AIMC Topic: Lumbar Vertebrae

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Automated segmentation of 2D low-dose CT images of the psoas-major muscle using deep convolutional neural networks.

Radiological physics and technology
The psoas-major muscle has been reported as a predictive factor of sarcopenia. The cross-sectional area (CSA) of the psoas-major muscle in axial images has been indicated to correlate well with the whole-body skeletal muscle mass. In this study, we e...

Random forest classifiers aid in the detection of incidental osteoblastic osseous metastases in DEXA studies.

International journal of computer assisted radiology and surgery
PURPOSE: Dual-energy X-ray absorptiometry (DEXA) studies are used for screening patients for low bone mineral density (BMD). Patients with breast and prostate cancer are often treated with hormone-altering drugs that result in low BMD. These patients...

Qualitative versus quantitative lumbar spinal stenosis grading by machine learning supported texture analysis-Experience from the LSOS study cohort.

European journal of radiology
PURPOSE: To investigate and compare the reproducibility and accuracy of qualitative ratings and quantitative texture analysis (TA) in detection and grading of lumbar spinal stenosis (LSS) in magnetic resonance imaging (MR) scans of the lumbar spine.

Robot-Assisted Versus Fluoroscopy-Guided Pedicle Screw Placement in Transforaminal Lumbar Interbody Fusion for Lumbar Degenerative Disease.

World neurosurgery
OBJECTIVE: To compare the clinical accuracy and perioperative outcomes for pedicle screw placement in transforaminal lumbar interbody fusion (TLIF) between the robot-assisted (RA) technique and fluoroscopy-guided (FG) technique.

Machine learning modeling for predicting hospital readmission following lumbar laminectomy.

Journal of neurosurgery. Spine
In BriefAuthors of this study analyzed hospital readmissions following laminectomy and developed predictive models to identify readmitted patients with an accuracy >95% when using all variables and >79% when using only predischarge variables. A model...

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

European radiology
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.

Can low-frequency guided waves at the tibia paired with machine learning differentiate between healthy and osteopenic/osteoporotic subjects? A pilot study.

Ultrasonics
PURPOSE: Axial transmission quantitative acoustics (ax-QA) has shown to be a promising tool for assessing bone health and properties in a safe, inexpensive, and portable manner. This study investigated the efficacy of low-frequency ax-QA measured at ...

Deep learning-based preoperative predictive analytics for patient-reported outcomes following lumbar discectomy: feasibility of center-specific modeling.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: There is considerable variability in patient-reported outcome measures following surgery for lumbar disc herniation. Individualized prediction tools that are derived from center- or even surgeon-specific data could provide valuabl...

Robot-assisted percutaneous placement of K-wires during minimally invasive interventions of the spine.

Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy
To assess the accuracy and time requirements of image-guided percutaneous K-wire insertion in the spine using an advanced robot assistance device for needle guidance and to demonstrate a radiation-free workflow for the physician. A planning CT-scan...

Fully automatic cross-modality localization and labeling of vertebral bodies and intervertebral discs in 3D spinal images.

International journal of computer assisted radiology and surgery
PURPOSE: We present a cross-modality and fully automatic pipeline for labeling of intervertebral discs and vertebrae in volumetric data of the lumbar and thoracolumbar spine. The main goal is to provide an algorithm that is applicable to a wide range...