Artificial intelligence-based point-of-care lung ultrasound for screening COVID-19 pneumoniae: Comparison with CT scans

• Published in: PloS one Vol. 18; no. 3; p. e0281127
• Main Authors: Kuroda, Yumi, Kaneko, Tomohiro, Yoshikawa, Hitomi, Uchiyama, Saori, Nagata, Yuichi, Matsushita, Yasushi, Hiki, Makoto, Minamino, Tohru, Takahashi, Kazuhisa, Daida, Hiroyuki, Kagiyama, Nobuyuki
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16-03-2023; Public Library of Science (PLoS)
• Subjects: Accuracy; Analysis; Artificial Intelligence; Automation; Bacterial pneumonia; Biology and Life Sciences; Causes of; Comparative analysis; Computed tomography; Computer and Information Sciences; Coronaviruses; COVID-19; COVID-19 - diagnostic imaging; CT imaging; Deep learning; Diagnosis; Diagnosis, Ultrasonic; Diagnostic imaging; Female; Health aspects; Hospitals; Humans; Lung - diagnostic imaging; Lung diseases; Lungs; Machine learning; Male; Mechanical ventilation; Medical imaging; Medicine and Health Sciences; Patients; Pneumonia; Point-of-Care Systems; Research and Analysis Methods; Retrospective Studies; Sensitivity; Tomography, X-Ray Computed - methods; Ultrasonic imaging; Ultrasonic testing; Ultrasound; Ventilation; Viral diseases; Japan; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0281127

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).
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. The sample size calculation was not performed given the retrospective all-comer nature of the study.
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 CT-validated pneumonia was seen in 71.4% of patients at total 577 lung zones (53.3%). The 12-zone AI-POCUS for detecting CT-validated pneumonia in the patient-level showed the accuracy of 94.5% (85.1%- 98.1%), sensitivity of 92.3% (79.7%- 97.3%), specificity of 100% (80.6%- 100%), positive predictive value of 95.0% (89.6% - 97.7%), and Kappa of 0.33 (0.27-0.40). When simplified with 8-zone scan, the accuracy, sensitivity, and sensitivity were 83.9% (72.2%- 91.3%), 77.5% (62.5%- 87.7%), and 100% (80.6%- 100%), respectively. The zone-level accuracy, sensitivity, and specificity of AI-POCUS were 65.3% (61.4%- 69.1%), 37.2% (32.0%- 42.7%), and 97.8% (95.2%- 99.0%), respectively.
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.