Classification of Aortic Dissection and Rupture on Post-contrast CT Images Using a Convolutional Neural Network.
J Digit Imaging
; 32(6): 939-946, 2019 12.
Article
em En
| MEDLINE
| ID: mdl-31515752
ABSTRACT
Aortic dissections and ruptures are life-threatening injuries that must be immediately treated. Our national radiology practice receives dozens of these cases each month, but no automated process is currently available to check for critical pathologies before the images are opened by a radiologist. In this project, we developed a convolutional neural network model trained on aortic dissection and rupture data to assess the likelihood of these pathologies being present in prospective patients. This aortic injury model was used for study prioritization over the course of 4 weeks and model results were compared with clinicians' reports to determine accuracy metrics. The model obtained a sensitivity and specificity of 87.8% and 96.0% for aortic dissection and 100% and 96.0% for aortic rupture. We observed a median reduction of 395 s in the time between study intake and radiologist review for studies that were prioritized by this model. False-positive and false-negative data were also collected for retraining to provide further improvements in subsequent versions of the model. The methodology described here can be applied to a number of modalities and pathologies moving forward.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Ruptura Aórtica
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Interpretação de Imagem Radiográfica Assistida por Computador
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Tomografia Computadorizada por Raios X
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Redes Neurais de Computação
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Meios de Contraste
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Dissecção Aórtica
Tipo de estudo:
Diagnostic_studies
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Observational_studies
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Prognostic_studies
Limite:
Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article