Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/20025
Title: Advanced Diagnostics of Respiratory Distress Syndrome in Premature Infants Treated with Surfactant and Budesonide through Computer-Assisted Chest X-ray Analysis
Authors: Prodanovic, Tijana
Petrovic Savic, Suzana
Prodanovic, Nikola
Simovic, Aleksandra
Zivojinovic, Suzana
Cekovic Djordjevic, Jelena
Savic, Dragana
Issue Date: 2024
Abstract: This research addresses the respiratory distress syndrome (RDS) in preterm newborns caused by insufficient surfactant synthesis, which can lead to serious complications, including pneumothorax, pulmonary hypertension, and pulmonary hemorrhage, increasing the risk of a fatal outcome. By analyzing chest radiographs and blood gases, we specifically focus on the significant contributions of these parameters to the diagnosis and analysis of the recovery of patients with RDS. The study involved 32 preterm newborns, and the analysis of gas parameters before and after the administration of surfactants and inhalation corticosteroid therapy revealed statistically significant changes in values of parameters such as FiO2, pH, pCO2, HCO3, and BE (Sig. < 0.05), while the pO2 parameter showed a potential change (Sig. = 0.061). Parallel to this, the research emphasizes the development of a lung segmentation algorithm implemented in the MATLAB programming environment. The key steps of the algorithm include preprocessing, segmentation, and visualization for a more detailed understanding of the recovery dynamics after RDS. These algorithms have achieved promising results, with a global accuracy of 0.93 ± 0.06, precision of 0.81 ± 0.16, and an F-score of 0.82 ± 0.14. These results highlight the potential application of algorithms in the analysis and monitoring of recovery in newborns with RDS, also underscoring the need for further development of software solutions in medicine, particularly in neonatology, to enhance the diagnosis and treatment of preterm newborns with respiratory distress syndrome.
URI: https://scidar.kg.ac.rs/handle/123456789/20025
Type: article
DOI: 10.3390/diagnostics14020214
ISSN: 2075-4418
Appears in Collections:Faculty of Engineering, Kragujevac

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