B-line quantification: comparing learners novice to lung ultrasound assisted by machine artificial intelligence technology to expert review
dc.contributor.author | Russell, Frances M. | |
dc.contributor.author | Ehrman, Robert R. | |
dc.contributor.author | Barton, Allen | |
dc.contributor.author | Sarmiento, Elisa | |
dc.contributor.author | Ottenhoff, Jakob E. | |
dc.contributor.author | Nti, Benjamin K. | |
dc.contributor.department | Emergency Medicine, School of Medicine | en_US |
dc.date.accessioned | 2023-01-26T13:57:06Z | |
dc.date.available | 2023-01-26T13:57:06Z | |
dc.date.issued | 2021-06-30 | |
dc.description.abstract | Background: The 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. Methods: This 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. Results: Fifty-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). Conclusion: After 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. | en_US |
dc.eprint.version | Final published version | en_US |
dc.identifier.citation | Russell FM, Ehrman RR, Barton A, Sarmiento E, Ottenhoff JE, Nti BK. B-line quantification: comparing learners novice to lung ultrasound assisted by machine artificial intelligence technology to expert review. Ultrasound J. 2021;13(1):33. Published 2021 Jun 30. doi:10.1186/s13089-021-00234-6 | en_US |
dc.identifier.uri | https://hdl.handle.net/1805/31020 | |
dc.language.iso | en_US | en_US |
dc.publisher | Springer | en_US |
dc.relation.isversionof | 10.1186/s13089-021-00234-6 | en_US |
dc.relation.journal | The Ultrasound Journal | en_US |
dc.rights | Attribution 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.source | PMC | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Point-of-care ultrasound | en_US |
dc.subject | Lung ultrasound | en_US |
dc.subject | Acute heart failure | en_US |
dc.subject | Novice learner | en_US |
dc.title | B-line quantification: comparing learners novice to lung ultrasound assisted by machine artificial intelligence technology to expert review | en_US |
dc.type | Article | en_US |