Fully Automated Echocardiogram Interpretation in Clinical Practice

TL;DR: Zhang et al. as discussed by the authors used a deep learning model that has enjoyed spectacular success in addressing computer vision problems including image classification, face recognition, robot navigation, and driverless cars to name a few.

TL;DR: In this paper , a review examines the current state and application of artificial intelligence and machine learning in cardiovascular medicine and highlights the impact of these technologies on the clinical practice of medicine in other specialties.

TL;DR: In this paper, the authors describe the current state of use of handheld ultrasound devices in the emergency department (ED) including device overview, institutional concerns, unique areas of use, recent literature since their adoption into clinical EM, and their future potential.

TL;DR: In this article, an automated machine learning algorithm was used to quantitatively quantify the left ventricular ejection fraction (EF) from guidelines-recommended apical views, but the results showed that EF was not accurate.

TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.

TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.

TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.

TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.