Cargando…

Detection of liver cirrhosis in standard T2-weighted MRI using deep transfer learning

OBJECTIVES: To investigate the diagnostic performance of deep transfer learning (DTL) to detect liver cirrhosis from clinical MRI. METHODS: The dataset for this retrospective analysis consisted of 713 (343 female) patients who underwent liver MRI between 2017 and 2019. In total, 553 of these subject...

Descripción completa

Detalles Bibliográficos
Autores principales:	Nowak, Sebastian, Mesropyan, Narine, Faron, Anton, Block, Wolfgang, Reuter, Martin, Attenberger, Ulrike I., Luetkens, Julian A., Sprinkart, Alois M.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Springer Berlin Heidelberg 2021
Materias:	Imaging Informatics and Artificial Intelligence
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8523404/ https://www.ncbi.nlm.nih.gov/pubmed/33974149 http://dx.doi.org/10.1007/s00330-021-07858-1

Ejemplares similares

Deep learning for standardized, MRI-based quantification of subcutaneous and subfascial tissue volume for patients with lipedema and lymphedema
por: Nowak, Sebastian, et al.
Publicado: (2022)

End-to-end automated body composition analyses with integrated quality control for opportunistic assessment of sarcopenia in CT
por: Nowak, Sebastian, et al.
Publicado: (2021)

Deep learning supports the differentiation of alcoholic and other-than-alcoholic cirrhosis based on MRI
por: Luetkens, Julian A., et al.
Publicado: (2022)

Transformer-based structuring of free-text radiology report databases
por: Nowak, S., et al.
Publicado: (2023)

Simplified intravoxel incoherent motion diffusion-weighted MRI of liver lesions: feasibility of combined two-colour index maps
por: Mürtz, Petra, et al.
Publicado: (2021)

Comparison of different ROI analysis methods for liver lesion characterization with simplified intravoxel incoherent motion (IVIM)
por: Mesropyan, Narine, et al.
Publicado: (2021)

Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability
por: Netzer, Nils, et al.
Publicado: (2023)

Synthetic extracellular volume fraction without hematocrit sampling for hepatic applications
por: Mesropyan, Narine, et al.
Publicado: (2021)

Accelerate gas diffusion-weighted MRI for lung morphometry with deep learning
por: Duan, Caohui, et al.
Publicado: (2021)

AI-assisted biparametric MRI surveillance of prostate cancer: feasibility study
por: Roest, C., et al.
Publicado: (2022)

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

Multiparametric cardiac magnetic resonance imaging in pediatric and adolescent patients with acute myocarditis
por: Isaak, Alexander, et al.
Publicado: (2021)

Sources of variation in multicenter rectal MRI data and their effect on radiomics feature reproducibility
por: Schurink, Niels W., et al.
Publicado: (2021)

Deep learning to automate the labelling of head MRI datasets for computer vision applications
por: Wood, David A., et al.
Publicado: (2021)

Assessment of liver cirrhosis severity with extracellular volume fraction MRI
por: Mesropyan, Narine, et al.
Publicado: (2022)

Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning
por: Lin, Yu-Chun, et al.
Publicado: (2023)

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)

Using deep learning to safely exclude lesions with only ultrafast breast MRI to shorten acquisition and reading time
por: Jing, Xueping, et al.
Publicado: (2022)

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)

Influence of hydration status on cardiovascular magnetic resonance myocardial T1 and T2 relaxation time assessment: an intraindividual study in healthy subjects
por: Luetkens, Julian A., et al.
Publicado: (2020)

Visual ensemble selection of deep convolutional neural networks for 3D segmentation of breast tumors on dynamic contrast enhanced MRI
por: Rahimpour, Masoomeh, et al.
Publicado: (2022)

Investigating the impact of structured reporting on the linguistic standardization of radiology reports through natural language processing over a 10-year period
por: Vosshenrich, Jan, et al.
Publicado: (2023)

Deep learning–assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge
por: Hosseinzadeh, Matin, et al.
Publicado: (2021)

Deep-learning-based 3D super-resolution MRI radiomics model: superior predictive performance in preoperative T-staging of rectal cancer
por: Hou, Min, et al.
Publicado: (2022)

Predicting visual recovery in pituitary adenoma patients post-endoscopic endonasal transsphenoidal surgery: Harnessing delta-radiomics of the optic chiasm from MRI
por: Zhang, Yang, et al.
Publicado: (2023)

Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors
por: Faron, Anton, et al.
Publicado: (2021)

Pediatric age estimation from radiographs of the knee using deep learning
por: Demircioğlu, Aydin, et al.
Publicado: (2022)

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

Body composition analysis using CT and MRI: intra-individual intermodal comparison of muscle mass and myosteatosis
por: Faron, Anton, et al.
Publicado: (2020)

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)

Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening
por: Pan, Yaling, 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)

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)

Cannot write session to /tmp/vufind_sessions/sess_bib0eprgerka4jf0vqbvdocb58