Artificial intelligence-based detection of aortic stenosis from chest radiographs.

ABSTRACT:

Aims

We aimed to develop models to detect aortic stenosis (AS) from chest radiographs-one of the most basic imaging tests-with artificial intelligence.

Methods and results

We used 10 433 retrospectively collected digital chest radiographs from 5638 patients to train, validate, and test three deep learning models. Chest radiographs were collected from patients who had also undergone echocardiography at a single institution between July 2016 and May 2019. These were labelled from the corresponding echocardiography assessments as AS-positive or AS-negative. The radiographs were separated on a patient basis into training [8327 images from 4512 patients, mean age 65 ±  (standard deviation) 15 years], validation (1041 images from 563 patients, mean age 65 ± 14 years), and test (10

SUBMITTER: Ueda D 

PROVIDER: S-EPMC9707887 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature