AI-based lumbar central canal stenosis classification on sagittal MR images is comparable to experienced radiologists using axial images.
Jasper W van der GraafLiron BrundelMiranda L van HooffMarinus de KleuverNikolas LessmannBas J MareschMyrthe M VesteringJacco SpermonBram van GinnekenMatthieu J C M RuttenPublished in: European radiology (2024)
Question How can the classification of lumbar central canal stenosis (LCCS) be made more efficient? Findings Multiclass and binary AI models, using only sagittal MR images, performed on par with experienced radiologists who also had access to axial sequences. Clinical relevance Our AI algorithm accurately classifies LCCS from sagittal MRI, matching experienced radiologists. This study offers a promising tool for automated LCCS assessment from sagittal T2 MRI, potentially reducing the reliance on additional axial imaging.
Keyphrases
- deep learning
- artificial intelligence
- contrast enhanced
- convolutional neural network
- machine learning
- magnetic resonance imaging
- minimally invasive
- computed tomography
- high resolution
- diffusion weighted imaging
- mass spectrometry
- high throughput
- fluorescence imaging
- photodynamic therapy
- optical coherence tomography