This paper presents an energy optimization approach to applying electrical impedance tomography (EIT) for medical diagnostics, particularly in detecting lung diseases. The designed Lung Electrical Tomography System (LETS) incorporates 102 electrodes and advanced image reconstruction algorithms. Energy efficiency is achieved through the use of modern electronic components and high-efficiency DC/DC converters that reduce the size and weight of the device without the need for additional cooling. Special attention is given to minimizing energy consumption during electromagnetic measurements and data processing, significantly improving the system’s overall performance. Research studies confirm the device’s high energy efficiency while maintaining the accuracy of the classification of lung disease using the LightGBM algorithm. This solution enables long-term patient monitoring and precise diagnosis with reduced energy consumption, marking a key step towards sustainable medical diagnostics based on EIT technology.

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informujące swoich czytelników o najnowszych osiągnięciach naukowych i nowoczesnych rozwiązaniach technicznych w Polsce i na świecie, popularyzuje problemy techniczne oraz poszerza wiedzę i kulturę techniczną.

LETS (Lung Electrical Tomography System) is an innovative medical system enabling comprehensive heart and lung function monitoring. It utilizes advanced techniques such as electrical impedance tomography (EIT), body surface potential mapping (BSPM), and electrical capacitance tomography (ECT) to provide accurate information about physiological parameters. The LETS system allows one-time examinations and continuous monitoring 24 hours a day. Remotely measuring and monitoring parameters such as the electrical activity of the heart muscle, blood flow, blood pressure, pulse, and lung aeration enables rapid identification and intervention in case of abnormalities. The system also allows for the detection of cardiac arrhythmias and other heart disorders, as well as the assessment of lung respiratory capacity and optimization of ventilation strategies. However, it's important to note that the LETS system has some limitations. For example, it may not be suitable for patients with certain pacemakers or other implanted devices. Overall, LETS represents a breakthrough in medical imaging, opening up new possibilities in diagnosing and monitoring heart and lung diseases. Its integration may contribute to improving patient care and increasing the effectiveness of medical interventions.

This article presents the development and analysis of numerical models for a medical diagnostic system based on Electrical Impedance Tomography (EIT), aimed at supporting the diagnosis of key respiratory diseases. High-resolution CT scans from the LCSTS dataset were segmented to create detailed anatomical models of the thorax, including lungs, heart, bronchi, vessels, and surrounding tissues. Numerical simulations of EIT measurements were performed using the finite element method (FEM) in 3D, generating time-series data reflecting inhalation and exhalation patterns for healthy cases and various pathological states: COPD, ARDS, pneumothorax (PTX), pneumonia (PNA), bronchospasm, and pulmonary hypertension (PHTN), each at multiple progression stages. The analysis of simulated signals revealed characteristic impedance patterns for each disease, visualized as compressed time-series overlays and similarity heatmaps using cosine and Euclidean distances. The results demonstrate the potential of EIT to differentiate between diseases but also highlight the challenges due to subtle inter-class differences.

The article presents research findings on developing a medical diagnostic system based on electrical impedance tomography technology. One of the key components of this project is developing a method for reconstructing the structure of human lungs using this technology. The authors of the article compared the effectiveness of two different loss functions in training a neural network, which is tasked with accurately replicating the lung structure based on electrical impedance tomography data. The researchers analyzed various approaches to calculating loss functions, including cosine embedding loss and InfoNCE loss. They compared the results obtained using these two functions to identify which one performs better in lung structure reconstruction. The findings of these studies may have significant implications for the development of diagnostic systems based on electrical impedance tomography and for improving the effectiveness of lung disease diagnosis. Additionally, the authors discuss potential future directions for the project, including possible applications of the research findings in clinical practice. Future research efforts may focus on optimizing neural network parameters, exploring alternative loss functions, or utilizing advanced machine learning techniques for even more precise lung structure reconstruction. The pursuit of improving such diagnostic systems could lead to significant advancements in the field of medicine, particularly in diagnosing and treating respiratory diseases.