evaluation [12]. CT sinuses for patients with acute rhinosinusitis typically demonstrates sinus opacity and mucosal thickening (Figures 2, 3&4) [13]. Paranasal sinus air-fluid level is often demonstrated; however should be correlated clinically, as there are several causative differentials, depending on site (Table 4). ...
Acute rhinosinusitis (ARS) is an inflammatory process of the nasal mucosa and paranasal sinuses, usually caused by viral infection and is usually self-resolving . The true incidence of acute virus rhinosinusitis has always been described as high, with an estimation that shows adults are likely to suffer two to five episodes of viral ARS per year, in comparison to seven to ten episodes per year in children. Post-viral ARS occurs as a perpetuation of the inflammatory condition, even when the viral agent has been eliminated, with a reported estimate of 3.4 cases per 100/year. It is also estimated that only 0.5–2% would progress to Acute Bacterial Rhinosinusitis (ABRS).The incidence of ABRS complications is approximately 3 per million of the population per year, despite vastly different utilization of antibiotics in various countries. This figure has not reduced significantly despite the advent of widespread antibiotic prescribing. Delays in starting treatment, incomplete antibiotic regiment or immunosuppression are some of the risk factors leading to complications from acute rhinosinusitis. Complications of ABRS include periorbital, intra-cranial and osseous conditions which are potentially life-threatening, where an early commencement of intravenous antibiotics and /or surgical drainage is carried out.
This is imperative to avoid long term sequelae. In all studies, orbital complications are the most frequent while osseous appear to be relatively uncommon, with males significantly more affected than females. Orbital complications appear more common in children while intracranial complications can occur at any age, with a preponderance of young adults. Anatomy of paranasal sinus venous drainage and cerebral venous drainage up to 3% of patients with ARS present with orbital inflammation including preseptal cellulitis and orbital cellulitis, orbital abscess and subperiosteal empyema. Orbital inflammatory complications are often caused by infection of ethmoid or frontal sinus. The valveless ethmoidal veins drain into ophthalmic veins, thus increasing risk of cavernous venous sinus thrombosis from ethmoid sinusitis. Approximately 3% of intracranial complications such as cerebritis, subdural empyema, meningitis, venous sinus thrombosis and cerebral abscess are due to sinusitis.
In the medical field, diagnostic tools that make use of deep neural networks have reached a level of performance never before seen. A proper diagnosis of a patient’s condition is crucial in modern medicine since it determines whether or not the patient will receive the care they need. Data from a sinus CT scan is uploaded to a computer and displayed on a high-definition monitor to give the surgeon a clear anatomical orientation before endoscopic sinus surgery. In this study, a unique method is presented for detecting and diagnosing paranasal sinus disorders using machine learning. The researchers behind the current study designed their own approach. To speed up diagnosis, one of the primary goals of our study is to create an algorithm that can accurately evaluate the paranasal sinuses in CT scans. The proposed technology makes it feasible to automatically cut down on the number of CT scan images that require investigators to manually search through them all. In addition, the approach offers an automatic segmentation that may be used to locate the paranasal sinus region and crop it accordingly. As a result, the suggested method dramatically reduces the amount of data that is necessary during the training phase. As a result, this results in an increase in the efficiency of the computer while retaining a high degree of performance accuracy. The suggested method not only successfully identifies sinus irregularities but also automatically executes the necessary segmentation without requiring any manual cropping. This eliminates the need for time-consuming and error-prone human labor. When tested with actual CT scans, the method in question was discovered to have an accuracy of 95.16 percent while retaining a sensitivity of 99.14 percent throughout.
