B-line quantification: comparing learners novice to lung ultrasound assisted by machine artificial intelligence technology to expert review

The Ultrasound Journal

Allen Barton¹  Elisa Sarmiento²  Benjamin K. Nti²  Frances M. Russell²  Robert R. Ehrman³  Jakob E. Ottenhoff³ 

[1] Boone County Emergency Physicians, 46077, Zionsville, IN, USA;Department of Emergency Medicine, Indiana University School of Medicine, 720 Eskenazi Ave, FOB 3rd Floor, 46202, Indianapolis, IN, USA;Department of Emergency Medicine, Wayne State University School of Medicine, 4021 St Antoine Ave, Suite 6G, 48201, Detroit, MI, USA;

关键词: Artificial intelligence;    Point-of-care ultrasound;    Lung ultrasound;    Acute heart failure;    Novice learner;   

DOI  :  10.1186/s13089-021-00234-6

来源: Springer

【 摘 要 】

BackgroundThe goal of this study was to assess the ability of machine artificial intelligence (AI) to quantitatively assess lung ultrasound (LUS) B-line presence using images obtained by learners novice to LUS in patients with acute heart failure (AHF), compared to expert interpretation.MethodsThis was a prospective, multicenter observational study conducted at two urban academic institutions. Learners novice to LUS completed a 30-min training session on lung image acquisition which included lecture and hands-on patient scanning. Learners independently acquired images on patients with suspected AHF. Automatic B-line quantification was obtained offline after completion of the study. Machine AI counted the maximum number of B-lines visualized during a clip. The criterion standard for B-line counts was semi-quantitative analysis by a blinded point-of-care LUS expert reviewer. Image quality was blindly determined by an expert reviewer. A second expert reviewer blindly determined B-line counts and image quality. Intraclass correlation was used to determine agreement between machine AI and expert, and expert to expert.ResultsFifty-one novice learners completed 87 scans on 29 patients. We analyzed data from 611 lung zones. The overall intraclass correlation for agreement between novice learner images post-processed with AI technology and expert review was 0.56 (confidence interval [CI] 0.51–0.62), and 0.82 (CI 0.73–0.91) between experts. Median image quality was 4 (on a 5-point scale), and correlation between experts for quality assessment was 0.65 (CI 0.48–0.82).ConclusionAfter a short training session, novice learners were able to obtain high-quality images. When the AI deep learning algorithm was applied to those images, it quantified B-lines with moderate-to-fair correlation as compared to semi-quantitative analysis by expert review. This data shows promise, but further development is needed before widespread clinical use.

【 授权许可】

CC BY