Orit Shimon

TL;DR: An introduction to deep learning technology is provided and the stages that are entailed in the design process of deep learning radiology research are presented and the results of a survey of the application of convolutional neural networks to radiologic imaging are detailed.

TL;DR: In this article, a systematic review of current literature applying deep learning for the diagnosis of pulmonary embolism on CTPA is performed. But, these are only preliminary retrospective works, indicating the need for future research to determine the clinical impact of automated PE detection on patient care.

TL;DR: For a general use case, word embedding using a large cohort of non-English head CT reports and ATN improves NLP performance, and for a more specific task, BOW and deep learning showed similar results.

TL;DR: In this paper, the authors systematically examined the application of DL in non-invasive liver fibrosis imaging, and found that DL has the potential to enhance noninvasive fibrosis assessment.

TL;DR: There was no statistically significant association between early combination therapy and 30-day mortality in this retrospective cohort of nonpregnant adult patients with invasive listeriosis and a randomized controlled trial may be necessary to assess optimal treatment.