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This paper outlines the protocol for the deployment of a cloud-based universal medical image repository system. The proposal aims not only at the deployment but also at the automatic expansion of the platform, incorporating Artificial Intelligence (AI) for the analysis of medical image examinations. The methodology encompasses efficient data management through a universal database, along with the deployment of various AI models designed to assist in diagnostic decision-making. By presenting this protocol, the goal is to overcome technical challenges and issues that impact all phases of the workflow, from data management to the deployment of AI models in the healthcare sector. These challenges include ethical considerations, compliance with legal regulations, establishing user trust, and ensuring data security. The system has been deployed, with a tested and validated proof of concept, possessing the capability to receive thousands of images daily and to sustain the ongoing deployment of new AI models to expedite the analysis process in medical image exams., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Pacheco et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Silva et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

We present a Raman spectroscopy study of the vibrational properties of free-base meso-tetra(4-pyridyl) porphyrin polycrystals under various temperature and hydrostatic pressure conditions. The combination of experimental results and Density Functional Theory (DFT) calculations allows us to assign most of the observed Raman bands. The modifications in the Raman spectra when excited with 488 nm and 532 nm laser lights indicate that a resonance effect in the Qy band is taking place. The pressure-dependent results show that the resonance conditions change with increasing pressure, probably due to the shift of the electronic transitions. The temperature-dependent results show that the relative intensities of the Raman modes change at low temperatures, while no frequency shifts are observed. The experimental and theoretical analysis presented here suggest that these molecules are well represented by the C2v point symmetry group.

Chest radiographs allow for the meticulous examination of a patient's chest but demands specialized training for proper interpretation. Automated analysis of medical imaging has become increasingly accessible with the advent of machine learning (ML) algorithms. Large labeled datasets are key elements for training and validation of these ML solutions. In this paper we describe the Brazilian labeled chest x-ray dataset, BRAX: an automatically labeled dataset designed to assist researchers in the validation of ML models. The dataset contains 24,959 chest radiography studies from patients presenting to a large general Brazilian hospital. A total of 40,967 images are available in the BRAX dataset. All images have been verified by trained radiologists and de-identified to protect patient privacy. Fourteen labels were derived from free-text radiology reports written in Brazilian Portuguese using Natural Language Processing., (© 2022. The Author(s).)