Machine Learning Predicts Decompression Levels for Lumbar Spinal Stenosis Using Canal Radiomic Features from Computed Tomography Myelography.
Guoxin FanDongdong WangYufeng LiZhipeng XuHong WangHuaqing LiuXiang LiaoPublished in: Diagnostics (Basel, Switzerland) (2023)
ML successfully extracted valuable and interpretable radiomic features from the spinal canal using CTM images, and accurately predicted decompression levels for LSS patients. The EmbeddingLSVC_SVM classifier has the potential to assist surgical decision making in clinical practice, as it showed high discrimination, advantageous calibration, and competitive utility in selecting decompression levels in LSS patients using canal radiomic features from CTM.
Keyphrases
- end stage renal disease
- computed tomography
- machine learning
- ejection fraction
- newly diagnosed
- chronic kidney disease
- clinical practice
- minimally invasive
- prognostic factors
- peritoneal dialysis
- deep learning
- magnetic resonance
- artificial intelligence
- patient reported outcomes
- risk assessment
- patient reported
- contrast enhanced