Performance of a point-of-care ultrasound platform for artificial intelligence-enabled assessment of pulmonary B-lines.

ABSTRACT:

Background

The incorporation of artificial intelligence (AI) into point-of-care ultrasound (POCUS) platforms has rapidly increased. The number of B-lines present on lung ultrasound (LUS) serve as a useful tool for the assessment of pulmonary congestion. Interpretation, however, requires experience and therefore AI automation has been pursued. This study aimed to test the agreement between the AI software embedded in a major vendor POCUS system and visual expert assessment.

Methods

This single-center prospective study included 55 patients hospitalized for various respiratory symptoms, predominantly acutely decompensated heart failure. A 12-zone protocol was used. Two experts in LUS independently categorized B-lines into 0, 1-2, 3-4, and ≥ 5. The intraclass correlation coeffic

SUBMITTER: Labaf A 

PROVIDER: S-EPMC11874383 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature