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Intelligent Decision Support System for Differential Diagnosis of Chronic Odontogenic Rhinosinusitis Based on U-Net Segmentation

  • 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.

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Metadaten
Author:Victoriia AlekseevaORCiD, Alina NechyporenkoORCiD, Marcus FrohmeORCiDGND, Vitaliy GarginORCiD, Ievgen Meniailov, Dmytro ChumachenkoORCiD
URN:urn:nbn:de:kobv:526-opus4-17088
DOI (Version of Record):https://doi.org/10.3390/electronics12051202
ISSN:2079-9292
Parent Title (English):Electronics
Publisher:MDPI
Document Type:Article
Language:English
Year of Publication:2023
Publishing Institution:Technische Hochschule Wildau
Release Date:2023/03/07
Tag:CT data; U-Net; artificial intelligence; chronic odontogenic rhinosinusitis; decision support system; deep learning; image segmentation; machine learning
Volume:12
Issue:5
Article Number:1202
Source:Alekseeva, V.; Nechyporenko, A.; Frohme, M.; Gargin, V.; Meniailov, I.; Chumachenko, D. Intelligent Decision Support System for Differential Diagnosis of Chronic Odontogenic Rhinosinusitis Based on U-Net Segmentation. Electronics 2023, 12, 1202. https://doi.org/10.3390/electronics12051202
Faculties an central facilities:Fachbereich Ingenieur- und Naturwissenschaften
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 616 Krankheiten
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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