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
- loop mediated isothermal amplification
- label free
- computed tomography
- heart failure
- high resolution
- real time pcr
- magnetic resonance imaging
- pulmonary hypertension
- emergency department
- photodynamic therapy
- magnetic resonance
- quantum dots
- fluorescence imaging
- contrast enhanced
- atrial fibrillation