Cargando…
A machine learning model based on readers’ characteristics to predict their performances in reading screening mammograms
OBJECTIVES: Proposing a machine learning model to predict readers’ performances, as measured by the area under the receiver operating characteristics curve (AUC) and lesion sensitivity, using the readers’ characteristics. METHODS: Data were collected from 905 radiologists and breast physicians who c...
Autores principales: | Gandomkar, Ziba, Lewis, Sarah J., Li, Tong, Ekpo, Ernest U., Brennan, Patrick C. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Nature Singapore
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9226081/ https://www.ncbi.nlm.nih.gov/pubmed/35122217 http://dx.doi.org/10.1007/s12282-022-01335-3 |
Ejemplares similares
-
Reliability of radiologists’ first impression when interpreting a screening mammogram
por: Gandomkar, Ziba, et al.
Publicado: (2023) -
Global processing provides malignancy evidence complementary to the information captured by humans or machines following detailed mammogram inspection
por: Gandomkar, Ziba, et al.
Publicado: (2021) -
Using Radiomics-Based Machine Learning to Create Targeted Test Sets to Improve Specific Mammography Reader Cohort Performance: A Feasibility Study
por: Tao, Xuetong, et al.
Publicado: (2023) -
Impact of the second reader on screening outcome at blinded double reading of digital screening mammograms
por: Coolen, Angela M. P., et al.
Publicado: (2018) -
Multi-vendor evaluation of artificial intelligence as an independent reader for double reading in breast cancer screening on 275,900 mammograms
por: Sharma, Nisha, et al.
Publicado: (2023)