Fully Automated Echocardiogram Interpretation in Clinical Practice

TL;DR: This work proposes a machine learning approach for estimating the LVEF from short echocardiogram videos, based on gradient-boosted trees, which is significantly simpler than the state of the art, but is competitive in terms of accuracy and has a higher degree of explainability.

TL;DR: Real-time guidance by deep learning improved the standardization of echocardiographic acquisitions by experienced sonographers.

TL;DR: An overview of evolving applications of artificial intelligence and machine learning in tetralogy of Fallot care is provided and the challenges, limitations, and future potential for integrating them into clinical care are emphasized.

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.