Automated B-lines quantification with lung ultrasound in mechanically ventilated patients.

Authors

Arianna Ciabattoni, Dejan Radovanovic, Tommaso Pozzi, Martina Gurgitano, Gianpaolo Carrafiello, Alessandro Monte, Silvia Coppola, Davide A Chiumello

Published in

Multidisciplinary respiratory medicine. Volume 21. Pages 1069. Sep 10, 2026. Epub Sep 10, 2026.

Abstract

If ultrasound dedicated software for the identification and quantification of interstitial syndrome is equal or superior to experts observation is still being explored. The primary objective of this study was to compare the performance of an automatic method based on a dedicated software algorithm with that of expert clinicians in terms of B-lines quantification.
Adult patients with acute respiratory failure who were deeply sedated, paralysed and mechanically ventilated were prospectively enrolled within 48 hours from ICU admission and underwent lung ultrasound by a trained physician. For each patient, 12 clips were stored, one for each thoracic region. The clips were then analyzed automatically using a built-in automatic recognition system and visually by two experts. A score of 0 was assigned if fewer than two B-lines were detected, a score of 1 if three or more B-lines were detected and a score of 2 if coalescent B-lines were detected.
A total of 40 patients were included in the study. Out of the 480 clips analysed, 54.2% had a lung score of 0, 6.8% had a lung score of 1, 8.8% had a lung score of 2 and 20.2% had a lung score of 3. The agreement between the experts in detecting the scores 0, 1, 2 was excellent (ICC = 0.82) in 383 (79.8%) clips. The agreement between the automatic system and the experts was poor (ICC = 0.33), with a significant difference (p < 0.001).
In mechanically ventilated patients with acute respiratory failure, an automatic B-lines quantification system did not present an acceptable accuracy compared to expert consensus for B-lines counting, probably due to the coexistence of consolidation and pleural effusion.

PMID:
42720572
Bibliographic data and abstract were imported from PubMed on 10 Sep 2026.

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