Cargando…
Comparing a deep learning model's diagnostic performance to that of radiologists to detect Covid -19 features on chest radiographs
BACKGROUND: Whether the sensitivity of Deep Learning (DL) models to screen chest radiographs (CXR) for CoVID-19 can approximate that of radiologists, so that they can be adopted and used if real-time review of CXRs by radiologists is not possible, has not been explored before. OBJECTIVE: To evaluate...
Autores principales: | Krishnamoorthy, Sabitha, Ramakrishnan, Sudhakar, Colaco, Lanson Brijesh, Dias, Akshay, Gopi, Indu K, Gowda, Gautham A G, Aishwarya, KC, Ramanan, Veena, Chandran, Manju |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer - Medknow
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7996677/ https://www.ncbi.nlm.nih.gov/pubmed/33814762 http://dx.doi.org/10.4103/ijri.IJRI_914_20 |
Ejemplares similares
-
Do pediatric intensivists and radiologists concur on the interpretation of chest radiographs?
por: Chambliss, C Robert, et al.
Publicado: (1998) -
The impact of artificial intelligence on the reading times of radiologists for chest radiographs
por: Shin, Hyun Joo, et al.
Publicado: (2023) -
Novel Method to Improve Radiologist Agreement in Interpretation of Serial Chest Radiographs in the ICU
por: Castro, Denise A, et al.
Publicado: (2015) -
Multireader evaluation of radiologist performance for COVID-19 detection on emergency department chest radiographs
por: Gichoya, Judy W., et al.
Publicado: (2022) -
Comparison of Chest Radiograph Captions Based on Natural Language Processing vs Completed by Radiologists
por: Zhang, Yaping, et al.
Publicado: (2023)