Developing an Echocardiography-Based, Automatic Deep Learning Framework for the Differentiation of Increased Left Ventricular Wall Thickness Etiologies.
James LiChieh-Ju ChaoJiwoong Jason JeongJuan Maria FarinaAmith R SeriTimothy BarryHana NewmanMegan CampanyMerna AbdouMichael O'SheaSean D SmithBishoy AbrahamSeyedeh Maryam HosseiniYuxiang WangSteven LesterSaid AlsidawiSusan WilanskyEric SteidleyJulie RosenthalChadi AyoubChristopher P AppletonWin-Kuang ShenMartha GroganGarvan C KaneJae K OhBhavik N PatelReza ArsanjaniImon BanerjeePublished in: Journal of imaging (2023)
The echo-based InceptionResnetV2 fusion model can accurately classify the main etiologies of increased LV wall thickness and can facilitate the process of diagnosis and workup.
Keyphrases
- deep learning
- left ventricular
- optical coherence tomography
- machine learning
- heart failure
- magnetic resonance
- artificial intelligence
- hypertrophic cardiomyopathy
- convolutional neural network
- cardiac resynchronization therapy
- acute myocardial infarction
- pulmonary hypertension
- aortic stenosis
- mitral valve
- magnetic resonance imaging
- diffusion weighted imaging
- contrast enhanced
- diffusion weighted
- neural network
- ejection fraction