Imaging in Sinonasal Disorders

Eggesbø, Heidi B.

doi:10.5772/intechopen.90773

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…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].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Analysing Paranasal Sinuses

Özbay

Tunç

2022

ELEKTRON ELEKTROTECH

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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 %.

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Section: Introductionmentioning

confidence: 99%

Deep Learning in Analysing Paranasal Sinuses

Özbay

Tunç

2022

ELEKTRON ELEKTROTECH

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Machine learning framework for simulation of artifacts in paranasal sinuses diagnosis using CT images

Musleh

2024

XST

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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.

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Imaging in Sinonasal Disorders

Cited by 2 publications

References 21 publications

Deep Learning in Analysing Paranasal Sinuses

Deep Learning in Analysing Paranasal Sinuses

Machine learning framework for simulation of artifacts in paranasal sinuses diagnosis using CT images

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