AIMC Topic: Spinal Stenosis

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Computational analysis of L4-L5 interspinous process devices and interbody fusion spacers using ceramic and polymeric materials via finite element modeling and artificial intelligence.

Scientific reports
Lumbar spinal stenosis involves pathological narrowing of the spinal canal, whereas disc degeneration refers to the progressive deterioration of intervertebral disc structure and function. Interspinous process devices (ISPs) are commonly used to mana...

Prediction of early postoperative complications and transfusion risk after lumbar spinal stenosis surgery in geriatric patients: machine learning approach based on comprehensive geriatric assessment.

BMC medical informatics and decision making
BACKGROUND: Lumbar spinal stenosis is one of the most common surgery-requiring conditions of the spine in the aged population. As elderly patients often present with multiple comorbidities and limited physiological reserve, individualized risk assess...

Identifying patients at risk of increased health utilization following lumbar spine surgery.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
BACKGROUND: Adequate preoperative identification of patients at risk of significant healthcare utilization after surgery could help guide preoperative decision-making as well as postoperative patient management. While several studies have proposed me...

Torg-Pavlov ratio qualification to diagnose developmental cervical spinal stenosis based on HRViT neural network.

BMC musculoskeletal disorders
BACKGROUND: Developing computer-assisted methods to measure the Torg-Pavlov ratio (TPR), defined as the ratio of the sagittal diameter of the cervical spinal canal to the sagittal diameter of the corresponding vertebral body on lateral radiographs, c...

Deep learning-based prediction of cervical canal stenosis from mid-sagittal T2-weighted MRI.

Skeletal radiology
OBJECTIVE: This study aims to establish a large degenerative cervical myelopathy cohort and develop deep learning models for predicting cervical canal stenosis from sagittal T2-weighted MRI.

Using deep learning to enhance reporting efficiency and accuracy in degenerative cervical spine MRI.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Cervical spine MRI is essential for evaluating degenerative cervical spondylosis (DCS) but is time-consuming to report and subject to interobserver variability. The integration of artificial intelligence in medical imaging offers ...

Artificial intelligence for segmentation and classification in lumbar spinal stenosis: an overview of current methods.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PURPOSE: Lumbar spinal stenosis (LSS) is a frequently occurring condition defined by narrowing of the spinal or nerve root canal due to degenerative changes. Physicians use MRI scans to determine the severity of stenosis, occasionally complementing i...

CDUNeXt: efficient ossification segmentation with large kernel and dual cross gate attention.

Scientific reports
Ossification of the ligamentum flavum (OLF) is the main causative factor of spinal stenosis, but how to accurately and efficiently identify the ossification region is a clinical pain point and an urgent problem to be solved. Currently, we can only re...