Machine Learning Classification of Body Part, Imaging Axis, and Intravenous Contrast Enhancement on CT Imaging.
Can Assoc Radiol J
; 75(1): 82-91, 2024 Feb.
Article
em En
| MEDLINE
| ID: mdl-37439250
ABSTRACT
Purpose:
The development and evaluation of machine learning models that automatically identify the body part(s) imaged, axis of imaging, and the presence of intravenous contrast material of a CT series of images.Methods:
This retrospective study included 6955 series from 1198 studies (501 female, 697 males, mean age 56.5 years) obtained between January 2010 and September 2021. Each series was annotated by a trained board-certified radiologist with labels consisting of 16 body parts, 3 imaging axes, and whether an intravenous contrast agent was used. The studies were randomly assigned to the training, validation and testing sets with a proportion of 70%, 20% and 10%, respectively, to develop a 3D deep neural network for each classification task. External validation was conducted with a total of 35,272 series from 7 publicly available datasets. The classification accuracy for each series was independently assessed for each task to evaluate model performance.Results:
The accuracies for identifying the body parts, imaging axes, and the presence of intravenous contrast were 96.0% (95% CI 94.6%, 97.2%), 99.2% (95% CI 98.5%, 99.7%), and 97.5% (95% CI 96.4%, 98.5%) respectively. The generalizability of the models was demonstrated through external validation with accuracies of 89.7 - 97.8%, 98.6 - 100%, and 87.8 - 98.6% for the same tasks.Conclusions:
The developed models demonstrated high performance on both internal and external testing in identifying key aspects of a CT series.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Aprendizado Profundo
Tipo de estudo:
Observational_studies
Limite:
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Revista:
Can Assoc Radiol J
Assunto da revista:
RADIOLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Canadá