Vertebral body insufficiency fractures: detection of vertebrae at risk on standard CT images using texture analysis and machine learning

• Published in: European radiology Vol. 29; no. 5; pp. 2207 - 2217
• Main Authors: Muehlematter, Urs J., Mannil, Manoj, Becker, Anton S., Vokinger, Kerstin N., Finkenstaedt, Tim, Osterhoff, Georg, Fischer, Michael A., Guggenberger, Roman
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2019; Springer Nature B.V
• Subjects: Aged; Aged, 80 and over; Artificial intelligence; Bone Density; Case-Control Studies; Classification; Computed Tomography; Confidence intervals; Diagnostic Radiology; Diagnostic systems; Feature extraction; Female; Fractures; Fractures, Stress - diagnosis; Humans; Image detection; Imaging; Internal Medicine; Interventional Radiology; Learning algorithms; Lumbar Vertebrae - diagnostic imaging; Lumbar Vertebrae - injuries; Machine Learning; Male; Medical imaging; Medicine; Medicine & Public Health; Middle Aged; Neuroradiology; Osteoporosis; Patients; Radiology; Retrospective Studies; Risk analysis; ROC Curve; Source code; Spinal Fractures - diagnosis; Spine; Texture; Thoracic Vertebrae - diagnostic imaging; Thoracic Vertebrae - injuries; Tomography, X-Ray Computed - methods; Ultrasound; Vertebrae; Osteoporosis; Tomography, X-ray computed; Spine; Machine learning

Purpose To evaluate the diagnostic performance of bone texture analysis (TA) combined with machine learning (ML) algorithms in standard CT scans to identify patients with vertebrae at risk for insufficiency fractures. Materials and methods Standard CT scans of 58 patients with insufficiency fracture...