During the pandemic of novel coronavirus infection (COVID-19), computed tomography (CT) showed its effectiveness in diagnosis of coronavirus infection. However, ionizing radiation during CT studies causes concern for patients who require dynamic observation, as well as for examination of children and young people. For this retrospective study, we included 15 suspected for COVID-19 patients who were hospitalized in April 2020, Russia. There were 4 adults with positive polymerase chain reaction (PCR) test for COVID-19. All patients underwent magnetic resonance imaging (MRI) examinations using MR-LUND PROTOCOL: Single-shot Fast Spin Echo (SSFSE), LAVA 3D and IDEAL 3D, Echo-planar imaging (EPI) diffusion-weighted imaging (DWI) and Fast Spin Echo (FSE) T2 weighted imaging (T2WI). On T2WI changes were identified in 9 (60,0%) patients, on DWI – in 5 (33,3%) patients. In 5 (33,3%) patients lesions of the parenchyma were visualized on T2WI and DWI simultaneously. At the same time, 4 (26.7%) patients had changes in lung tissue only on T2WI. (P(McNemar) = 0,125; OR = 0,00 (95%); kappa = 0,500). In those patients who had CT scan, the changes were comparable to MRI. The results showed that in case of CT is not available, it is advisable to conduct a chest MRI for patients with suspected or confirmed COVID-19. Considering that T2WI is a fluid-sensitive sequence, if imaging for the lung infiltration is required, we can recommend the abbreviated MRI protocol consisting of T2 and T1 WI. These data may be applicable for interpreting other studies, such as thoracic spine MRI, detecting signs of viral pneumonia of asymptomatic patients. MRI can detect features of viral pneumonia.

The article clarifies at what stages of the life cycle of artificial intelligence systems (AIS) ethical issues arise and tells about global and domestic trends in this area. The international and national experience concerning ethical issues of the AIS use in healthcare is described. The international and national strategies for the development of AI in healthcare are analyzed and their main differences from each other are identified. Special attention is paid to the national strategy for developing AI in domestic healthcare. The main conclusions are summarized, and the importance of a strong successful healthcare system based on artificial intelligence that contributes to building trust and compliance with ethical standards is emphasized.

Purpose to develop a procedure for registering changes, notifying users about changes made, unifying software as a medical device based on artificial intelligence technologies (SaMD-AI) changes, as well as requirements for testing and inspectionsquality control before and after making changes. Methods The main types of changes, divided into two groups-major and minor. Major changes imply a subsequent change of a SaMD-AI version to improve efficiency and safety, to change the functionality, and to ensure the processing of new data types. Minor changes imply those that SaMD-AI developers can make due to errors in the program code. Three types of SaMD-AI testings are proposed to use: functional testing, calibration testing or control, and technical testing. ResultsThe presented approaches for validation SaMD-AI changes were introduced. The unified requirements for the request for changes and forms of their submission made this procedure understandable for SaMD-AI developers, and also adjusted the workload for the Experiment experts who checked all the changes made to SaMD-AI. Conclusion This article discusses the need to control changes in the module of SaMD-AI, as innovative products influencing medical decision making. It justifies the need to control a module operation of SaMD-AI after making changes. To streamline and optimize the necessary and sufficient control procedures, a systematization of possible changes in SaMD-AI and testing methods was carried out. KeywordsArtificial intelligence • Medical software based on artificial intelligence technologies • Software as a medical device • Modifications • Changes • Validation B Ekaterina Akhmad

Background The paper covers modern approaches to the evaluation of neoplastic processes with diffusion-weighted imaging (DWI) and proposes a physical model for monitoring the primary quantitative parameters of DWI and quality assurance. Models of hindered and restricted diffusion are studied. Material and method To simulate hindered diffusion, we used aqueous solutions of polyvinylpyrrolidone with concentrations of 0 to 70%. We created siloxane-based water-in-oil emulsions that simulate restricted diffusion in the intracellular space. To obtain a high signal on DWI in the broadest range of b values, we used silicon oil with high T2: cyclomethicone and caprylyl methicone. For quantitative assessment of our phantom, we performed DWI on 1.5T magnetic resonance scanner with various fat suppression techniques. We assessed water-in-oil emulsion as an extracorporeal source signal by simultaneously scanning a patient in whole-body DWI sequence. Results We developed phantom with control substances for apparent diffusion coefficient (ADC) measurements ranging from normal tissue to benign and malignant lesions: from 2.29 to 0.28 mm2/s. The ADC values of polymer solutions are well relevant to the mono-exponential equation with the mean relative difference of 0.91%. Conclusion The phantom can be used to assess the accuracy of the ADC measurements, as well as the effectiveness of fat suppression. The control substances (emulsions) can be used as a body marker for quality assurance in whole-body DWI with a wide range of b values.

During the pandemic of COVID-19, computed tomography showed its effectiveness in diagnosis of coronavirus infection. However, ionizing radiation during CT studies causes concern for patients who require dynamic observation, as well as for examination of children and young people. For this retrospective study, we included 15 suspected for COVID-19 patients who were hospitalized in April 2020, Russia. There were 4 adults with positive polymerase chain reaction (PCR) test for COVID-19. All patients underwent MRI examinations using MR-LUND PROTOCOL: Single-shot Fast Spin Echo (SSFSE), LAVA 3D and IDEAL 3D, EPI diffusion-weighted imaging (DWI) and Fast Spin Echo (FSE) T2WI without using respiratory or any other trigger. 3 patients also had CT scan performed. In 5 (33,3%) patients, detected lesioins were visualized on T2WI and DWI simultaneously. At the same time, 4 (26.7%) patients revealed lung tissue changes only on T2WI (P(McNemar)= 0,125, OR= 0,00 (95%), kappa=0,500). Pulmonary changes on MRI were also analyzed depending on their localization. In those patients who had CT scan, the changes were comparable to MRI. MRI of the lungs can detect features of viral pneumonia and assess severity of lung damage. The method can be used to diagnose COVID-19. These data may be applicable for interpreting other studies, such as thoracic spine MRI, detecting signs of viral pneumonia of asymptomatic patients. The current study was limited by a small sample size and absence of chest CT scans of all patients.