CT patterns of viral pneumonia are usually only qualitatively described in radiology reports. Artificial intelligence enables automated and reliable segmentation of lungs with chest CT. Based on this, the purpose of this study was to derive meaningful imaging biomarkers reflecting CT patterns of viral pneumonia and assess their potential to discriminate between healthy lungs and lungs with viral pneumonia. This study used non-enhanced and CT pulmonary angiograms (CTPAs) of healthy lungs and viral pneumonia (SARS-CoV-2, influenza A/B) identified by radiology reports and RT-PCR results. After deep learning segmentation of the lungs, histogram-based and threshold-based analyses of lung attenuation were performed and compared. The derived imaging biomarkers were correlated with parameters of clinical and biochemical severity (modified WHO severity scale; c-reactive protein). For non-enhanced CTs (n = 526), all imaging biomarkers significantly differed between healthy lungs and lungs with viral pneumonia (all p < 0.001), a finding that was not reproduced for CTPAs (n = 504). Standard deviation (histogram-derived) and relative high attenuation area [600–0 HU] (HU-thresholding) differed most. The strongest correlation with disease severity was found for absolute high attenuation area [600–0 HU] (r = 0.56, 95% CI = 0.46–0.64). Deep-learning segmentation-based histogram and HU threshold analysis could be deployed in chest CT evaluation for the differentiating of healthy lungs from AP lungs.

Статья посвящена проблеме выявления пожароопасных ситуаций на морском транспорте с использованием математического моделирования на основе системы нечеткого вывода. Подробно описана поэтапная процедура нечеткого вывода: составление базы правил, фаззификация входных переменных, агрегирование подусловий, активация подзаключений, аккумулирование заключений, дефаззификация выходных переменных. Объяснен алгоритм формирования нечеткой базы знаний для обеспечения пожарной безопасности на корабле на основе алгоритма Мамдани. Выбран и обоснован вид функции принадлежности. Разработанная модель предназначена для использования в алгоритмическом обеспечении корабельных систем безопасности и борьбы за живучесть с целью повышения обоснованности принятых решений при контроле пожарной опасности. Указанное повышение обоснованности основывается на индивидуализации критериев определения степени пожарной опасности для каждого помещения и постоянном обновлении базы данных с численными значениями факторов, характеризующих пожарную опасность.

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The article describes the procedure of marine fire-dangerous situations factors’ values forecasting based on artificial neural network. These factors are temperature, optical air density, aerosol concentration. Given procedure is flexible and can be expanded for other factors of fire-safety state of monitored object. Artificial neural network with architecture of three-layer perceptron is used for forecasting. The article gives a common scheme for realization of fire-dangerous situations factors’ values forecasting, substantiates the choice of used artificial neural network’s architecture, gives perceptron learning algorithm. As a result of given procedure execution factors’ values forecasting is implemented for prevention of fire-dangerous situation and the adoption of early actions. In case of integration of the developed procedure inside ship information management systems’ algorithmic support is capable of dramatically raise effectiveness of decisions made while providing fire safety on ships.