TY - JOUR A1 - Alekseeva, Victoriia A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Gargin, Vitaliy A1 - Meniailov, Ievgen A1 - Chumachenko, Dmytro T1 - Intelligent Decision Support System for Differential Diagnosis of Chronic Odontogenic Rhinosinusitis Based on U-Net Segmentation JF - Electronics N2 - 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. KW - CT data KW - deep learning KW - image segmentation KW - U-Net KW - decision support system KW - chronic odontogenic rhinosinusitis KW - machine learning KW - artificial intelligence Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17088 SN - 2079-9292 VL - 12 IS - 5 PB - MDPI ER - TY - CHAP A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Omelchenko, Vladyslav A1 - Alekseeva, Victoriia A1 - Lupyr, Andrii A1 - Gargin, Vitaliy ED - Shakhovska, Nataliya ED - Kovác, Michal ED - Izonin, Ivan ED - Chrétien, Stéphane T1 - Identification of Personality Based on the Sphenoid Sinus Structure Using Machine Learning T2 - Proceedings of the 6th International Conference on Informatics & Data-Driven Medicine, Bratislava, Slovakia, November 17-19, 2023 N2 - The aim of our study is to develop a new, simple, and effective method for identification of personality based on the characteristics of the sphenoid sinus structure, using machine learning for subsequent implementation into routine medical practice in Ukraine. The study involved 200 multislice computed tomography (MSCT) scans of individuals of various genders and ages. During the study, we obtained results with an accuracy exceeding 70%. KW - identification of personality KW - multislice computed tomography KW - deep learning Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0074-3609-1 UR - https://ceur-ws.org/Vol-3609/short2.pdf VL - 3609 SP - 167 EP - 173 PB - CEUR-WS.org ER -