Deep-learning model accurately classifies multi-label lung ultrasound findings, enhancing diagnostic accuracy and inter-reader agreement.

ABSTRACT: Despite the increasing use of lung ultrasound (LUS) in the evaluation of respiratory disease, operators' competence constrains its effectiveness. We developed a deep-learning (DL) model for multi-label classification using LUS and validated its performance and efficacy on inter-reader variability. We retrospectively collected LUS and labeled as normal, B-line, consolidation, and effusion from patients undergoing thoracentesis at a tertiary institution between January 2018 and January 2022. The development and internal testing involved 7580 images from January 2018 and December 2020, and the model's performance was validated on a temporally separated test set (n = 985 images collected after January 2021) and two external test sets (n = 319 and 54 images). Two radiologists interpreted LUS wi

SUBMITTER: Hong D 

PROVIDER: S-EPMC11437088 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature