Cargando…

Any unique image biomarkers associated with COVID-19?

OBJECTIVE: To define the uniqueness of chest CT infiltrative features associated with COVID-19 image characteristics as potential diagnostic biomarkers. METHODS: We retrospectively collected chest CT exams including n = 498 on 151 unique patients RT-PCR positive for COVID-19 and n = 497 unique patie...

Descripción completa

Detalles Bibliográficos
Autores principales:	Pu, Jiantao, Leader, Joseph, Bandos, Andriy, Shi, Junli, Du, Pang, Yu, Juezhao, Yang, Bohan, Ke, Shi, Guo, Youmin, Field, Jessica B., Fuhrman, Carl, Wilson, David, Sciurba, Frank, Jin, Chenwang
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Springer Berlin Heidelberg 2020
Materias:	Imaging Informatics and Artificial Intelligence
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7253230/ https://www.ncbi.nlm.nih.gov/pubmed/32462445 http://dx.doi.org/10.1007/s00330-020-06956-w

Ejemplares similares

Automated quantification of COVID-19 severity and progression using chest CT images
por: Pu, Jiantao, et al.
Publicado: (2020)

Systematic volumetric analysis predicts response to CSF drainage and outcome to shunt surgery in idiopathic normal pressure hydrocephalus
por: Wu, Dan, et al.
Publicado: (2021)

Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening
por: Pan, Yaling, et al.
Publicado: (2020)

CT-based radiomics for differentiating intracranial contrast extravasation from intraparenchymal haemorrhage after mechanical thrombectomy
por: Chen, Xiaojun, et al.
Publicado: (2022)

Development and multicenter validation of a CT-based radiomics signature for predicting severe COVID-19 pneumonia
por: Li, Liang, et al.
Publicado: (2021)

Deep learning assisted contrast-enhanced CT–based diagnosis of cervical lymph node metastasis of oral cancer: a retrospective study of 1466 cases
por: Xu, Xiaoshuai, et al.
Publicado: (2022)

Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs
por: Cheng, Chi-Tung, et al.
Publicado: (2019)

Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists
por: Antonelli, Michela, et al.
Publicado: (2019)

Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform
por: Fornacon-Wood, Isabella, et al.
Publicado: (2020)

Deep learning in interstitial lung disease—how long until daily practice
por: Trusculescu, Ana Adriana, et al.
Publicado: (2020)

Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2
por: Fang, Xu, et al.
Publicado: (2020)

Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia
por: Ippolito, Davide, et al.
Publicado: (2020)

Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients
por: Mushtaq, Junaid, et al.
Publicado: (2020)

Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis
por: Xue, Li-Yun, et al.
Publicado: (2020)

Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors
por: Strohm, Lea, et al.
Publicado: (2020)

Implementation of eHealth and AI integrated diagnostics with multidisciplinary digitized data: are we ready from an international perspective?
por: Bukowski, Mark, et al.
Publicado: (2020)

Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network
por: Ye, Hai, et al.
Publicado: (2019)

Decision-making based on 3D printed models in laparoscopic liver resections with intraoperative ultrasound: a prospective observational study
por: Witowski, Jan, et al.
Publicado: (2019)

Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer
por: Bleker, Jeroen, et al.
Publicado: (2019)

Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma—a systematic review and meta-analysis
por: Ursprung, Stephan, et al.
Publicado: (2020)

Radiologists in the loop: the roles of radiologists in the development of AI applications
por: Scheek, Damian, et al.
Publicado: (2021)

Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19
por: Feng, Zhichao, et al.
Publicado: (2021)

Automated age- and sex-specific volumetric estimation of regional brain atrophy: workflow and feasibility
por: Caspers, Julian, et al.
Publicado: (2020)

Artificial Intelligence–assisted chest X-ray assessment scheme for COVID-19
por: Rangarajan, Krithika, et al.
Publicado: (2021)

The usage of deep neural network improves distinguishing COVID-19 from other suspected viral pneumonia by clinicians on chest CT: a real-world study
por: Xie, Qiuchen, et al.
Publicado: (2020)

Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks
por: Koitka, Sven, et al.
Publicado: (2020)

Applications of artificial intelligence (AI) in diagnostic radiology: a technography study
por: Rezazade Mehrizi, Mohammad Hosein, et al.
Publicado: (2020)

Practical applications of deep learning: classifying the most common categories of plain radiographs in a PACS using a neural network
por: Dratsch, Thomas, et al.
Publicado: (2020)

A comparison between manual and artificial intelligence–based automatic positioning in CT imaging for COVID-19 patients
por: Gang, Yadong, et al.
Publicado: (2021)

A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)
por: Wang, Shuai, et al.
Publicado: (2021)

Quantitative CT radiomics-based models for prediction of haematoma expansion and poor functional outcome in primary intracerebral haemorrhage
por: Pszczolkowski, Stefan, et al.
Publicado: (2021)

Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis
por: O’Shea, Robert J., et al.
Publicado: (2021)

Pneumothorax detection in chest radiographs: optimizing artificial intelligence system for accuracy and confounding bias reduction using in-image annotations in algorithm training
por: Rueckel, Johannes, et al.
Publicado: (2021)

Liver fibrosis staging by deep learning: a visual-based explanation of diagnostic decisions of the model
por: Yin, Yunchao, et al.
Publicado: (2021)

Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis
por: van Kempen, Evi J., et al.
Publicado: (2021)

An international survey on AI in radiology in 1041 radiologists and radiology residents part 2: expectations, hurdles to implementation, and education
por: Huisman, Merel, et al.
Publicado: (2021)

An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude
por: Huisman, Merel, et al.
Publicado: (2021)

Advanced gastric cancer: CT radiomics prediction and early detection of downstaging with neoadjuvant chemotherapy
por: Xu, Qinmei, et al.
Publicado: (2021)

Detection of liver cirrhosis in standard T2-weighted MRI using deep transfer learning
por: Nowak, Sebastian, et al.
Publicado: (2021)

Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning
por: Ryu, Hwaseong, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_3cpaoo738tki89ksa9qm5pbgn9