Federated Learning for Breast Density Classification: A Real-World Implementation

Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting. Seven clinical institutions from across the world joined this FL effort to train a model for breast density classification based on Breast Imaging, Reporting & Data System (BI-RADS). We show that despite substantial differences among the datasets from all sites (mammography system, class distribution, and data set size) and without centralizing data, we can successfully train AI models in federation. The results show that models trained using FL perform 6.3% on average better than their counterparts trained on an institute's local data alone. Furthermore, we show a 45.8% relative improvement in the models' generalizability when evaluated on the other participating sites' testing data.Comment: Accepted at the 1st MICCAI Workshop on "Distributed And Collaborative Learning"; add citation to Fig. 1 & 2 and update Fig.

Percepção de gestantes acerca das complicações da infecção por Zika Vírus: revisão de literatura

Objetivou-se, evidenciar à luz da literatura científica, a percepção de gestantes acerca das complicações da infecção por zika vírus. Trata-se de revisão narrativa da literatura, do tipo descritiva-exploratória. A busca de dados foi realizada no período de outubro a novembro de 2022, de forma pareada e independente por dois pesquisadores nas bases de dados indexadas a BVS. Foram aplicados os seguintes filtros; artigos completos, disponíveis para download e leitura na íntegra; publicados nos três idiomas (português, inglês e espanhol). Salienta-se que neste estudo não será delimitado o recorte temporal de publicação dos artigos identificados, visto que buscou-se elevar abrangência da busca. Foram incluídos os artigos que versassem acerca da temática em estudo, excluindo as duplicatas, os resumos e relatos de experiências. Assim, obtiveram-se dez artigos para compor amostra final. Mediante o processo analítico dos estudos, evidenciou-se que as gestantes reconhecem que a infecção pelo Zika vírus, é uma doença que poderá repercutir negativamente na saúde materna-fetal e no desenvolvimento da gestação, podendo propiciar graves complicações, principalmente para os fetos. Portanto, faz-se necessário na implementação de cuidados pré-natais integrais, holísticos e, sobretudo de qualidade que visem a prevenção e controle do zika vírus, em especial durante a gravidez

Possible high COVID-19 airborne infection risk in deep and poorly ventilated 2D street canyons

Despite the widespread assumption that outdoor environments provide sufficient ventilation and dilution capacity to mitigate the risk of COVID-19 infection, there is little understanding of airborne infection risk in outdoor urban areas with poor ventilation. To address this gap, we propose a modified Wells-Riley model based on the purging flow rate (QPFR), by using computational fluid dynamic (CFD) simulations. The model quantifies the outdoor risk in 2D street canyons with different approaching wind speeds, urban heating patterns and aspect ratios (building height to street width). We show that urban morphology plays a critical role in controlling airborne infectious disease transmission in outdoor environments, especially under calm winds; with deep street canyons (aspect ratio > 3) having a similar infection risk as typical indoor environments. While ground and leeward wall heating could reduce the risk, windward heating (e.g., windward wall ~10 K warmer than the ambient air) can increase the infection risk by up to 75%. Our research highlights the importance of considering outdoor infection risk and the critical role of urban morphology in mitigating airborne infection risk. By identifying and addressing these risks, we can inform measures that may enhance public health and safety, particularly in densely populated urban environments

Possible high COVID-19 airborne infection risk in deep and poorly ventilated 2D street canyons

Despite the widespread assumption that outdoor environments provide sufficient ventilation and dilution capacity to mitigate the risk of COVID-19 infection, there is little understanding of airborne infection risk in outdoor urban areas with poor ventilation. To address this gap, we propose a modified Wells-Riley model based on the purging flow rate (QPFR), by using computational fluid dynamic (CFD) simulations. The model quantifies the outdoor risk in 2D street canyons with different approaching wind speeds, urban heating patterns and aspect ratios (building height to street width). We show that urban morphology plays a critical role in controlling airborne infectious disease transmission in outdoor environments, especially under calm winds; with deep street canyons (aspect ratio > 3) having a similar infection risk as typical indoor environments. While ground and leeward wall heating could reduce the risk, windward heating (e.g., windward wall ~10 K warmer than the ambient air) can increase the infection risk by up to 75%. Our research highlights the importance of considering outdoor infection risk and the critical role of urban morphology in mitigating airborne infection risk. By identifying and addressing these risks, we can inform measures that may enhance public health and safety, particularly in densely populated urban environments

Federated learning for predicting clinical outcomes in patients with COVID-19

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare