Assisted diagnosis algorithm for lung ultrasound in COVID-19 patients

Lung ultrasound has become one of the most promising medical techniques for the diagnosis and monitoring of pneumonia, which is one of the main complication of SARS-CoV-2 infection. Despite this, the lack of trained personnel in lung echography has restricted its use worldwide. Computer aided diagnosis could help reducing the learning curve for less experienced technicians and, therefore, extending the use of lung ultrasound more quickly, while reducing the exam duration. This work explores the feasibility of real-time image processing algorithms for automatic calculation of the lung score. A clinical trial with 30 patients was completed following the same protocol of acquiring saving 3 seconds videos of different thorax zones. Those videos were evaluated by an experienced physician and by a custom developed algorithm for detecting A-lines, B-lines, and consolidations. The concordance between both findings were 88% for B-lines, 93.4% for consolidations and 70.2% for A-lines, reducing the acquisition time using the ULTRACOV prototype [1] by more than half compared to a conventional scanner. The good agreement of the results proves the feasibility of implementing real-time algorithms for aided diagnosis in lung ultrasound equipment.