During the COVID-19 pandemic, lung ultrasound has been revealed as a powerful technique for diagnosis and follow-up of pneumonia, the principal complication of SARS-CoV-2 infection. Nevertheless, being a relatively new and unknown technique, the lack of trained personnel has limited its application worldwide. Computer-aided diagnosis could possibly help to reduce the learning curve for less experienced physicians, and to extend such a new technique such as lung ultrasound more quickly. This work presents the preliminary results of the ULTRACOV (Ultrasound in Coronavirus disease) study, aimed to explore the feasibility of a real-time image processing algorithm for automatic calculation of the lung ultrasound score (LUS). A total of 28 patients positive on COVID-19 were recruited and scanned in 12 thorax zones following the lung score protocol, saving a 3 s video at each probe position. Those videos were evaluated by an experienced physician and by a custom developed automated detection algorithm, looking for A-Lines, B-Lines, consolidations, and pleural effusions. The agreement between the findings of the expert and the algorithm was 88.0% for B-Lines, 93.4% for consolidations and 99.7% for pleural effusion detection, and 72.8% for the individual video score. The standard deviation of the patient lung score difference between the expert and the algorithm was ±2.2 points over 36. The exam average time with the ULTRACOV prototype was 5.3 min, while with a conventional scanner was 12.6 min. Conclusion: A good agreement between the algorithm output and an experienced physician was observed, which is a first step on the feasibility of developing a real-time aided-diagnosis lung ultrasound equipment. Additionally, the examination time was reduced to less than half with regard to a conventional ultrasound exam. Acquiring a complete lung ultrasound exam within a few minutes is possible using fairly simple ultrasound machines that are enhanced with artificial intelligence, such as the one we propose. This step is critical to democratize the use of lung ultrasound in these difficult times.

Lung ultrasound (LUS) allows for the detection of a series of manifestations of COVID-19, such as B-lines and consolidations. The objective of this work was to study the inter-rater reliability (IRR) when detecting signs associated with COVID-19 in the LUS, as well as the performance of the test in a longitudinal or transverse orientation. Thirty-three physicians with advanced experience in LUS independently evaluated ultrasound videos previously acquired using the ULTRACOV system on 20 patients with confirmed COVID-19. For each patient, 24 videos of 3 s were acquired (using 12 positions with the probe in longitudinal and transverse orientations). The physicians had no information about the patients or other previous evaluations. The score assigned to each acquisition followed the convention applied in previous studies. A substantial IRR was found in the cases of normal LUS (κ = 0.74), with only a fair IRR for the presence of individual B-lines (κ = 0.36) and for confluent B-lines occupying < 50% (κ = 0.26) and a moderate IRR in consolidations and B-lines > 50% (κ = 0.50). No statistically significant differences between the longitudinal and transverse scans were found. The IRR for LUS of COVID-19 patients may benefit from more standardized clinical protocols.

This paper describes the design and implementation of an ultrasonic non-contact air-coupled technique (UNCACT) using antisymmetric Lamb waves (ALW) for NDT assessments in novel composite sandwich plates of a car body shell. This technique is complemented with a C-Scan image implementation using guided waves. The finite element method (FEM) was developed using Comsol 6.1 for the interpretation of the several wave modes presented in the experiments, including the ALW mode. This FEM model is indispensable for the correct interpretation of the received signals and contributes to a better implementation of this technology. This is a novel contribution building upon previously reported work. Additionally, the phase velocity method (PVM) was applied for the verification of the ALW mode in the portion of the RF signal necessary for the C-Scan image.

Lung ultrasound (LUS) allows the detection of a series of manifestations of COVID-19 such as B lines and consolidations. The objective of this work was to study the inter-rater reliability (IRR) when detecting signs associated with COVID-19 in the LUS, as well as the impact of performing the test in the longitudinal or transverse orientation. 33 physicians with advanced experience in LUS, independently evaluated ultrasound videos previously acquired with the ULTRACOV system of 20 patients with confirmed COVID-19. In each patient, 24 videos of 3 seconds were acquired (using 12 positions with the probe in longitudinal and transverse orientations). Physicians had no information about the patients or other previous evaluations. The score assigned to each acquisition followed the convention applied in previous studies. A substantial IRR was found in the cases of normal LUS (κ = 0.74), only a fair IRR for the presence of individual B lines (κ = 0.36) and for confluent B lines occupying 50% (κ = 0.50). No statistically significant differences between the longitudinal and transverse scans were found. The IRR in LUS of COVID-19 patients may benefit from more standardization of the clinical protocols.

This paper describes the design and implementation of an ultrasonic non-contact air-coupled technique (UNCACT) using antisymmetric Lamb waves (ALW) for NDT assessment in novel composite sandwich plates of the car body shell. This technique is complemented with a C-Scan image implementation using this kind of guided waves. The finite element method using Comsol 6.1 is developed for the interpretation of the several wave modes presented in the experiments, including the ALW mode. The phase velocity method (PVM) is applied for the verification of the ALW mode in the portion of the RF signal necessary in the C-Scan image.