Наукові праці. Кафедра оториноларингології

Permanent URI for this collectionhttps://repo.knmu.edu.ua/handle/123456789/1555

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Subject: search.filters.subject.2023а

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    Mechanisms of action of different groups of drugs in meniere’s disease
    (2023) Radchenko, Pavlo; Abramenko, Valeriia; Dzyza, Alla
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    Peculiarities of сraniometric indicators of the facial skull of a mature person according to computer tomography data
    (2023) Sosonna, L.; Ostapchuk, K.; Yurevych, N.; Babiy, L.; Sazonova, O.; Trach, O.; Alekseeva, V.
    The presented results of the study were conducted to determine the features of the cranio-metric indicators of the facial skull in mature individuals based on computer tomography data. A total of 40 participants, comprising 20 men and 20 women aged between 44 and 60 years, were includ-ed in the study. CT scans were performed on these individuals, and the following cranio-metric parameters were examined: cranial index, upper facial index, width of the upper jaw, and maximum width of the fore-head. During the study, it was found that the average cranial index was 77.3±1.85%. The upper facial index showed values of 53.37±3.57%, while the average width of the forehead was 13.75±0.5x10-2 m. Among the female participants, the majority exhibited a mesocranial type of skull (95% of individuals), with only 5% having a dolichocranial type. The average cranial index for these women was 78.1%. The mean cranial index for the entire sample remained consistent at 77.3±1.85%. Additionally, the mean upper facial index was 53.37±3.57%. The upper facial index is indicative of the ratio of the width of the upper face to its length and serves as a valuable tool for analyzing facial profiles. The average forehead width measured 13.75±0.5 x 10-2 m. These facial skull structure indicators can prove valuable in the planning of surgical interventions within the facial skull region, enabling a more precise analysis of individual patient characteristics and the determi-nation of optimal surgical approaches.
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    Investigation of the Impact of Insulin Resistance on the Bone Density of the Upper Wall of the Maxillary Sinus
    (2023-11) Alekseeva, Victoriia; Reshetnik, Viktor; Frohme, Marcus; Irina, Kachailo; Irina, Muryzina; Nechyporenko, Alina
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    Identification of Personality Based on the Sphenoid Sinus Structure Using Machine Learning
    (2023-11) Nechyporenko, Alina; Frohme, Marcus; Omelchenko, Vladyslav; Alekseeva, Victoriia; Lupyr, Andrii; Gargin, Vitaliy
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    The Peculiarities of Measuring Bone Density in Males and Females Using Uncertainty Calculation
    (2023-11) Nechyporenko, Alina; Reshetnik, Viktor; Dzyza, Alla; Alekseeva, Victoriia; Lupyr, Andrii; Gargin, Vitaliy
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    Intelligent Decision Support System for Differential Diagnosis of Chronic Odontogenic Rhinosinusitis Based on U-Net Segmentation
    (2023-03) Nechyporenko, Alina; Frohme, Marcus; Gargin, Vitaliy; Meniailov, Ievgen; Chumachenko, Dmytro; Alekseeva, Victoriia
    The share of chronic odontogenic rhinosinusitis is 40% among all chronic rhinosinusitis. Using automated information systems for differential diagnosis will improve the efficiency of decision-making by doctors in diagnosing chronic odontogenic rhinosinusitis. Therefore, this study aimed to develop an intelligent decision support system for the differential diagnosis of chronic odontogenic rhinosinusitis based on computer vision methods. A dataset was collected and processed, including 162 MSCT images. A deep learning model for image segmentation was developed. A 23 convolutional layer U-Net network architecture has been used for the segmentation of multi-spiral computed tomography (MSCT) data with odontogenic maxillary sinusitis. The proposed model is implemented in such a way that each pair of repeated 3 × 3 convolutions layers is followed by an Exponential Linear Unit instead of a Rectified Linear Unit as an activation function. The model showed an accuracy of 90.09%. To develop a decision support system, an intelligent chatbot allows the user to conduct an automated patient survey and collect patient examination data from several doctors of various profiles. The intelligent information system proposed in this study made it possible to combine an image processing model with a patient interview and examination data, improving physician decision-making efficiency in the differential diagnosis of Chronic Odontogenic Rhinosinusitis. The proposed solution is the first comprehensive solution in this area.