AI-based automated detection and stability analysis of traumatic vertebral body fractures on computed tomography
Purpose - We developed and tested a neural network for automated detection and stability analysis of vertebral body fractures on computed tomography (CT). - Materials and Methods - 257 patients who underwent CT were included in this Institutional Review Board (IRB) approved study. 463 fractured and...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
April 2024
|
| In: |
European journal of radiology
Year: 2024, Volume: 173, Pages: 1-7 |
| ISSN: | 1872-7727 |
| DOI: | 10.1016/j.ejrad.2024.111364 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.ejrad.2024.111364 Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S0720048X24000809 
|
| Author Notes: | Constanze Polzer, Eren Yilmaz, Carsten Meyer, Hyungseok Jang, Olav Jansen, Cristian Lorenz, Christian Bürger, Claus-Christian Glüer, Sam Sedaghat |
| Summary: | Purpose - We developed and tested a neural network for automated detection and stability analysis of vertebral body fractures on computed tomography (CT). - Materials and Methods - 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. |
|---|---|
| Item Description: | Online verfügbar: 13. Februar 2024, Artikelversion: 16. Februar 2024 Gesehen am 21.08.2024 |
| Physical Description: | Online Resource |
| ISSN: | 1872-7727 |
| DOI: | 10.1016/j.ejrad.2024.111364 |