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Akademický článek

Can cerebellar and brainstem apparent diffusion coefficient (ADC) values predict neuromotor outcome in term neonates with hypoxic-ischemic encephalopathy (HIE) treated with hypothermia?

Autor: Gemma Arca-Díaz, Thomas J Re, Marie Drottar, Carmen Rosa Fortuno, Katyucia De Macedo-Rodrigues, Kiho Im, Josep Figueras-Aloy, Patricia Ellen Grant

Publikováno v: PLoS ONE, Vol 12, Iss 7, p e0178510 (2017)

To determine the apparent diffusion coefficient (ADC) in specific infratentorial brain structures during the first week of life and its relation with neuromotor outcome for Hypoxic-ischemic encephalopathy (HIE) in term neonates with and without whole

Externí odkaz: https://doaj.org/article/5c0bf05efcc848cb8c97a264d9fca6bc

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Evaluating deep learning methods in detecting and segmenting different sizes of brain metastases on 3D post-contrast T1-weighted images

Autor: Eli Gibson, Yue Cao, Siqi Liu, Youngjin Yoo, James M. Balter, Thomas J. Re, Ceccaldi Pascal

Publikováno v: J Med Imaging (Bellingham)

Purpose: We investigate the impact of various deep-learning-based methods for detecting and segmenting metastases with different lesion volume sizes on 3D brain MR images. Approach: A 2.5D U-Net and a 3D U-Net were selected. We also evaluated weak le

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::78f97a24f64dc71d5d4a87db7044c470
https://doi.org/10.1117/1.jmi.8.3.037001

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Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks

Publikováno v: Scientific Reports
Scientific reports, vol 11, iss 1
Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)

With the rapid growth and increasing use of brain MRI, there is an interest in automated image classification to aid human interpretation and improve workflow. We aimed to train a deep convolutional neural network and assess its performance in identi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1704fd367bfc3982adba223fbe6f9b0e
http://europepmc.org/articles/PMC7994311

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Akademický článek

Implementable Prediction of Pressure Injuries in Hospitalized Adults: Model Development and Validation

Autor: Thomas J Reese, Henry J Domenico, Antonio Hernandez, Daniel W Byrne, Ryan P Moore, Jessica B Williams, Brian J Douthit, Elise Russo, Allison B McCoy, Catherine H Ivory, Bryan D Steitz, Adam Wright

Publikováno v: JMIR Medical Informatics, Vol 12, Pp e51842-e51842 (2024)

Abstract BackgroundNumerous pressure injury prediction models have been developed using electronic health record data, yet hospital-acquired pressure injuries (HAPIs) are increasing, which demonstrates the critical challenge of implementing these mo

Externí odkaz: https://doaj.org/article/256d797e406f48249030bc1e8aca9329

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Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings

Autor: Thomas Weikert, Raphael Twerenbold, Alexander W. Sauter, Jens Bremerich, Sasa Grbic, Constantin Anastasopoulos, Benedikt J. Wiggli, David J. Winkel, Dorin Comaniciu, Gregor Sommer, Thomas J. Re, Shikha Chaganti, Saikiran Rapaka, Tilo Niemann

Publikováno v: Korean Journal of Radiology

OBJECTIVE: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. MATERIALS AN

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7372a7ac2edccaa25fc9a7f0d222d521
https://doi.org/10.3348/kjr.2020.0994

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