AIMC Topic: Spine

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Identifying Primary Sites of Spinal Metastases: Expert-Derived Features vs. ResNet50 Model Using Nonenhanced MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: The spinal column is a frequent site for metastases, affecting over 30% of solid tumor patients. Identifying the primary tumor is essential for guiding clinical decisions but often requires resource-intensive diagnostics.

Automated spinopelvic measurements on radiographs with artificial intelligence: a multi-reader study.

La Radiologia medica
PURPOSE: To develop an artificial intelligence (AI) algorithm for automated measurements of spinopelvic parameters on lateral radiographs and compare its performance to multiple experienced radiologists and surgeons.

Development of a model for measuring sagittal plane parameters in 10-18-year old adolescents with idiopathic scoliosis based on RTMpose deep learning technology.

Journal of orthopaedic surgery and research
PURPOSE: The study aimed to develop a deep learning model for rapid, automated measurement of full-spine X-rays in adolescents with Adolescent Idiopathic Scoliosis (AIS). A significant challenge in this field is the time-consuming nature of manual me...

The use of machine learning for the prediction of response to follow-up in spine registries.

International journal of medical informatics
BACKGROUND: One of the main challenges in the maintenance of registries is to keep a high follow-up rate and a reliable strategy to limit dropout is currently lacking. Aim of this study was to utilize machine learning (ML) models to highlight the cha...

3D MFA: An automated 3D Multi-Feature Attention based approach for spine segmentation using a multi-stage network pruning.

Computers in biology and medicine
Spine segmentation poses significant challenges due to the complex anatomical structure of the spine and the variability in imaging modalities, which often results in unclear boundaries and overlaps with surrounding tissues. In this research, a novel...

Spine X-ray image segmentation based on deep learning and marker controlled watershed.

Journal of X-ray science and technology
BACKGROUND: The development of automatic methods for vertebral segmentation provides the objective analysis of each vertebra in the spine image, which is important for the diagnosis of various spinal diseases. However, vertebrae have inter-class simi...

Automatic 3-D Lamina Curve Extraction From Freehand 3-D Ultrasound Data Using Sequential Localization Recurrent Convolutional Networks.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Freehand 3-D ultrasound imaging is emerging as a promising modality for regular spine exams due to its noninvasiveness and affordability. The laminae landmarks play a critical role in depicting the 3-D shape of the spine. However, the extraction of t...

Accelerated Spine MRI with Deep Learning Based Image Reconstruction: A Prospective Comparison with Standard MRI.

Academic radiology
RATIONALE AND OBJECTIVES: To evaluate the performance of deep learning (DL) reconstructed MRI in terms of image acquisition time, overall image quality and diagnostic interchangeability compared to standard-of-care (SOC) MRI.

Optimization and control of robotic vertebral plate grinding: Predictive modeling, parameter optimization, and fuzzy control strategies for minimizing bone damage in laminectomy procedures.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
During the robotic grinding of vertebral plates in high-risk laminectomy procedures, programmed operations may inadvertently induce force or temperature-related damage to the bone tissue. Therefore, it is imperative to explore a control methodology a...

Augmenting a spine CT scans dataset using VAEs, GANs, and transfer learning for improved detection of vertebral compression fractures.

Computers in biology and medicine
In recent years, deep learning has become a popular tool to analyze and classify medical images. However, challenges such as limited data availability, high labeling costs, and privacy concerns remain significant obstacles. As such, generative models...