Publications by authors named "Eduardo P Reis"
Article Synopsis
- Researchers developed and validated an open-source AI algorithm to detect different contrast phases in abdominal CT scans, using data from 739 exams across 200 patients.
- The algorithm achieved high accuracy rates of 92.3% for internal testing and 90.1% for external validation, indicating strong performance in identifying non-contrast, arterial, venous, and delayed phases.
- The study confirms the algorithm's effectiveness and potential for clinical applications, enhancing how medical professionals interpret CT scans for improved patient outcomes.
Article Synopsis
- Analyzing large amounts of textual data from electronic health records can overwhelm clinicians, affecting their time management.
- This study tested eight large language models (LLMs) on various clinical summarization tasks, finding that their adapted versions performed comparably or better than expert medical summaries in many cases.
- The research indicates that integrating LLMs into clinical processes might reduce documentation workload, helping doctors dedicate more time to patient care.
Article Synopsis
- Large language models (LLMs) show potential in summarizing electronic health records (EHR), but their effectiveness in clinical tasks needed further exploration.
- The study evaluated eight LLMs across various clinical summary tasks and found that the best-adapted models often produced superior summaries compared to human-generated ones.
- Results suggest that using LLMs in clinical settings could reduce the time clinicians spend on documentation, allowing them to focus more on direct patient care.
Article Synopsis
- - The study focuses on optimizing the height scaling method used to assess sarcopenia (muscle loss) via the skeletal muscle index (SMI) by analyzing skeletal muscle area (SMA) from CT scans of 16,575 patients.
- - Researchers conducted allometric analysis to determine the best height scaling powers for different age and sex groups, finding deviations from the traditional square height method that indicated better predictions of muscle mass.
- - The findings suggested that using these newly derived scaling powers improved the SMI's ability to predict all-cause mortality, highlighting the importance of accurate muscle assessment in clinical settings.
Artificial intelligence (AI) models for automatic generation of narrative radiology reports from images have the potential to enhance efficiency and reduce the workload of radiologists. However, evaluating the correctness of these reports requires metrics that can capture clinically pertinent differences. In this study, we investigate the alignment between automated metrics and radiologists' scoring of errors in report generation.
View Article and Find Full Text PDFStudies evaluating the local quality of death certification in Brazil focused on completeness of death reporting or inappropriate coding of causes of death, with few investigating missing data. We aimed to use missing and unexpected values in core topics to assess the quality of death certification in Brazilian municipalities, to evaluate its correlation with the percentage of garbage codes, and to employ a data-driven approach with non-linear models to investigate the association of the socioeconomic and health infrastructure context with quality of death statistics among municipalities. This retrospective study used data from the Mortality Information System (2010-2017), and municipal data regarding healthcare infrastructure, socioeconomic characteristics, and death rates.
View Article and Find Full Text PDFChest 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.
View Article and Find Full Text PDFThe 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.
View Article and Find Full Text PDFObjective This study describes epidemiological and clinical features of patients with confirmed infection by SARS-CoV-2 diagnosed and treated at Hospital Israelita Albert Einstein , which admitted the first patients with this condition in Brazil. Methods In this retrospective, single-center study, we included all laboratory confirmed COVID-19 cases at Hospital Israelita Albert Einstein , São Paulo, Brazil, from February until March 2020. Demographic, clinical, laboratory and radiological data were analyzed.
View Article and Find Full Text PDFUnlabelled: Transaxonal degenerations result from neuronal death or the interruption of synaptic connections among neuronal structures. These degenerations are not common but may be recognized by conventional magnetic resonance imaging.
Objective: The learning objectives of this review include recognition of the imaging characteristics of transaxonal degenerations involving cerebellar connections, the identification of potential encephalic lesions that can lead to these degenerations and correlation of the clinical manifestations with imaging findings that reflect this involvement.
Background: Ischemic postconditioning (IP) in renal Ischemia reperfusion injury (IRI) models improves renal function after IRI. Ketamine affords significant benefits against IRI-induced acute kidney injury (AKI). The present study investigated the effects of IP and IP associated with subanesthetic S(+)-ketamine in ischemia-reperfusion-induced AKI.
View Article and Find Full Text PDF