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

PLoS One. 2023 Mar 16;18(3):e0281127. doi: 10.1371/journal.pone.0281127. eCollection 2023.

Abstract

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. The sample size calculation was not performed given the retrospective all-comer nature of the study.

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 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.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Artificial Intelligence
  • COVID-19* / diagnostic imaging
  • Female
  • Humans
  • Lung / diagnostic imaging
  • Male
  • Pneumonia*
  • Point-of-Care Systems
  • Retrospective Studies
  • Tomography, X-Ray Computed / methods

Grants and funding

Kagiyama and Daida are affiliated with a department funded by Philips Japan, Asahi KASEI Corporation, Inter Reha Co., Ltd, and Toho Holdings Co., Ltd., based on collaborative research agreements. Other authors have no conflict of interest to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials. This work was partially supported by the Japan Society for Promotion of Science KAKENHI (by the Japanese government), with a Grant Number 21K18086. There was no additional external funding received for this study.