AI-based automated detection and stability analysis of traumatic vertebral body fractures on computed tomography.
Eur J Radiol
; 173: 111364, 2024 Apr.
Article
de En
| MEDLINE
| ID: mdl-38364589
ABSTRACT
PURPOSE:
We developed and tested a neural network for automated detection and stability analysis of vertebral body fractures on computed tomography (CT). MATERIALS ANDMETHODS:
257 patients who underwent CT were included in this Institutional Review Board (IRB) approved study. 463 fractured and 1883 non-fractured vertebral bodies were included, with 190 fractures unstable. Two readers identified vertebral body fractures and assessed their stability. A combination of a Hierarchical Convolutional Neural Network (hNet) and a fracture Classification Network (fNet) was used to build a neural network for the automated detection and stability analysis of vertebral body fractures on CT. Two final test settings were chosen one with vertebral body levels C1/2 included and one where they were excluded.RESULTS:
The mean age of the patients was 68 ± 14 years. 140 patients were female. The network showed a slightly higher diagnostic performance when excluding C1/2. Accordingly, the network was able to distinguish fractured and non-fractured vertebral bodies with a sensitivity of 75.8 % and a specificity of 80.3 %. Additionally, the network determined the stability of the vertebral bodies with a sensitivity of 88.4 % and a specificity of 80.3 %. The AUC was 87 % and 91 % for fracture detection and stability analysis, respectively. The sensitivity of our network in indicating the presence of at least one fracture / one unstable fracture within the whole spine achieved values of 78.7 % and 97.2 %, respectively, when excluding C1/2.CONCLUSION:
The developed neural network can automatically detect vertebral body fractures and evaluate their stability concurrently with a high diagnostic performance.Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Fractures du rachis
/
Corps vertébral
Limites:
Aged
/
Aged80
/
Female
/
Humans
/
Male
/
Middle aged
Langue:
En
Journal:
Eur J Radiol
/
Eur. j. radiol
/
European journal of radiology
Année:
2024
Type de document:
Article
Pays d'affiliation:
Allemagne
Pays de publication:
Irlande