Bionic models for identification of biological systems

This article proposes a clinical decision support system that processes biomedical data. For this purpose a bionic model has been designed based on neural networks, genetic algorithms and immune systems. The developed system has been tested on data from pregnant women. The paper focuses on the approach to enable selection of control actions that can minimize the risk of adverse outcome. The control actions (hyperparameters of a new type) are further used as an additional input signal. Its values are defined by a hyperparameter optimization method. A software developed with Python is briefly described

Use of artificial neural networks for differentiated diagnostics of ischemic and hemorrhagic perinatal affections of central neural system in newborns of different terms of gestation

The article describes neonatal and pediatric neurology researching. Among the causes of childhood disability first place belongs to diseases of the nervous system. Among perinatal brain damage leading place is occupied by cerebrovascular pathology. One of the main causes of hemorrhagic and ischemic brain damage is impaired cerebral hemodynamics. However there is no single point of view on the processes underlying the development of ischemic brain lesions and intracranial hemorrhage in premature infants. It reveals necessity of immunobiochemical neurospecific proteins defining during neonatal period. Proteins, namely neurospecific enolase, a neurotrophicfactor of nerve growth, vascularendothelial growth factor, allow early finding of pathological disorder. What is a profitable advantage compared to the widely used clinical and instrumental examination and laboratory methods to assist in determining location and extent of the brain. Articleshows importance for a multifunction-oriented model of studying peculiarities of the child, starting with finding patterns in complex processes, due to the influence of internal and external factors on the functional state of the organism based on its individual characteristics, and ending with the solution of problems of differential diagnosis. Thus enabling to seek for hidden dependencies in complex processes conditioned by internal and external factors, leading us to performing differential diagnosis. As for mathematical models and data processing algorithms, the authors used an artificial neural network. These algorithms are used when there is no a precise decision-makingsystem. The medical diagnosis of ischemic and hemorrhagic perinatal central nervous system lesions of newborns maybe added in the list problems to be solved by artificial neural networks. The paper gives valuable information aboutinvestigating child's body properties with neural networks algorithms. Results of applying these algorithms are aimed to increase accuracy of differential diagnosis of ischemic or hemorrhagic perinatal damage to the central nervous system in newborns of different gestational ages are presented

Segmentation of anatomical structures of the heart based on echocardiography

Nowadays, many practical applications in the field of medical image processing require valid and reliable segmentation of images in the capacity of input data. Some of the commonly used imaging techniques are ultrasound, CT, and MRI. However, the main difference between the other medical imaging equipment and EchoCG is that it is safer, low cost, non-invasive and non-traumatic. Three-dimensional EchoCG is a non-invasive imaging modality that is complementary and supplementary to two-dimensional imaging and can be used to examine the cardiovascular function and anatomy in different medical settings. The challenging problems, presented by EchoCG image processing, such as speckle phenomena, noise, temporary non-stationarity of processes, unsharp boundaries, attenuation, etc. forced us to consider and compare existing methods and then to develop an innovative approach that can tackle the problems connected with clinical applications. Actual studies are related to the analysis and development of a cardiac parameters automatic detection system by EchoCG that will provide new data on the dynamics of changes in cardiac parameters and improve the accuracy and reliability of the diagnosis. Research study in image segmentation has highlighted the capabilities of image-based methods for medical applications. The focus of the research is both theoretical and practical aspects of the application of the methods. Some of the segmentation approaches can be interesting for the imaging and medical community. Performance evaluation is carried out by comparing the borders, obtained from the considered methods to those manually prescribed by a medical specialist. Promising results demonstrate the possibilities and the limitations of each technique for image segmentation problems. The developed approach allows: to eliminate errors in calculating the geometric parameters of the heart; perform the necessary conditions, such as speed, accuracy, reliability; build a master model that will be an indispensable assistant for operations on a beating heart

Comparative Study of Deep Learning Models for Automatic Coronary Stenosis Detection in X-ray Angiography

The article explores the application of machine learning approach to detect both single-vessel and multivessel coronary artery disease from X-ray angiography. Since the interpretation of coronary angiography images requires interventional cardiologists to have considerable training, our study is aimed at analysing, training, and assessing the potential of the existing object detectors for classifying and detecting coronary artery stenosis using angiographic imaging series. 100 patients who underwent coronary angiography at the Research Institute for Complex Issues of Cardiovascular Diseases were retrospectively enrolled in the study. To automate the medical data analysis, we examined and compared three models (SSD MobileNet V1, Faster-RCNN ResNet-50 V1, FasterRCNN NASNet) with various architecture, network complexity, and a number of weights. To compare developed deep learning models, we used the mean Average Precision (mAP) metric, training time, and inference time. Testing results show that the training/inference time is directly proportional to the model complexity. Thus, Faster-RCNN NASNet demonstrates the slowest inference time. Its mean inference time per one image made up 880 ms. In terms of accuracy, FasterRCNN ResNet-50 V1 demonstrates the highest prediction accuracy. This model has reached the mAP metric of 0.92 on the validation dataset. SSD MobileNet V1 has demonstrated the best inference time with the inference rate of 23 frames per second

ВЫЯВЛЕНИЕ ЗАКОНОМЕРНОСТЕЙ ДИНАМИЧЕСКИХ ПРОЦЕССОВ НА ОСНОВЕ ЭНЕРГОИНФОРМАЦИОННЫХ ТЕХНОЛОГИЙ

The purpose of the research is developing of approaches and criterias that allow obtaining individual health value. Existing methods of diagnosing may be divided into two categories: statistic methods and functional dependency, based on metabolic energy-information process that takes place in organism.There is no doubt that wide spread statistic methods are beneficial and in most cases allow recognizing general trend of researched processes. But all statistic approaches share same drawback, they do not allow taking evincive conclusion while individual diagnosing and grading physician's impact in dynamics. Thus we face necessity for developing new ways of diagnosing, allowing individual health evaluating based on common trend. This task may be subdivided into: find common trend and recognize informative variables; identify anomalies and form homogeneous groups; apply methods to obtain average values, adequately revealing group functioning.The article, based on systematic approach, describes technology presenting the results of observations and child’s body is presented as complex dynamical system, that includes all required elements for the sustained functioning under external environment condition. Energy-informative approach is used for synthesizing generalized criteria for evaluating the functioning of dynamical biosystems.Given developed criteria and coefficients revealing the degree of tension in the body under normal functioning conditions. The average groups coefficient values are taken into consideration. Described energy-information approach enables determine state of normal or pathology functioning that may be crucial for children.Application of energy-information approach to evaluate the functioning of dynamic systems, allows re­cognizing common trend of researched objects and obtain individual health values. Such tasks are faced by practical and evidence-based medicine – finding individual health values, based on analysis of common trend of functioning.Целью исследования являлась разработка подходов и критериев, позволяющих получить индивидуальную оценку состояния здоровья. Все существующие в настоящее время подходы для диагностики состояния здоровья можно разделить на две группы: статистические методы и функциональные зависимости, формируемые на основе законов, определяющих обменные энергоинформационные процессы, происходящие в организме.Наиболее развитые и широко применяемые в настоящее время статистические методы являются безусловно полезными и в большинстве случаев дают возможность понять общую тенденцию изучаемых процессов. Однако статистические методы с точки зрения практической медицины обладают одним существенным недостатком, не позволяющим принимать доказательные выводы при индивидуальном прогнозе и выявлении воздействий лечебных процедур в динамике. В связи с этим существует необходимость в разработке новых подходов, позволяющих на фоне общих тенденций делать доказательные выводы о состоянии здоровья каждого обследуемого. Для достижения поставленной цели необходимо решить ряд задач: выявить общие закономерности и осуществить адекватный выбор наблюдаемых переменных; выделить аномальные наблюдения и сформировать однородные группы; выбрать методы, позволяющие получать усредненные оценки и адекватно отражающие функционирование выделенных однородных групп объектов.С позиции системного подхода рассмотрена технология представления результатов наблюдений, а организм ребенка представлен как сложная динамическая система, содержащая все необходимые элементы для устойчивого функционирования в условиях внешней среды. С целью формирования обобщенных критериев оценки функционирования динамической биосистемы приводится энергоинформационный подход.Приведены разработанные критерии и полученные коэффициенты, характеризующие степень напряженности организма в условиях нормального функционирования. Рассмотрены усредненные значения коэффициентов по группам. Представленный в статье энергоинформационный подход дает возможность выявить состояние организма на грани нормы и патологии, что особенно актуально для детей.Применение энергоинформационных технологий для оценки функционирования динамических систем позволяет выявлять закономерности группового поведения объектов исследования и получать индивидуальные оценки состояния. Такого рода задачи ставятся в практической и доказательной медицине, когда на фоне общих закономерностей функционирования сущуствует возможность делать индивидуальные оценки состояния

Outlier detection and classification in sensor data streams for proactive decision support systems

A paper has a deal with the problem of quality assessment in sensor data streams accumulated by proactive decision support systems. The new problem is stated where outliers need to be detected and to be classified according to their nature of origin. There are two types of outliers defined; the first type is about misoperations of a system and the second type is caused by changes in the observed system behavior due to inner and external influences. The proposed method is based on the data-driven forecast approach to predict the values in the incoming data stream at the expected time. This method includes the forecasting model and the clustering model. The forecasting model predicts a value in the incoming data stream at the expected time to find the deviation between a real observed value and a predicted one. The clustering method is used for taxonomic classification of outliers. Constructive neural networks models (CoNNS) and evolving connectionists systems (ECS) are used for prediction of sensors data. There are two real world tasks are used as case studies. The maximal values of accuracy are 0.992 and 0.974, and F1 scores are 0.967 and 0.938, respectively, for the first and the second tasks. The conclusion contains findings how to apply the proposed method in proactive decision support systems