Diffusion-weighted imaging with color-coded images: towards a reduction in reading time while keeping a similar accuracy

The aim of this study was to develop a diagnostic tool capable of providing diffusion and apparent diffusion coefficient (ADC) map information in a single color-coded image and to assess the performance of color-coded images compared with their corresponding diffusion and ADC map. The institutional review board approved this retrospective study, which sequentially enrolled 36 head MRI scans. Diffusion-weighted images (DWI) and ADC maps were compared to their corresponding color-coded images. Four raters had their interobserver agreement measured for both conventional (DWI) and color-coded images. Differences between conventional and color-coded images were also estimated for each of the 4 raters. Cohen's kappa and percent agreement were used. Also, paired-samples t-test was used to compare reading time for rater 1. Conventional and color-coded images had substantial or almost perfect agreement for all raters. Mean reading time of rater 1 was 47.4 seconds for DWI and 27.9 seconds for color-coded images (P = .00007). These findings are important because they support the role of color-coded images as being equivalent to that of the conventional DWI in terms of diagnostic capability. Reduction in reading time (which makes the reading easier) is also demonstrated for one rater in this study.Departamento de Diagnóstico por Imagem da Escola Paulista de Medicina da UNIFESP, Rua Napoleão de Barros, 800 Vila Clementino, 04024-002 São Paulo, SP, BrazilDepartamento de Diagnóstico por Imagem da Escola Paulista de Medicina da UNIFESP, Rua Napoleão de Barros, 800 Vila Clementino, 04024-002 São Paulo, SP, BrazilWeb of Scienc

Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts.

The Radiological Society of North of America (RSNA) and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society have led a series of joint panels and seminars focused on the present impact and future directions of artificial intelligence (AI) in radiology. These conversations have collected viewpoints from multidisciplinary experts in radiology, medical imaging, and machine learning on the current clinical penetration of AI technology in radiology and how it is impacted by trust, reproducibility, explainability, and accountability. The collective points-both practical and philosophical-define the cultural changes for radiologists and AI scientists working together and describe the challenges ahead for AI technologies to meet broad approval. This article presents the perspectives of experts from MICCAI and RSNA on the clinical, cultural, computational, and regulatory considerations-coupled with recommended reading materials-essential to adopt AI technology successfully in radiology and, more generally, in clinical practice. The report emphasizes the importance of collaboration to improve clinical deployment, highlights the need to integrate clinical and medical imaging data, and introduces strategies to ensure smooth and incentivized integration. Keywords: Adults and Pediatrics, Computer Applications-General (Informatics), Diagnosis, Prognosis © RSNA, 2024

Qualidade do Biogás a partir de resíduos industriais da avicultura

Práticas adequadas de manejo dos resíduos são essenciais para que a indústria avícola cresça e se desenvolva sob as condições de restrições legais atualmente existentes. As operações de produção de frangos e poedeiras, além de carne e ovos, geram anualmente um grande volume de resíduos na forma de esterco, efluentes, camas de aves e aves mortas. O presente estudo foi realizado na Unidade Industrial de Aves, localizado no município de Matelândia-Pr, objetivou-se monitorar parâmetros de qualidade sendo eles: pH, DBO, DQO, acidez volátil, alcalinidade, sólidos totais e sólidos voláteis. As coletas ocorreram num período de 6 meses, sendo recolhido material 1 vez por semana. A média dos valores de pH ficaram entre 6,21 afluente e 7,02 no efluente. O valor mínimo encontrado foi de 5,07 e o máximo 8,38, ambos os valores na entrada do biodigestor. A média dos valores de DBO encontrados ficaram entre 3.830 mg/L no afluente e 405,1 mg/L no efluente. O valor máximo encontrado foi de 15.890 mg/L no afluente e o valor mínimo encontrado foi de 84 mg/L na saída do sistema de biodigestão.

Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays

Artificial intelligence (AI)-generated clinical advice is becoming more prevalent in healthcare. However, the impact of AI-generated advice on physicians’ decision-making is underexplored. In this study, physicians received X-rays with correct diagnostic advice and were asked to make a diagnosis, rate the advice’s quality, and judge their own confidence. We manipulated whether the advice came with or without a visual annotation on the X-rays, and whether it was labeled as coming from an AI or a human radiologist. Overall, receiving annotated advice from an AI resulted in the highest diagnostic accuracy. Physicians rated the quality of AI advice higher than human advice. We did not find a strong effect of either manipulation on participants’ confidence. The magnitude of the effects varied between task experts and non-task experts, with the latter benefiting considerably from correct explainable AI advice. These findings raise important considerations for the deployment of diagnostic advice in healthcare

OPERAÇÃO DE GRUPO GERADOR A BIOGÁS NA GERAÇÃO DE ENERGIA ELÉTRICA

A biomassa residual proveniente da agroindústria pode ser tratada por biodigestores, que apresenta como produto energético, o biogás. Uma das formas de aproveitamento do biogás é na alimentação de motores a combustão interna, para a geração de energia elétrica. A proposição deste trabalho foi avaliar o desempenho de um grupo gerador a biogás, quanto a operação e geração de energia elétrica. Para a análise dos parâmetros elétricos e operacionais, foram utilizados dois analisadores de energia. O resultado obtido do balanço energético, no período de seis meses de análise, foi o excedente 72 MWh, que pelo contrato de compra e venda é comercializada com a concessionária de energia elétrica

Glândula mamária do mocó (Kerodon rupestris - Wied Neuwied, 1820): aspectos morfológicos

In attempt to collaborate with the rational zootecnic exploration of the Rock Cavy (Kerodon rupestris), the morphology of the mammary gland from these rodends was described, so that it can become a food source for northeastern population. Were used five animals yielded by the Center of Multiplication of Animals Savage (CEMAS), created by the Superior School of Agriculture of Mossoró (ESAM) registered in IBAMA as scientific breeding with number 12,492- 0004. The mammary glands were remove through dissecation and were photographed. Each animal presented two mammary glands in the base of the insertion of the pelvic members in the abdominal region. Histologicaly the mammary gland is composed of a great amount of lobes, replete of alveolar tubs glands with cubical simple epithelium. Its mammary papilla showed an amount of muscular staple fibres in circular way that assists it in the contraction for excrement of milk.Procurando colaborar com a exploração zootécnica racional dos mocós (Kerodon rupestris) foi descrita a morfologia da glândula mamária destes roedores, que por sua vez podem tornar-se uma fonte de alimento para população nordestina. Foram utilizados cinco animais cedidos pelo Centro de Multiplicação de Animais Silvestres (CEMAS) da pela Escola Superior de Agricultura de Mossoró (ESAM) registrado junto ao IBAMA como criadouro científico sob o número 12.492-0004. As glândulas mamárias foram retiradas através de dissecação e foram fotografadas. Cada animal apresentou duas glândulas mamárias localizadas na região inguinal. Histologicamente a glândula mamária era composta por uma grande quantidade de lóbulos, repletos de glândulas túbulos-alveolares com epitélio simples cúbico. Na papila mamária fibras musculares apresentaram-se dispostas de modo circular, o que possivelmente auxilia na contração para excreção do leite

Federated Learning on Heterogenous Data using Chest CT

Large data have accelerated advances in AI. While it is well known that population differences from genetics, sex, race, diet, and various environmental factors contribute significantly to disease, AI studies in medicine have largely focused on locoregional patient cohorts with less diverse data sources. Such limitation stems from barriers to large-scale data share in medicine and ethical concerns over data privacy. Federated learning (FL) is one potential pathway for AI development that enables learning across hospitals without data share. In this study, we show the results of various FL strategies on one of the largest and most diverse COVID-19 chest CT datasets: 21 participating hospitals across five continents that comprise >10,000 patients with >1 million images. We present three techniques: Fed Averaging (FedAvg), Incremental Institutional Learning (IIL), and Cyclical Incremental Institutional Learning (CIIL). We also propose an FL strategy that leverages synthetically generated data to overcome class imbalances and data size disparities across centers. We show that FL can achieve comparable performance to Centralized Data Sharing (CDS) while maintaining high performance across sites with small, underrepresented data. We investigate the strengths and weaknesses for all technical approaches on this heterogeneous dataset including the robustness to non-Independent and identically distributed (non-IID) diversity of data. We also describe the sources of data heterogeneity such as age, sex, and site locations in the context of FL and show how even among the correctly labeled populations, disparities can arise due to these biases

Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT

The Coronavirus disease 2019 (COVID-19) presents open questions in how we clinically diagnose and assess disease course. Recently, chest computed tomography (CT) has shown utility for COVID-19 diagnosis. In this study, we developed Deep COVID DeteCT (DCD), a deep learning convolutional neural network (CNN) that uses the entire chest CT volume to automatically predict COVID-19 (COVID+) from non-COVID-19 (COVID−) pneumonia and normal controls. We discuss training strategies and differences in performance across 13 international institutions and 8 countries. The inclusion of non-China sites in training significantly improved classification performance with area under the curve (AUCs) and accuracies above 0.8 on most test sites. Furthermore, using available follow-up scans, we investigate methods to track patient disease course and predict prognosis

Estudo da interação da radiação de microondas com sistemas biológicos e aplicações

-O objetivo do presente projeto é estudar os possíveis mecanismos da interação de radiação de microondas de baixa intensidade em sistemas biológicos. Realizamos estudos em alguns tecidos vegetais e animais. Com relação ao tecido vegetal estudamos a taxa de crescimento da semente de milho e feijão expostos a radiação de microondas. A faixa de frequencia utilizada nas sementes foi de 10GHz e com uma intensidade em torno de 0,003 microwatts por centímetro quadrado. Pode-se observar um aumento da taxa de germinação nas sementes expostas à radiação de microondas quando comparada com o controle. Também estão sendo realizados experimentos de radiação de microondas proveniente de um telefone celular (GSM 1.8GHz) em ratos. As análises estão focadas nas alterações das proteínas de stress encontradas na glandula pineal dos ratos. Sabe-se que a via de sinalização de MAPKs é a mais importante na regulação transcricional induzida por estímulo extracelular (YOON e SEGER, 2006). ERK1 e ERK2 estão entre as proteínas que atuam nesta via de sinalização, sendo que culturas de Células Rat1 e HeLa apresentaram aumento da expressão de ERK1 e ERK2 (in vitro) quando expostas à radiação de aparelhos celulares (FRIENDMAN et al. 2007). Deste modo, nossos estudos visam avaliar a influência da radiação emitida por celulares sobre ERK1 e 2, além de outras proteínas, como AKT (que atua no ciclo celular), PKC (relacionada com o desenvolvimento de câncer) e TH (muito importante no sistema nervoso central). Para o presente estudo foi desenvolvido no Laboratório de Óptica da UFJF um dispositivo capaz de realizar ligações de um aparelho de celular sem influenciar na radiação emitida. O equipamento é controlado por um micorcontrolador PIC12F675 e usa um motor de passo PM55L-048 para pressionar as teclas

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.