Artificial intelligence-based detection of aortic stenosis from chest radiographs.
Daiju UedaAkira YamamotoShoichi EharaShinichi IwataKoji AboShannon L WalstonToshimasa MatsumotoAkitoshi ShimazakiMinoru YoshiyamaYukio MikiPublished in: European heart journal. Digital health (2021)
We created artificial intelligence (AI) models using deep learning to identify aortic stenosis (AS) from chest radiographs. Three AI models were developed and evaluated with 10 433 retrospectively collected radiographs and labelled from echocardiography reports. The ensemble AI model could detect AS in a test dataset with an area under the receiver operating characteristic curve of 0.83 (95% confidence interval 0.78-0.88). Since chest radiography is a cost-effective and widely available imaging test, our model can provide an additive resource for the detection of AS.
Keyphrases
- artificial intelligence
- aortic stenosis
- deep learning
- transcatheter aortic valve replacement
- left ventricular
- ejection fraction
- aortic valve replacement
- transcatheter aortic valve implantation
- aortic valve
- machine learning
- big data
- convolutional neural network
- coronary artery disease
- label free
- loop mediated isothermal amplification
- real time pcr
- high resolution
- pulmonary hypertension
- magnetic resonance imaging
- mass spectrometry
- adverse drug
- image quality
- quantum dots
- sensitive detection