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
- heart failure
- machine learning
- magnetic resonance
- hypertrophic cardiomyopathy
- artificial intelligence
- convolutional neural network
- computed tomography
- acute myocardial infarction
- aortic stenosis
- pulmonary hypertension
- mitral valve
- left atrial
- cardiac resynchronization therapy
- magnetic resonance imaging
- atrial fibrillation
- diffusion weighted imaging
- percutaneous coronary intervention
- acute coronary syndrome
- neural network