Assessment of a Deep Learning Algorithm for the Detection of Rib Fractures on Whole-Body Trauma Computed Tomography.
Korean J Radiol
; 21(7): 891-899, 2020 07.
Article
em En
| MEDLINE
| ID: mdl-32524789
ABSTRACT
OBJECTIVE:
To assess the diagnostic performance of a deep learning-based algorithm for automated detection of acute and chronic rib fractures on whole-body trauma CT. MATERIALS ANDMETHODS:
We retrospectively identified all whole-body trauma CT scans referred from the emergency department of our hospital from January to December 2018 (n = 511). Scans were categorized as positive (n = 159) or negative (n = 352) for rib fractures according to the clinically approved written CT reports, which served as the index test. The bone kernel series (1.5-mm slice thickness) served as an input for a detection prototype algorithm trained to detect both acute and chronic rib fractures based on a deep convolutional neural network. It had previously been trained on an independent sample from eight other institutions (n = 11455).RESULTS:
All CTs except one were successfully processed (510/511). The algorithm achieved a sensitivity of 87.4% and specificity of 91.5% on a per-examination level [per CT scan rib fracture(s) yes/no]. There were 0.16 false-positives per examination (= 81/510). On a per-finding level, there were 587 true-positive findings (sensitivity 65.7%) and 307 false-negatives. Furthermore, 97 true rib fractures were detected that were not mentioned in the written CT reports. A major factor associated with correct detection was displacement.CONCLUSION:
We found good performance of a deep learning-based prototype algorithm detecting rib fractures on trauma CT on a per-examination level at a low rate of false-positives per case. A potential area for clinical application is its use as a screening tool to avoid false-negative radiology reports.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Fraturas das Costelas
/
Ferimentos e Lesões
/
Tomografia Computadorizada por Raios X
/
Aprendizado Profundo
Tipo de estudo:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Revista:
Korean J Radiol
Assunto da revista:
RADIOLOGIA
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Suíça