RESEARCH TEAM, CONTRIBUTORS AND CO-AUTHORS OF SCIENTIFIC PUBLICATIONS

  • [Rozmiar: 11195 bajtów]
  • [Rozmiar: 11195 bajtów]
  • [Rozmiar: 11195 bajtów]

AGH RESEARCH TEAM "SMART-DEEP-BIOMED":

founded in 2020 and devoted to the implemenation of artificial intelligence to biomedical engineering issues

  • dr hab. Adrian Horzyk, prof. AGH (Team Leader)
  • dr ing Tomasz Orzechowski
  • dr ing Elżbieta Pociask
  • dr ing Joanna Grabska-Chrz±stowska
  • lek med. Daniel Bulanda (Ph.D. student)
  • mgr ing Dawid Bydłosz (Ph.D. student)

EXTERNAL COLLABORATORS:

SCOPE OF RESEARCH AND DEVELOPMENT INTERESTS:

The SMART-DEEP-BIOMED research team deals with the development of solutions in the field of modern artificial intelligence, deep learning, knowledge engineering and associative-cognitive knowledge-based smart systems for solving important tasks in the field of biomedical engineering and medicine. Our goal is to develop and improve methods, algorithms, and solutions to support the work of doctors in diagnostics, classification, prediction, detection and recommendation as well as gathering knowledge and optimizing methods of treating patients, and in prevention and warning based on the knowledge base as well as predictive and recommendation algorithms.

RESEARCH TOPICS:

PUBLIKACJE NAUKOWE:


DESCRIPTION OF THE STUDIES (since 2020):

As the latest research shows (Eric Topol, "Deep Medicine. How Artificial Intelligence Can Make Healthcare Human Again", New York, 2019), up to 60% of commissioned medical tests and procedures are carried out incorrectly, inaccurately or unnecessarily due to limited time for subjective examination and subject, familiarization with the history of the disease and erroneous, fast, inaccurate or insufficient differential analysis and diagnosis. This often results in improper treatment that does not bring about improvement in health, deterioration of health, or even death. The most common cause of death in Poland is cardiovascular disease (43.5% in 2017 according to the CSO Demographic Yearbook), which due to the complicated diagnostic and therapeutic process, lack of proper preparation of some doctors participating in the treatment process, lack of access to appropriate diagnostic procedures, as well as the lack of time necessary for adequate analysis of available medical data, are often inadequately treated, causing the early threats to be overlooked, leading to further complications, heart failure, and even death.

Electrocardiography (ECG) is one of the most frequently performed tests in Poland. According to the CSO report "Health and health care in 2016," as much as 13.2% of the population had an ECG during the year as part of outpatient care. This is due to, among others, low cost of testing, availability of apparatus, relatively simple operation of the equipment, non-invasiveness, and high diagnostic value of the examination. Electrocardiography and its variants, i.e., Holter ECG monitoring (continuous recording of ECG signal for 24 hours or several days) or exercise ECG, are indicated, among others in patients with previously diagnosed cardiovascular disease, in people with suspected cardiovascular disease and healthy people with an increased risk of cardiovascular disease. Besides, imaging tests, i.e., echocardiography, computed tomography, X-ray, cardiac magnetic resonance imaging, and coronary angiography, play an important role in the diagnostic process of heart disease. Echocardiography is one of the most frequently performed outpatient care tests (4.8% of the population in 2016, according to the Central Statistical Office). For many years, attempts have been made to computer support the diagnostic process based on ECG signals and cardiac imaging. Many of them, despite promising results, have not been implemented into clinical practice. This could have been caused, among others, by lack of a comprehensive and practical approach to the developed solutions enabling easy implementation into clinical practice, insufficient effectiveness of these methods, or a high demand for computing power, which for financial and organizational reasons is often not available in clinical practice.

The process of recognizing patterns and then extracting the knowledge contained in ECG signals and imaging of the heart is certainly not a trivial task while remaining very important from the point of view of potential benefits. Many approaches to the analysis of ECG signals by classical methods and those using deep neural networks have failed, because due to their mode of operation, they take into account only a certain subset of essential features that they are not able to properly associate together, leading to misclassification. In addition, deep networks are insensitive to small changes, which in the case of an ECG signal are often of fundamental importance, indicating, e.g., symptoms of a heart attack or a specific type of arrhythmia. Another critical problem of deep neural networks is low energy efficiency - both learning and inference require equipment with enormous computing power, which in practice is often not available, thus limiting the possibilities of implementing these solutions in everyday clinical and academic practice. This problem particularly concerns the analysis of medical images, which due to the multidimensionality of data (from 1 to 3 dimensions of the geometric space, from 1 to 4 dimensions of the color space and time) require access to very efficient equipment to be able to analyze such data using the most effective learning methods deep.

The purpose of this research team's research is to build a new adaptive model of heart activity based on neural associative-cognitive models that can eliminate these inconveniences and develop a representation of ECG signals in an unprecedented manner taking into account the frequency and number of changes over time, using frequent events and rare at the same time. In addition, thanks to effective representation of knowledge in the associative-cognitive model, it will be possible to associate information obtained thanks to the analysis of other modalities using machine learning techniques, mainly cardiac imaging, i.e., echocardiography, computed tomography, X-ray, magnetic resonance imaging or coronary angiography. The research will also create new neural and associative data structures, algorithms, and computational methods that search for associatively aggregated frequent patterns, representing them in an associative model of knowledge and classifying cardiovascular diseases.

In addition, it is planned to use many existing, leading solutions based mainly on deep learning models (advanced models based on convolutional and recursive neural networks) that will support the associative-cognitive model in specific well-defined tasks, as well as serve as a reference point in the conducted tests.

During the research, models of associative knowledge representation for biomedical signals and images will be developed, developing the team's scientific workshop and enabling applications for external grants for research and implementation in medical equipment or the form of computer applications.

Research conducted as part of this research group falls under the priority research areas of artificial intelligence, knowledge engineering, cognitive science, biocybernetics, and biomedical engineering (POB 5) and includes the development of these methods to obtain automatic classification and support of doctors, and then use in telemedicine (POB 6), saving health and the lives of many sick people affected by heart disease.

A specialized computer equipped with modern GPGPU units, certified medical monitors that meet the requirements of the TK and MR diagnostic station (compliance with the DICOM standard), a modern ECG apparatus allowing to record signals in high quality and digital form and modern single- and multi-channel ECG holsters will be used in the research to perform calculations, embed created algorithms, test computational models, collect data, and evaluate possible applications in telemedicine.