Artificial intelligence-based pulmonary embolism classification: Development and validation using real-world data.

This paper presents an artificial intelligence-based classification model for the detection of pulmonary embolism in computed tomography angiography. The proposed model, developed from public data and validated on a large dataset from a tertiary hospital, uses a two-dimensional approach that integrates temporal series to classify each slice of the examination and make predictions at both slice and examination levels. The training process consists of two stages: first using a convolutional neural network InceptionResNet V2 and then a recurrent neural network long short-term memory model.

Quality and safety innovations in the Radiology Department during the COVID-19 pandemic: a Latin American experience.

Radiology departments were forced to make significant changes in their routine during the coronavirus disease 2019 pandemic, to prevent further transmission of the coronavirus and optimize medical care as well. In this article, we describe our Radiology Department's policies in a private hospital for coronavirus disease 2019 preparedness focusing on quality and safety for the patient submitted to imaging tests, the healthcare team involved in the exams, the requesting physician, and for other patients and hospital environment.