Two- Versus 8-Zone Lung Ultrasound in Heart Failure: Analysis of a Large Data Set Using a Deep Learning Algorithm

Abstract

Objective

Scanning protocols for lung ultrasound often include 8 or more lung zones, which may limit real-world clinical use. We sought to compare a 2-zone, anterior-superior thoracic ultrasound protocol for B-line artifact detection with an 8-zone approach in patients with known or suspected heart failure using a deep learning (DL) algorithm.

Methods

Adult patients with suspected heart failure and B-lines on initial lung ultrasound were enrolled in a prospective observational study. Subjects received daily ultrasounds with a hand-held ultrasound system using an 8-zone protocol (right and left anterior/lateral and superior/inferior). A previously published deep learning algorithm that rates severity of B-lines on a 0–4 scale was adapted for use on hand-held ultrasound full video loops. Average severities for 8 and 2 zones were calculated utilizing DL ratings. Bland–Altman plot analyses were used to assess agreement and identify bias between 2- and 8-zone scores for both primary (all patients, 5728 videos, 205 subjects) and subgroup (confirmed diagnosis of heart failure or pulmonary edema, 4464 videos, 147 subjects) analyses.

Results

Bland–Altman plot analyses revealed excellent agreement for both primary and subgroup analyses. The absolute difference on the 4-point scale between 8- and 2-zone average scores was not significant for the primary dataset (0.03; 95% CI −0.01 to 0.07) or the subgroup (0.01; 95% CI −0.04 to 0.06).

Conclusion

Utilization of a 2-zone, anterior-superior thoracic ultrasound protocol provided similar severity information to an 8-zone approach for a dataset of subjects with known or suspected heart failure.