Improvement of artificial intelligence-based computed tomography pulmonary angiography in identifying acute pulmonary embolism.

ABSTRACT:

Background

Acute pulmonary embolism (APE) is a potentially fatal condition. Although artificial intelligence (AI) algorithms show promise in diagnosis, overreliance on them may lead to critical errors. This study aimed to improve diagnostic accuracy by integrating AI algorithms with clinical indicators.

Methods

A retrospective analysis was conducted on 329 patients with suspected APE who underwent computed tomography pulmonary angiography (CTPA), lower-extremity venous ultrasound, and laboratory tests. Independent risk factors were identified through logistic regression. The diagnostic performance of individual and combined predictors was assessed via receiver operating characteristic (ROC) curves. Interrater agreement between the AI algorithms and radiologists was evaluated

SUBMITTER: Song J 

PROVIDER: S-EPMC12514735 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature