Interobserver Agreement and Correlation of an Automated Algorithm for B‐Line Identification and Quantification With Expert Sonologist Review in a Handheld Ultrasound Device

• Published in: Journal of ultrasound in medicine Vol. 41; no. 10; pp. 2487 - 2495
• Main Authors: Moore, Christopher L., Wang, Jing, Battisti, Anthony J., Chen, Alvin, Fincke, Jonathan, Wang, Anita, Wagner, Michael, Raju, Balasundar, Baloescu, Cristiana
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.10.2022
• Subjects: artificial intelligence; b‐lines; handheld ultrasound; lung ultrasound; POCUS; point‐of‐care ultrasound; b-lines; point-of-care ultrasound; lung ultrasound; POCUS; artificial intelligence; handheld ultrasound
• ISSN: 0278-4297; 1550-9613
• DOI: 10.1002/jum.15935

Objectives B‐lines are ultrasound artifacts that can be used to detect a variety of pathologic lung conditions. Computer‐aided methods to detect and quantify B‐lines may standardize quantification and improve diagnosis by novice users. We sought to test the performance of an automated algorithm for the detection and quantification of B‐lines in a handheld ultrasound device (HHUD). Methods Ultrasound images were prospectively collected on adult emergency department patients with dyspnea. Images from the first 124 patients were used for algorithm development. Clips from 80 unique subjects for testing were randomly selected in a predefined proportion of B‐lines (0 B‐lines, 1–2 B‐lines, 3 or more B‐lines) and blindly reviewed by five experts using both a manual and reviewer‐adjusted process. Intraclass correlation coefficient (ICC) and weighted kappa were used to measure agreement, while an a priori threshold of an ICC (3,k) of 0.75 and precision of 0.3 were used to define adequate performance. Results ICC between the algorithm and manual count was 0.84 (95% confidence interval [CI] 0.75–0.90), with a precision of 0.15. ICC between the reviewer‐adjusted count and the algorithm count was 0.94 (95% CI 0.90–0.96), and the ICC between the manual and reviewer‐adjusted counts was 0.94 (95% CI 0.90–0.96). Weighted kappa was 0.72 (95% CI 0.49–0.95), 0.88 (95% CI 0.74–1), and 0.85 (95% CI 0.89–0.96), respectively. Conclusions This study demonstrates a high correlation between point‐of‐care ultrasound experts and an automated algorithm to identify and quantify B‐lines using an HHUD. Future research may incorporate this HHUD in clinical studies in multiple settings and users of varying experience levels.