AI-based detection and classification of distal radius fractures using low-effort data labeling: evaluation of applicability and effect of training set size

OBJECTIVES: To evaluate the performance of a deep convolutional neural network (DCNN) in detecting and classifying distal radius fractures, metal, and cast on radiographs using labels based on radiology reports. The secondary aim was to evaluate the effect of the training set size on the algorithm’s...

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Detalles Bibliográficos
Autores principales: Tobler, Patrick, Cyriac, Joshy, Kovacs, Balazs K., Hofmann, Verena, Sexauer, Raphael, Paciolla, Fabiano, Stieltjes, Bram, Amsler, Felix, Hirschmann, Anna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Musculoskeletal
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8379111/
https://www.ncbi.nlm.nih.gov/pubmed/33742228
http://dx.doi.org/10.1007/s00330-021-07811-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8379111/
https://www.ncbi.nlm.nih.gov/pubmed/33742228
http://dx.doi.org/10.1007/s00330-021-07811-2

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