Fuzzy-Based Bioengineering System for Predicting and Diagnosing Diseases of the Nervous System Triggered by the Interaction of Industrial Frequency Electromagnetic Fields.

The study aims to enhance the standard of medical care for individuals working in the electric power industry who are exposed to industrial frequency electromagnetic fields and other relevant risk factors. This enhancement is sought through the integration of fuzzy mathematical models with contemporary information and intellectual technologies. The study addresses the challenges of forecasting and diagnosing illnesses within a specific demographic characterized by a combination of poorly formalized issues with interconnected conditions. To tackle this complexity, a methodological framework was developed for synthesizing hybrid fuzzy decision rules. This approach combines clinical expertise with artificial intelligence methodologies to promote innovative problem-solving strategies. Additionally, the researchers devised an original method to evaluate the body's protective capacity, which was integrated into these decision rules to enhance the precision and efficacy of medical decision-making processes. The research findings indicate that industrial frequency electromagnetic fields contribute to illnesses of societal significance. Additionally, it highlights that these effects are worsened by other risk factors such as adverse microclimates, noise, vibration, chemical exposure, and psychological stress. Diseases of the neurological, immunological, cardiovascular, genitourinary, respiratory, and digestive systems are caused by these variables in conjunction with unique physical traits. The development of mathematical models in this study makes it possible to detect and diagnose disorders in workers exposed to electromagnetic fields early on, especially those pertaining to the autonomic nervous system and heart rhythm regulation. The results can be used in clinical practice to treat personnel in the electric power industry since expert evaluation and modeling showed high confidence levels in decision-making accuracy.

Using Fuzzy Mathematical Model in the Differential Diagnosis of Pancreatic Lesions Using Ultrasonography and Echographic Texture Analysis.

Malignant tumors of the pancreas are the fourth leading cause of cancer-related deaths. This is mainly because they are often diagnosed at a late stage. One of the challenges in diagnosing focal lesions in the pancreas is the difficulty in distinguishing them from other conditions due to the unique location and anatomy of the organ, as well as the similarity in their ultrasound characteristics. One of the most sensitive imaging modalities of the pancreas is endoscopic ultrasonography. However, clinicians recognize that EUS is a difficult and highly operator-dependent method, while its results are highly dependent on the experience of the investigator. Hybrid technologies based on artificial intelligence methods can improve the accuracy and objectify the results of endosonographic diagnostics. Endoscopic ultrasonography was performed on 272 patients with focal lesions of the pancreatobiliary zone, who had been treated in the surgical section of the Kursk Regional Clinical Hospital in 2014-2023. The study utilized an Olympus EVIS EXERA II video information endoscopic system, along with an EU-ME1 ultrasound unit equipped with GF UM160 and GF UC140P-AL5 echo endoscopes. Out of the focal formations in the pancreatobiliary zone, pancreatic cancer was detected in 109 patients, accounting for 40.1% of the cases. Additionally, 40 patients (14.7%) were diagnosed with local forms of chronic pancreatitis. The reference sonograms displayed distinguishable focal pancreatic pathologies, leading to the development of hybrid fuzzy mathematical decision-making rules at the South-West State University in Kursk, Russian Federation. This research resulted in the creation of a fuzzy hybrid model for the differential diagnosis of chronic focal pancreatitis and pancreatic cancer. Endoscopic ultrasonography, combined with hybrid fuzzy logic methodology, has made it possible to create a model for differentiating between chronic focal pancreatitis and pancreatic ductal adenocarcinoma. Statistical testing on control samples has shown that the diagnostic model, based on reference endosonograms of the echographic texture of pancreatic focal pathology, has a confidence level of 0.6 for the desired diagnosis. By incorporating additional information about the contours of focal formations obtained through endosonography, the reliability of the diagnosis can be increased to 0.9. This level of reliability is considered acceptable in clinical practice and allows for the use of the developed model, even with data that is not well-structured.

Differential Diagnosis of Pancreatic Cancer and Chronic Pancreatitis According to Endoscopic Ultrasonography Based on the Analysis of the Nature of the Contours of Focal Formations Based on Fuzzy Mathematical Models.

One of the key echographic signs of focal pathology of the pancreas is the presence of formation contours and their nature. Endoscopic ultrasonography has a unique ability to visualize the echographic texture of the pancreatic parenchyma, and also allows you to assess in detail the boundaries and nature of the contours of the tumor formations of the organ due to the proximity of the ultrasound sensor. However, the differential diagnosis of focal pancreatic lesions remains a difficult clinical task due to the similarity of their echosemiotics. One of the ways to objectify and improve the accuracy of ultrasound data is the use of artificial intelligence methods for interpreting images. Improving the quality of differential diagnosis of focal pathology of the pancreas according to endoscopic ultrasonography based on the analysis of the nature of the contours of focal formations using fuzzy mathematical models.

Method for Determining the Body's Level of Protection According to Oxidant Status in Assessing the Influence of Industrial Risk Factors on Health.

This work aims at improving the quality of health assessments, specifically under the influence of occupational risk factors. For this purpose, additional informative indicators are utilized in prognostic and diagnostic models. The models are used to characterize the level of body protection based on oxidative status. A quantitative method is proposed to assess the body's level of protection by means of the levels of lipid peroxidation and antioxidant activity, which characterize the body's oxidative status. A mechanism is developed for integrating the proposed method into prognostic and diagnostic decision rules. The developed rules are in the form of mathematical models used to synthesize hybrid fuzzy decision rules, which are then used to quantify the level of body protection (LBP) against external risk factors, based on the use of protection level functions in terms of lipid peroxidation and antioxidant activity. A mechanism for embedding LBP into predictive and diagnostic decision rules has been proposed. The proposed method is used to predict the occurrence and development of coronary heart disease in railroad locomotive drivers. It was found that to improve the predicting and diagnosing of diseases caused by external pathogenic factors, quantitative assessments of LBP, determined by oxidative status, can be implemented. It has been established that the use of the protection level indicator in predictive decision rules makes it possible to increase the efficiency of the prediction while simultaneously increasing its accuracy.

Classifier for the functional state of the respiratory system via descriptors determined by using multimodal technology.

Currently, intelligent systems built on a multimodal basis are used to study the functional state of living objects. Its essence lies in the fact that a decision is made through several independent information channels with the subsequent aggregation of these decisions. The method of forming descriptors for classifiers of the functional state of the respiratory system includes the study of the spectral range of the respiratory rhythm and the construction of the wavelet plane of the monitoring electrocardiosignal overlapping this range. Then, variations in the breathing rhythm are determined along the corresponding lines of the wavelet plane. Its analysis makes it possible to select slow waves corresponding to the breathing rhythm and systemic waves of the second order. Analysis of the spectral characteristics of these waves makes it possible to form a space of informative features for classifiers of the functional state of the respiratory system. To construct classifiers of the functional state of the respiratory system, hierarchical classifiers were used. As an example, we took a group of patients with pneumonia with a well-defined diagnosis (radiography, X-ray tomography, laboratory data) and a group of volunteers without pulmonary pathology. The diagnostic sensitivity of the obtained classifier was 76% specificity with a diagnostic specificity of 82%, which is comparable to the results of X-ray studies. It is shown that the corresponding lines of the wavelet planes are correlated with the respiratory system and, using their Fourier analysis, descriptors can be obtained for training neural network classifiers of the functional state of the respiratory system.

Biotechnical system based on fuzzy logic prediction for surgical risk classification using analysis of current-voltage characteristics of acupuncture points.

OBJECTIVE: This study aimed to develop expert fuzzy logic model to assist physicians in the prediction of postoperative complications of prostatic hyperplasia before surgery. METHODS: A method for classification of surgical risks was developed. The effect of rotation of the current-voltage characteristics at biologically active points (acupuncture points) was used for the formation of classifier descriptors. The effect determined reversible and non-reversible changes in electrical resistance at acupuncture points with periodic exposure to a sawtooth probe current. Then, the developed method was tested on the prediction of the success of surgical treatment of benign prostatic hyperplasia. RESULTS: Input descriptors were obtained from collected data including current-voltage characteristics of 5 acupuncture points and composed of 27 arrays feeding in the model. The maximum diagnostic sensitivity of the classifier for the success of a surgical operation in the control sample was 88% and for testing data set prediction accuracy was 97%. CONCLUSION: The use of tuples of current-voltage characteristic descriptors of acupuncture points in the classifiers could be used to predict the success of surgical treatment with satisfactory accuracy. The model can be a valuable tool to support physicians' diagnosis.

Developing neural network model for predicting cardiac and cardiovascular health using bioelectrical signal processing.

Coronary vascular disease (CHD) is one of the most fatal diseases worldwide. Cardio vascular diseases are not easily diagnosed in early disease stages. Early diagnosis is important for effective treatment, however, medical diagnoses are based on physician's personal experiences of the disease which increase time and testing cost to reach diagnosis. Physicians assess patients' condition based on electrocardiography, sonography and blood test results. In this research we develop classification model of the functional state of the cardiovascular system based on the monitoring of the evolution of the amplitudes of the first and second harmonics of the system rhythm of 0.1 Hz. We separate the signal to three streams; the first stream works with natural electro cardio signal, the other two streams are obtained as a result of frequency analysis of the amplitude- and frequency-detected electro cardio signal. We use sliding window of a demodulated electro cardio signal by means of amplitude and frequency detectors. The developed NN model showed an increase in accuracy of diagnostic efficiency by 11%. The neural network model can be trained to give accurate early detection of disease class.

Application of fuzzy neural network model and current-voltage analysis of biologically active points for prediction post-surgery risks.

The work investigates neural network model for prediction of post-surgical treatment risks. The descriptors of the risk classifiers are formed on the basis of the analysis of the current-voltage characteristics of one, two and three biologically active points. The training and verification samples were formed by examining 120 patients with a diagnosis of benign prostatic hyperplasia. Of these, 62 patients were successfully operated on (class C1), 30 had various complications after surgery (class C2), 28 patients required additional treatment (class C3). The constructed classifiers showed a high quality of predicting critical conditions during surgical treatment.