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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Leaky Scanning02:28

Leaky Scanning

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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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Machines01:19

Machines

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
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Machines: Problem Solving II01:30

Machines: Problem Solving II

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Machines: Problem Solving I01:22

Machines: Problem Solving I

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Open-Source Machine Learning Computed Tomography Scan Segmentation for Spine Osteoporosis Diagnostics.

Akshay Sankar1, Michael R Kann1, Samuel Adida1

  • 1University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.

Neurosurgery
|February 5, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning analysis of computed tomography (CT) Hounsfield units (HUs) shows strong correlation with bone mineral density (BMD) measurements from dual-energy x-ray absorptiometry (DXA). This automated CT method offers a consistent and efficient approach for osteoporosis risk stratification.

Keywords:
DXAHounsfield unitsMachine learningOsteoporosisSegmentation

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Area of Science:

  • Radiology
  • Medical Imaging
  • Osteoporosis Research

Background:

  • Dual-energy x-ray absorptiometry (DXA) is the standard for bone mineral density (BMD) assessment but has limitations.
  • Hounsfield units (HUs) from CT scans offer a potential alternative for osteoporosis risk stratification.
  • Machine learning (ML) can segment CT anatomy and derive BMD metrics.

Purpose of the Study:

  • To evaluate an automated CT segmentation platform.
  • To investigate the relationship between vertebral HUs and DXA-based BMD.
  • To assess CT-derived HU density for osteoporosis risk stratification.

Main Methods:

  • Retrospective analysis of 229 patients with concurrent lumbar CT and DXA scans.
  • Utilized the TotalSegmentator ML model for lumbar spine segmentation.
  • Computed vertebral body, trabecular, and cortical bone HU density and compared with DXA T-scores.

Main Results:

  • Mean HUs from L1-L5 correlated significantly with DXA T-scores for femoral neck, lumbar spine, and hip.
  • Healthy individuals exhibited higher vertebral HUs compared to osteopenic individuals.
  • CT-derived HUs were predictive of low BMD and osteoporosis, with identified optimal threshold values.

Conclusions:

  • ML-driven CT segmentation provides a robust and consistent assessment of vertebral bone density.
  • Vertebral Hounsfield units correlate well with DXA BMD measurements.
  • Automated CT analysis offers an efficient method for osteoporosis risk stratification.