Artificial Intelligence-based Point-of-care Lung Ultrasound for Screening COVID-19 Pneumoniae: Comparison with CT Scans

Background: Although lung ultrasound has been reported to be a portable, cost-effective, and accurate method to detect pneumonia, it has not been widely used because of the difficulty in its interpretation. Here, we aimed to investigate the effectiveness of a novel artificial intelligence-based automated pneumonia detection method using point-of-care lung ultrasound (AI-POCUS) for the coronavirus disease 2019 (COVID-19). Methods: : We enrolled consecutive patients admitted with COVID-19 who underwent computed tomography (CT) in August and September 2021. A 12-zone AI-POCUS was performed by a novice observer using a pocket-size device within 24 h of the CT scan. Fifteen control subjects were also scanned. Additionally, the accuracy of the simplified 8-zone scan excluding the dorsal chest, was assessed. More than three B-lines detected in one lung zone were considered zone-level positive, and the presence of positive AI-POCUS in any lung zone was considered patient-level positive. Results: : A total of 577 lung zones from 56 subjects (59.4 ± 14.8 years, 23% female) were evaluated using AI-POCUS. The mean number of days from disease onset was 9, and 14% of patients were under mechanical ventilation. The patient-level accuracy of 12-zone AI-POCUS for detecting CT-validated pneumonia was 94.5%, sensitivity was 92.3%, and specificity was 100%. When simplified with 8-zone scan, the accuracy, sensitivity, and sensitivity were 83.9%, 77.5%, and 100%, respectively. The zone-level accuracy, sensitivity, and specificity of AI-POCUS were 65.3 %, 37.2%, and 97.8 %, respectively. Interpretation: AI-POCUS using the novel pocket-size ultrasound system showed excellent agreement with CT-validated COVID-19 pneumonia, even when used by a novice observer.