Assessment of left ventricular wall thickness and dimension: accuracy of a deep learning model with prediction uncertainty.

Left ventricular (LV) geometric patterns aid clinicians in the diagnosis and prognostication of various cardiomyopathies. The aim of this study is to assess the accuracy and reproducibility of LV dimensions and wall thickness using deep learning (DL) models. A total of 30,080 unique studies were included; 24,013 studies were used to train a convolutional neural network model to automatically assess, at end-diastole, LV internal diameter (LVID), interventricular septal wall thickness (IVS), posterior wall thickness (PWT), and LV mass.

Automated Atrial Fibrillation Diagnosis by Echocardiography without ECG: Accuracy and Applications of a New Deep Learning Approach.

Background: Automated rhythm detection on echocardiography through artificial intelligence (AI) has yet to be fully realized. We propose an AI model trained to identify atrial fibrillation (AF) using apical 4-chamber (AP4) cines without requiring electrocardiogram (ECG) data.

Methods: Transthoracic echocardiography studies of consecutive patients ≥ 18 years old at our tertiary care centre were retrospectively reviewed for AF and sinus rhythm.