AIMC Topic: Spinal Fractures

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Clinical and Radiologic Outcomes of Robot-Assisted Kyphoplasty versus Fluoroscopy-Assisted Kyphoplasty in the Treatment of Osteoporotic Vertebral Compression Fractures: A Retrospective Comparative Study.

World neurosurgery
BACKGROUND: Making surgery as less aggressive as possible is best for elderly patients with osteoporotic vertebral compression fractures (OVCFs). Recently, we attempted a more precise, minimally invasive, and robot-assisted kyphoplasty in our clinica...

A deep-learning model for identifying fresh vertebral compression fractures on digital radiography.

European radiology
OBJECTIVES: To develop a deep-learning (DL) model for identifying fresh VCFs from digital radiography (DR), with magnetic resonance imaging (MRI) as the reference standard.

Deep Learning in the Detection of Rare Fractures - Development of a "Deep Learning Convolutional Network" Model for Detecting Acetabular Fractures.

Zeitschrift fur Orthopadie und Unfallchirurgie
BACKGROUND: Fracture detection by artificial intelligence and especially Deep Convolutional Neural Networks (DCNN) is a topic of growing interest in current orthopaedic and radiological research. As learning a DCNN usually needs a large amount of tra...

Automated Vertebral Segmentation and Measurement of Vertebral Compression Ratio Based on Deep Learning in X-Ray Images.

Journal of digital imaging
Vertebral compression fracture is a deformity of vertebral bodies found on lateral spine images. To diagnose vertebral compression fracture, accurate measurement of vertebral compression ratio is required. Therefore, rapid and accurate segmentation o...

Diagnostic Accuracy and Failure Mode Analysis of a Deep Learning Algorithm for the Detection of Cervical Spine Fractures.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Artificial intelligence decision support systems are a rapidly growing class of tools to help manage ever-increasing imaging volumes. The aim of this study was to evaluate the performance of an artificial intelligence decision...

Feasibility of a generalized convolutional neural network for automated identification of vertebral compression fractures: The Manitoba Bone Mineral Density Registry.

Bone
BACKGROUND: Vertebral fracture assessment (VFA) images are acquired in dual-energy (DE) or single-energy (SE) scan modes. Automated identification of vertebral compression fractures, from VFA images acquired using GE Healthcare scanners in DE mode, h...

Differential diagnosis of benign and malignant vertebral fracture on CT using deep learning.

European radiology
OBJECTIVES: To evaluate the performance of deep learning using ResNet50 in differentiation of benign and malignant vertebral fracture on CT.

Application of deep learning algorithm to detect and visualize vertebral fractures on plain frontal radiographs.

PloS one
BACKGROUND: Identification of vertebral fractures (VFs) is critical for effective secondary fracture prevention owing to their association with the increasing risks of future fractures. Plain abdominal frontal radiographs (PARs) are a common investig...

Artificial intelligence for the detection of vertebral fractures on plain spinal radiography.

Scientific reports
Vertebral fractures (VFs) cause serious problems, such as substantial functional loss and a high mortality rate, and a delayed diagnosis may further worsen the prognosis. Plain thoracolumbar radiography (PTLR) is an essential method for the evaluatio...

Identification of Vertebral Fractures by Convolutional Neural Networks to Predict Nonvertebral and Hip Fractures: A Registry-based Cohort Study of Dual X-ray Absorptiometry.

Radiology
Background Detection of vertebral fractures (VFs) aids in management of osteoporosis and targeting of fracture prevention therapies. Purpose To determine whether convolutional neural networks (CNNs) can be trained to identify VFs at VF assessment (VF...