Cargando…
Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study
OBJECTIVES: Artificial intelligence (AI) algorithms have been developed to detect imaging features on chest X-ray (CXR) with a comprehensive AI model capable of detecting 124 CXR findings being recently developed. The aim of this study was to evaluate the real-world usefulness of the model as a diag...
Autores principales: | Jones, Catherine M, Danaher, Luke, Milne, Michael R, Tang, Cyril, Seah, Jarrel, Oakden-Rayner, Luke, Johnson, Andrew, Buchlak, Quinlan D, Esmaili, Nazanin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8689166/ https://www.ncbi.nlm.nih.gov/pubmed/34930738 http://dx.doi.org/10.1136/bmjopen-2021-052902 |
Ejemplares similares
-
Chest radiographs and machine learning – Past, present and future
por: Jones, Catherine M, et al.
Publicado: (2021) -
Do comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax detection in chest radiography
por: Seah, Jarrel, et al.
Publicado: (2021) -
Analysis of Line and Tube Detection Performance of a Chest X-ray Deep Learning Model to Evaluate Hidden Stratification
por: Tang, Cyril H. M., et al.
Publicado: (2023) -
Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review
por: Ahmad, Hassan K., et al.
Publicado: (2023) -
Evaluating the performance of artificial intelligence software for lung nodule detection on chest radiographs in a retrospective real-world UK population
por: Maiter, Ahmed, et al.
Publicado: (2023)