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206 results for “Lumbar Spine”

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zenodo44/100

parametric CAD model of the lumbar spine

<p>7Tepe Parametric Lumbar Spine Model</p> <p>Tweaking parameters of associative CAD models offer interesting pathways for the application of artificial intelligence methods, such as optimization by genetic algorithms and neural networks.</p> <p>Construction of a robust lumbar verteral assembly requires a holistic design approach. The concept of using a layout-sketch as the core of a cad model is becoming more popular due to its top-down design philosophy. However, there remains local issues such as the contact properties at the transverse facet joints, which cannot be taken care of alone by the top-down approach. Inverting the perspective to a bottom-up approach and observing some of the intricate geometrical features of for example the laminae leads to a model that can simply be represented by a relatively scarce number of parameters, yet capturing the physiological aspect of the organ in quite an accurate way.</p> <p>For those who are interested in what our 7Tepe Parametric Lumbar Spine Model version 0.1 currently looks like, just download the source in Parasolid format (x_t extension) and import it to your favorite CAD system. Currently, we have not shared our source CAD file belonging to proprietary software products, but we intend to do so in the near future.</p> <p>Designed by Ogulcan in proprietary solid modeling software.</p>

opencc-by-4.0Apr 2018View details →
zenodo40/100

SPIDER - Lumbar spine segmentation in MR images: a dataset and a public benchmark

<p>This is a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset&nbsp;includes 447&nbsp;sagittal T1 and T2 MRI series from 218&nbsp;studies of 218 patients with a history of low back pain. The data was collected from four different hospitals. There is an additional&nbsp;hidden test set, not available here, used in the accompanying SPIDER challenge on spider.grand-challenge.org. We share this data&nbsp;to encourage wider participation and collaboration in the field of spine segmentation, and ultimately improve the diagnostic value of lumbar spine MRI.</p> <p>Which MRI studies are assigned to the training and validation sets can be found in the overview file. This file also provides the biological sex for all patients and the age for the patients for which this was available. It also includes a number of scanner and acquisition parameters for each individual MRI study. The dataset also comes with radiological gradings found in a separate file for the following degenerative changes:</p> <p>1.&ensp;&ensp;&ensp;&ensp;Modic changes (type I, II or III)</p> <p>2.&ensp;&ensp;&ensp;&ensp;Upper and lower endplate changes / Schmorl nodes (binary)</p> <p>3.&ensp;&ensp;&ensp;&ensp;Spondylolisthesis (binary)</p> <p>4.&ensp;&ensp;&ensp;&ensp;Disc herniation (binary)</p> <p>5.&ensp;&ensp;&ensp;&ensp;Disc narrowing (binary)</p> <p>6.&ensp;&ensp;&ensp;&ensp;Disc bulging (binary)</p> <p>7.&ensp;&ensp;&ensp;&ensp;Pfirrman grade (grade 1 to 5).&nbsp;</p> <p>All radiological gradings are provided per IVD level.</p> <div>This dataset, and the associated public benchmark, are described in this paper: <a href="https://www.nature.com/articles/s41597-024-03090-w" target="_blank" rel="noopener">https://www.nature.com/articles/s41597-024-03090-w</a></div> <div>The public segmenation challenge can be found here: <a href="https://spider.grand-challenge.org/" target="_blank" rel="noopener">https://spider.grand-challenge.org/</a></div> <div>&nbsp;</div> <div>When using this dataset, please cite this dataset with the correct DOI, and also cite the afformentioned paper.</div>

opencc-by-4.0Nov 2021View details →
zenodo40/100

Lumbar Spine Vertebral Compression Fractures (VCFs) Dataset: MRI T1-Weighted Images for Benign and Malignant Classification

<p>This dataset was prepared for the study of classification of benign vertebral compression fractures (VCFs) secondary to osteoporosis and malignant VCFs secondary to neoplastic infiltration. The original study in which it was used aimed to assist in differentiating these conditions using three-dimensional radiomic techniques and artificial neural networks.</p> <p>This dataset was assembled from sagittal T1-weighted magnetic resonance imaging (MRI) scans of the lumbar spine obtained from consecutive patients diagnosed with benign or malignant VCFs at the University Hospital of the Ribeir&atilde;o Preto Medical School (HCFMRP) between the years 2010 and 2019. The images were acquired using the Philips Achieva 1.5 T and 3 T MRI systems and were stored in the DICOM (Digital Imaging and Communications in Medicine) format.</p> <p>The compilation of the dataset followed a rigorous selection and filtering process. From the initial set of cases of vertebral fractures in the lumbar region, patients who had received prior treatment (such as chemotherapy, radiotherapy, or surgery), those with fractures of traumatic etiology, old fractures, patients under 18 years old, and cases of malignant fractures without biopsy confirmation were excluded. With these exclusions, the final set consists of 91 patients (36 men and 55 women, with a mean age of 64.24 &plusmn; 11.75 years), of which 47 have benign VCFs and 44 have malignant VCFs.</p> <p>For the segmentation of fractured vertebrae, the images were pre-processed by normalizing the intensity to 256 gray levels (0 to 255), and histogram equalization was applied to improve contrast. The vertebrae were semi-automatically segmented using the 3D Slicer software. Each segmentation was saved in the "nrrd" format, native to 3D Slicer. The entire segmentation process, as well as the definition and application of exclusion criteria, were supervised by a senior radiologist with 20 years of experience in musculoskeletal radiology.</p> <p>The structure of this dataset includes, in addition to the DICOM exams, a directory containing the requantized images to 256 gray levels in the "nrrd" format and another with the segmentation files in the "seg.nrrd" format. A spreadsheet with detailed information about each patient's class, sex, age, and which vertebral bodies were segmented is also available. All DICOM files in this dataset have been anonymized to ensure patient privacy.</p> <p>This dataset was structured to provide a robust basis for the training and validation of machine learning models focused on the classification of vertebral compression fractures. It was designed to aid in the massive three-dimensional extraction of radiomic features, enabling the search for radiomic signatures capable of assisting radiologists in the accurate characterization of these fractures.</p> <p>For more information on how the dataset was created and used, please refer to the original article: <a href="https://link.springer.com/article/10.1007/s10278-023-00847-4" target="_blank" rel="noopener"><em>Chiari-Correia NS, Nogueira-Barbosa MH, Chiari-Correia RD, Azevedo-Marques PM. A 3D Radiomics-Based Artificial Neural Network Model for Benign Versus Malignant Vertebral Compression Fracture Classification in MRI. J Digit Imaging. 2023;36(4):1565-1577. doi:10.1007/s10278-023-00847-4</em></a></p>

opencc-by-4.0Aug 2024View details →
zenodo40/100

Supplement to "Endogenous DHEAS is causally linked with lumbar spine bone mineral density and forearm fractures in women - A mendelian randomization study"

<p>Supplemental tables to &quot;Endogenous DHEAS is causally linked with lumbar spine bone mineral density and forearm fractures in women - A mendelian randomization study&quot; by Johan Quester,&nbsp;Maria Nethander, Anna Eriksson and Claes Ohlsson.</p>

opencc-by-4.0Sep 2021View details →
ClinicalTrials.gov40/100

Study of Opioid Use After Lumbar and Cervical Spine Surgery

ClinicalTrials.gov study NCT02674711. IPD Sharing: YES. Countries: 1. Publications: 9.

controlledIPD-YESFeb 2026View details →
zenodo36/100

Finite Element model of the upper lumbar and lower thoracic spine

<p>Finite element method&nbsp;(FEM)&nbsp;model of&nbsp;the lumbar spine,&nbsp;stored in <a href="https://code-aster.org/">code_aster</a> med-file format.</p> <p>The dataset is used to develop the SODALITE virtual clinical trial use-case. It contains a FEM-model of a part of the lumbar spine. Modelled are a part of the vertebra L2, the vertebra L1 and the intervertebral disc between them. The model is generated based on the&nbsp;coresponding computer tomographic imaging&nbsp;<a href="https://doi.org/10.5281/zenodo.3959070">volume dataset</a>&nbsp;and <a href="https://doi.org/10.5281/zenodo.3961270">iso-surface</a>.</p> <p>The datasets&#39; content is illustrated by the attached png image which shows a shaded surface rendering of the dataset.</p> <p>&nbsp;</p>

opencc-by-4.0Jul 2020View details →
dryad36/100

Small skeletons show size-specific scaling: an exploration of allometry in the mammalian lumbar spine

<p>Studies of vertebrate bone biomechanics often focus on skeletal adaptations at upper extremes of body mass, ignoring the importance of skeletal adaptations at lower extremes. Yet mammals are ancestrally small and most modern species have masses under 5 kg, so the evolution of morphology and function at small size should be prioritized for understanding how mammals make a living. We examined allometric scaling of lumbar vertebrae in the small-bodied Philippine endemic rodents known as cloud rats, which vary in mass across two orders of magnitude (15.5g-2700g). External vertebral dimensions scale with isometry or positive allometry, likely relating to body size and nuances in quadrupedal posture. In contrast to most mammalian trabecular bone studies, bone volume fraction and trabecular thickness scale with positive allometry and isometry, respectively. It is physiologically impossible for these trends to continue to the upper extremes of mammalian body size, and we demonstrate a fundamental difference in trabecular bone allometry between large- and small-bodied mammals. These findings have important implications for the biomechanical capabilities of mammalian bone at small body size, for the selective pressures that govern skeletal evolution in small mammals, and for the way we define "small" and "large" in the context of vertebrate skeletons.</p>

opencc-zeroMar 2024View details →
ClinicalTrials.gov36/100

Intraoperative Infusion of Precedex to Reduce Length of Stay After Lumbar Spine Fusion

ClinicalTrials.gov study NCT00808665. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov36/100

Lofexidine for Adults Undergoing Lumbar Spine Surgery

ClinicalTrials.gov study NCT04126083. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov36/100

Epidural Analgesia Versus IV Analgesia in Lumbar Spine Fusions

ClinicalTrials.gov study NCT01986946. IPD Sharing: Not stated. Countries: 1. Publications: 7.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov36/100

ESP Block in MIS Lumbar Spine Surgery

ClinicalTrials.gov study NCT05856539. IPD Sharing: NO. Countries: 1. Publications: 16.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov36/100

ViBone in Cervical and Lumbar Spine Fusion

ClinicalTrials.gov study NCT03425682. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov36/100

A Randomized Trial of Exparel vs Saline in Opioid Reduction of Pain Management Following Lumbar Spine Surgeries.

ClinicalTrials.gov study NCT04644796. IPD Sharing: NO. Countries: 1. Publications: 5.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov36/100

Comparison of the Effects of 2 Drugs on Lumbar Spine Volumetric BMD in Men With Glucocorticoid-Induced Osteoporosis

ClinicalTrials.gov study NCT00503399. IPD Sharing: Not stated. Countries: 4. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov36/100

The Effectiveness of IV/PO Acetaminophen in the Perioperative Period in Reducing Opiate Use After Lumbar Spine Fusion

ClinicalTrials.gov study NCT03104816. IPD Sharing: NO. Countries: 1. Publications: 4.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov36/100

Trial of Inserting Prevalence Information Into Lumbar Spine Imaging Reports

ClinicalTrials.gov study NCT02015455. IPD Sharing: Not stated. Countries: 1. Publications: 6.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov36/100

Lumbar Fusion With Nexxt Spine 3D-Printed Titanium Interbody Cages

ClinicalTrials.gov study NCT03647501. IPD Sharing: NO. Countries: 1. Publications: 18.

closedIPD-NOFeb 2026View details →
dryad36/100

Small skeletons show size-specific scaling: an exploration of allometry in the mammalian lumbar spine

Open the record for dataset details and reuse information.

publicMar 2024View details →
zenodo32/100

Triangulated surface geometry of the upper lumbar and lower thoracic spine

<p>Triangulated surface geometry of&nbsp;the upper lumbar and lower thoracic spine,&nbsp;stored as <a href="https://www.loc.gov/preservation/digital/formats/fdd/fdd000504.shtml">STL</a>.</p> <p>The dataset is used to develop the SODALITE virtual clinical trial use-case. It contains a triangulated&nbsp;iso-surface of a part of the lumbar spine. The geometry starts&nbsp;caudal at L2 which is only partially contained and ends&nbsp;cranial at&nbsp;T9 which also only partially contained. The triangulation is extracted from the coresponding <a href="https://doi.org/10.5281/zenodo.3959070">volume dataset</a>.</p> <p>The datasets content is illustrated by the attached png image which shows a shaded surface rendering of the dataset.</p> <pre><code>Format : STL Header : BINARY Byte Order : LittleEndian Grid Type : Unstructured # Nodes : 346051 # Elements : 692244 Data-Type : Uint32, Float32, Uint16 </code></pre> <p>&nbsp;</p>

opencc-by-4.0Jul 2020View details →
zenodo32/100

THE EFFECT OF BODY SIZE AND COMPOSITION ON LUMBAR SPINE TRABECULAR BONE SCORE IN MORPHOLOGICALLY DIVERSE SUBJECTS

<p><strong>Aim:</strong> The trabecular bone score (TBS) is a tool for assessing bone quality and health. Current TBS algorithm corrects for body mass index (BMI), as a proxy of regional tissue thickness. However, this approach fails to consider BMI inaccuracies due to individual differences in body stature, composition and somatotype. This study investigated the relationship between TBS and body size and composition in subjects with a normal BMI, but with large morphological diversity in body fatness and height.</p> <p><strong>Methods:</strong> Young male subjects (n=97; age 17.2&plusmn;1.0 years), including ski jumpers (n=25), volleyball players (n=48) and non-athletes (controls n=39), were recruited. The TBS was determined from L1-L4 dual-energy X-ray absorptiometry (DXA) scans using TBSiNsight software.</p> <p><strong>Results:</strong> TBS correlated negatively with height and tissue thickness in the L1-L4 area in ski jumpers (r= -0.516 and r= -0.529), volleyball players (r= -0.525 and r= -0.436), and the total group (r=-0.559 and r=-0.463), respectively. Multiple regression analyses revealed that height, L1-L4 soft tissue thickness, fat mass and muscle mass were significant determinants of TBS (R<sup>2</sup>= 0.587, p&lt;0.001). L1-L4 soft tissue thickness explained 27% and height 14% of the TBS variance.</p> <p><strong>Conclusion: </strong>The negative association of TBS and both features suggests that a very low L1-L4 tissue thickness may lead to overestimation of the TBS, while tall stature may have the opposite effect. It seems that the utility of the TBS as a skeletal assessment tool in lean and/or tall young male subjects could be improved if tissues thickness in the lumbar spine area and stature instead of BMI were considered in the algorithm.</p>

opencc-by-4.0May 2023View details →

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