Cargando…

How artificial intelligence improves radiological interpretation in suspected pulmonary embolism

OBJECTIVES: To evaluate and compare the diagnostic performances of a commercialized artificial intelligence (AI) algorithm for diagnosing pulmonary embolism (PE) on CT pulmonary angiogram (CTPA) with those of emergency radiologists in routine clinical practice. METHODS: This was an IRB-approved retr...

Descripción completa

Detalles Bibliográficos
Autores principales:	Cheikh, Alexandre Ben, Gorincour, Guillaume, Nivet, Hubert, May, Julien, Seux, Mylene, Calame, Paul, Thomson, Vivien, Delabrousse, Eric, Crombé, Amandine
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Springer Berlin Heidelberg 2022
Materias:	Imaging Informatics and Artificial Intelligence
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8938594/ https://www.ncbi.nlm.nih.gov/pubmed/35316363 http://dx.doi.org/10.1007/s00330-022-08645-2

Ejemplares similares

Emergency teleradiological activity is an epidemiological estimator and predictor of the covid-19 pandemic in mainland France
por: Crombé, Amandine, et al.
Publicado: (2021)

What Influences the Way Radiologists Express Themselves in Their Reports? A Quantitative Assessment Using Natural Language Processing
por: Crombé, Amandine, et al.
Publicado: (2022)

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)

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

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

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

Artificial intelligence in radiology: 100 commercially available products and their scientific evidence
por: van Leeuwen, Kicky G., et al.
Publicado: (2021)

To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines)
por: Omoumi, Patrick, et al.
Publicado: (2021)

Training opportunities of artificial intelligence (AI) in radiology: a systematic review
por: Schuur, Floor, et al.
Publicado: (2021)

Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE)
por: Kelly, Brendan S., et al.
Publicado: (2022)

COVID-19 and vaccination impact on radiology departments provided by Dose Archiving and Communication System
por: Meyrignac, Olivier, et al.
Publicado: (2023)

Algorithmic transparency and interpretability measures improve radiologists’ performance in BI-RADS 4 classification
por: Jungmann, Friederike, et al.
Publicado: (2022)

Automated detection and segmentation of intracranial hemorrhage suspect hyperdensities in non-contrast-enhanced CT scans of acute stroke patients
por: Schmitt, N., et al.
Publicado: (2021)

AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study
por: Meng, Fanyang, et al.
Publicado: (2022)

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)

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)

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)

Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers
por: Fournier, Laure, et al.
Publicado: (2021)

How do providers of artificial intelligence (AI) solutions propose and legitimize the values of their solutions for supporting diagnostic radiology workflow? A technography study in 2021
por: Mehrizi, Mohammad H. Rezazade, et al.
Publicado: (2022)

Impact of Vaccination and the Omicron Variant on COVID-19–related Chest CT Findings: A Multicenter Study
por: Crombé, Amandine, et al.
Publicado: (2023)

Interpretability With Accurate Small Models
por: Ghose, Abhishek, et al.
Publicado: (2020)

Toward the appropriate interpretation of Alphafold2
por: Xu, Tian, et al.
Publicado: (2023)

Interpretable neural networks: principles and applications
por: Liu, Zhuoyang, et al.
Publicado: (2023)

An Interpretable Predictive Model of Vaccine Utilization for Tanzania
por: Hariharan, Ramkumar, et al.
Publicado: (2020)

Matrix Profile-Based Interpretable Time Series Classifier
por: Guidotti, Riccardo, et al.
Publicado: (2021)

No silver bullet: interpretable ML models must be explained
por: Marques-Silva, Joao, et al.
Publicado: (2023)

Suspect glaucoma detection from corneal densitometry supported by machine learning
por: García-Jiménez, Andrés, et al.
Publicado: (2022)

Can Glaucoma Suspect Data Help to Improve the Performance of Glaucoma Diagnosis?
por: Abbasi, Ashkan, et al.
Publicado: (2023)

Interpreting vision and language generative models with semantic visual priors
por: Cafagna, Michele, et al.
Publicado: (2023)

Self-Organising Map Based Framework for Investigating Accounts Suspected of Money Laundering
por: Alshantti, Abdallah, et al.
Publicado: (2021)

Monitoring the Progression of Clinically Suspected Microbial Keratitis Using Convolutional Neural Networks
por: Kuo, Ming-Tse, et al.
Publicado: (2023)

Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort
por: Mortani Barbosa, Eduardo J., et al.
Publicado: (2021)

Clustering and Recognition of Spatiotemporal Features Through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks
por: Su, Kun, et al.
Publicado: (2020)

Developing a Cancer Digital Twin: Supervised Metastases Detection From Consecutive Structured Radiology Reports
por: Batch, Karen E., et al.
Publicado: (2022)

Learning Systems versus Future Everyday Domestic Life: A Designer’s Interpretation of Social Practice Imaginaries
por: Viaene, Emilia, et al.
Publicado: (2021)

Interpretable machine learning for predicting pathologic complete response in patients treated with chemoradiation therapy for rectal adenocarcinoma
por: Wang, Du, et al.
Publicado: (2022)

Overcoming the Challenges in the Development and Implementation of Artificial Intelligence in Radiology: A Comprehensive Review of Solutions Beyond Supervised Learning
por: Hong, Gil-Sun, et al.
Publicado: (2023)

Reliable and Interpretable Mortality Prediction With Strong Foresight in COVID-19 Patients: An International Study From China and Germany
por: Bai, Tao, et al.
Publicado: (2021)

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)

Cannot write session to /tmp/vufind_sessions/sess_2m2n45d99qe6bov3517h8bmsmm