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Related Concept Videos

Anatomical Positions01:11

Anatomical Positions

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In anatomy, several standard anatomical positions are used as references for describing the position and orientation of different body parts. These positions help provide a common frame of reference when discussing anatomical structures. The anatomical position is the standard reference point for describing the body's position and orientation. In this position:
The body is upright, facing forward, and standing erect.
The feet are parallel and flat on the floor.
The arms are hanging by the...
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Correction: Komatsu et al. Three-Dimensional Visualization and Detection of the Pulmonary Venous-Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening. <i>Bioengineering</i> 2026, <i>13</i>, 100.

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Related Experiment Video

Updated: Jul 12, 2025

Computerized Dynamic Posturography for Postural Control Assessment in Patients with Intermittent Claudication
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PosturAll: A Posture Assessment Software for Children.

Ana Beatriz Neves1, Rodrigo Martins2, Nuno Matela1

  • 1Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal.

Bioengineering (Basel, Switzerland)
|October 28, 2023
PubMed
Summary
This summary is machine-generated.

Early medical screenings can identify musculoskeletal disorder risks. This study improved software for analyzing patient images to detect postural problems, aiding in early intervention and minimizing conditions like low back pain.

Keywords:
anatomical metric analysiscomputer visionmachine learningmusculoskeletal disorderspostural assessment

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging Analysis
  • Musculoskeletal Health

Background:

  • Musculoskeletal disorders (MSDs) like low back pain, neck pain, and scoliosis often stem from early-life risk factors.
  • Medical screenings during childhood may reduce the prevalence of these conditions.
  • Current diagnostic methods for postural issues can be enhanced with improved imaging software.

Purpose of the Study:

  • To refine image processing software for identifying anatomical markers and body metrics for postural assessment.
  • To develop risk indicators for potential musculoskeletal problems using improved software analysis.
  • To validate the software's efficacy by comparing its results with established clinical tools.

Main Methods:

  • Selected specific anatomical markers and body metrics for postural assessment.
  • Evaluated software's marker detection and optimization capabilities using green markers (20 mm diameter).
  • Acquired population data for validation against clinical software, employing blob detection and analyzing angle parameter influence.

Main Results:

  • Optimization tests indicated that angle parameters were most affected.
  • Postural analysis results obtained from the software were statistically equivalent to clinical standards.
  • Classifiers demonstrated better performance using binary classification with train/test split validation on the study population (16 healthy, 25 mild, 16 moderate-to-severe evidence).

Conclusions:

  • The improved software effectively processes patient images to provide risk indicators for musculoskeletal disorders.
  • The software's postural analysis is validated and statistically comparable to clinical assessments.
  • Binary classification with train/test split validation is a promising method for analyzing postural data from the developed software.