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Spine

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Artificial Intelligence in Spine and Paraspinal Muscle Analysis.

Advances in experimental medicine and biology
Disorders affecting the neurological and musculoskeletal systems represent international health burdens. A significant impediment to progress with interventional trials is the absence of responsive, objective, and valid outcome measures sensitive to ...

Computational Modeling, Augmented Reality, and Artificial Intelligence in Spine Surgery.

Advances in experimental medicine and biology
Over the past decade, advancements in computational modeling, augmented reality, and artificial intelligence (AI) have been driving innovations in spine surgery. Much of the research conducted in these fields is from the past 5 years. In 2021, the ma...

Construction and verification of a machine learning-based prediction model of deep vein thrombosis formation after spinal surgery.

International journal of medical informatics
BACKGROUND: Deep vein thromboembolism (DVT) is a common postoperative complication with high morbidity and mortality rates. However, the safety and effectiveness of using prophylactic anticoagulants for preventing DVT after spinal surgery remain cont...

Machine learning identifies clusters of the normal adolescent spine based on sagittal balance.

Spine deformity
PURPOSE: This study applied a machine learning semi-supervised clustering approach to radiographs of adolescent sagittal spines from a single pediatric institution to identify patterns of sagittal alignment in the normal adolescent spine. We sought t...

A review: artificial intelligence in image-guided spinal surgery.

Expert review of medical devices
INTRODUCTION: Due to the complex anatomy of the spine and the intricate surgical procedures involved, spinal surgery demands a high level of technical expertise from surgeons. The clinical application of image-guided spinal surgery has significantly ...

Classifying High-Risk Patients for Persistent Opioid Use After Major Spine Surgery: A Machine-Learning Approach.

Anesthesia and analgesia
BACKGROUND: Persistent opioid use is a common occurrence after surgery and prolonged exposure to opioids may result in escalation and dependence. The objective of this study was to develop machine-learning-based predictive models for persistent opioi...

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...

A machine learning approach for the design optimization of a multiple magnetic and inertial sensors wearable system for the spine mobility assessment.

Journal of neuroengineering and rehabilitation
BACKGROUND: Recently, magnetic and inertial measurement units (MIMU) based systems have been applied in the spine mobility assessment; this evaluation is essential in the clinical practice for diagnosis and treatment evaluation. The available systems...

SPINEPS-automatic whole spine segmentation of T2-weighted MR images using a two-phase approach to multi-class semantic and instance segmentation.

European radiology
OBJECTIVES: Introducing SPINEPS, a deep learning method for semantic and instance segmentation of 14 spinal structures (ten vertebra substructures, intervertebral discs, spinal cord, spinal canal, and sacrum) in whole-body sagittal T2-weighted turbo ...

An explainable machine learning estimated biological age based on morphological parameters of the spine.

GeroScience
Accurately estimating biological age is beneficial for measuring aging and predicting risk. It is widely accepted that the prevalence of spine compression increases significantly with age. However, biological age based on vertebral morphological data...