Use of Audiovisual Strategies as a Complementary Resource for Practical Courses in Biomedical Engineering

In recent years, the education has been influenced by the implementation of new learning strategies due to the confinement caused by the spread of COVID-19. Teachers adopted audiovisual resources that allow them to teach their classes remotely. However, the transition to this modality has been sudden, forced, and evolving “on the fly" in response to the pedagogical adaptations. A structured implementation of audiovisual media in education is required, since it represents an important part of access to information in our times. For this goal, we propose strategies for the development of complementary offline and online audiovisual content to help teach practical courses in a Biomedical Engineering bachelor program. In particular, we present content created for a Medical Imaging Systems course. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Characterization of COVID-19 Diseased Lung Tissue Based on Texture Features

Although real time polymerase chain reaction test (RT-PCR) is the gold standard method for the diagnosis of COVID-19 patients, the use of Computed Tomography (CT) images for diagnosis, assessment of the severity of this disease and its evolution is widely accepted due to the possibility to observe the lungs damage. This evaluation is mainly made qualitatively, therefore, techniques have been proposed to obtain relevant additional clinical information, such as texture features. In this work, CT scans from 46 patients with COVID-19 were used to characterize the lungs by means of textural features. In the proposed approach, pulmonary parenchyma was delimited using a U-NET previously trained with images from different pulmonary diseases. Texture metrics were calculated using co-occurrence and run-length matrices considering both lungs, right and left lung, as well as apex, middle zone and base lung regions. A boxplot descriptive analysis was performed looking for significant differences between regions of each estimated texture metric. Results show that Gray Level Non-Uniformity (GLNU) and Run-Length Non-Uniformity (RLNU) features have more significant differences between regions, suggesting that these metrics may provide a proper characterization of the pulmonary damage caused by COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.