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. This approach achieved an accuracy of 93% at the slice level and 77% at the examination level. External validation using a hospital dataset resulted in a precision of 86% for positive pulmonary embolism cases and 69% for negative pulmonary embolism cases. Notably, the model excels in excluding pulmonary embolism, achieving a precision of 73% and a recall of 82%, emphasizing its clinical value in reducing unnecessary interventions. In addition, the diverse demographic distribution in the validation dataset strengthens the model's generalizability. Overall, this model offers promising potential for accurate detection and exclusion of pulmonary embolism, potentially streamlining diagnosis and improving patient outcomes.

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.

Quality and safety innovations in the Radiology Department during the COVID-19 pandemic: a Latin American experience / Inovações de qualidade e segurança no Departamento de Radiologia durante a pandemia pela COVID-19: uma experiência Latino-Americano

ABSTRACT 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.

RESUMO Os departamentos de radiologia precisaram adotar mudanças significativas em sua rotina durante a pandemia da doença causada pelo novo coronavírus, a fim de reduzir sua transmissibilidade e otimizar os cuidados médicos. Neste artigo, descrevemos as políticas adotadas pelo Departamento de Radiologia de um hospital privado durante a pandemia, com foco em qualidade e segurança de paciente submetido a exames de imagem, equipe de assistência do departamento de imagem, médico solicitante, demais pacientes e ambiente hospitalar.