Artificial Intelligence and Democratization of the Use of Lung Ultrasound in COVID-19: On the Feasibility of Automatic Calculation of Lung Ultrasound Score

9 páginas, 6 figuras, 3 tablasDuring 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.This research was partially funded by CDTI (Spanish acronym: Centre for Industrial Technological Development), funding number COI-20201153. Partially supported by the Google Cloud Research Credits program with the funding number GCP19980904, by the project RTI2018-099118-A-I00 founded by MCIU/AEI/FEDER UE and by the European Commission–NextGenerationEU, through CSIC’s Global Health Platform (PTI Salud Global).Peer reviewe

Inter-Rater Variability in the Evaluation of Lung Ultrasound in Videos Acquired from COVID-19 Patients

12 páginas, 7 figuras, 1 tablaLung 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 = 0.50). 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 research was partially funded by CDTI (Spanish acronym: Centre for Industrial Tech- nological Development), funding number COI-20201153. Partially supported by the Google Cloud Research Credits program with the funding number GCP19980904, by the project RTI2018-099118- A-I00 founded by MCIU/AEI/FEDER UE and by the European Commission–NextGenerationEU, through CSIC’s Global Health Platform (PTI Salud Global)