Deep Learning Algorithms for Interpretation of Upper Extremity Radiographs: Laterality and Technologist Initial Labels as Confounding Factors.
AJR Am J Roentgenol
; 218(4): 714-715, 2022 04.
Article
em En
| MEDLINE
| ID: mdl-34755522
ABSTRACT
Convolutional neural networks (CNNs) trained to identify abnormalities on upper extremity radiographs achieved an AUC of 0.844 with a frequent emphasis on radiograph laterality and/or technologist labels for decision-making. Covering the labels increased the AUC to 0.857 (p = .02) and redirected CNN attention from the labels to the bones. Using images of radiograph labels alone, the AUC was 0.638, indicating that radiograph labels are associated with abnormal examinations. Potential radiographic confounding features should be considered when curating data for radiology CNN development.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Aprendizado Profundo
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article