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Related Concept Videos

Assessment of the Abdomen II: Percussion01:18

Assessment of the Abdomen II: Percussion

Percussion is a fundamental technique used to assess the liver, spleen, and abdominal organs by tapping the abdomen and interpreting the resulting sounds. This method helps identify fluid, distention, and masses through variations in sound, such as the high-pitched tympany of air-filled areas and the dullness of solid masses. Understanding how to percuss these organs provides valuable information for healthcare professionals in diagnosing conditions early.
Percussion
Percussion is an essential...

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Related Experiment Video

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Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts
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A Deep Learning Tool for Hip Minimum Joint Space Width Calculation on Antero-posterior Pelvis Radiographs.

Lainey G Bukowiec1, Anish Kanabar2, Miguel M Girod2

  • 1Mayo Clinic, Department of Orthopedic Surgery, Rochester, Minnesota; Orthopedic Surgery Artificial Intelligence Lab, Mayo Clinic, Rochester, Minnesota.

The Journal of Arthroplasty
|August 23, 2025
PubMed
Summary

An automated algorithm was developed to measure hip joint space width on X-rays. This tool accurately quantifies osteoarthritis progression, aiding patient evaluation and research.

Keywords:
artificial intelligencedeep learninghip preservationjoint space widthmachine learningosteoarthritis

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Minimum joint space width (mJSW) is a key quantitative metric for hip osteoarthritis progression.
  • Continuous mJSW measurement offers advantages over traditional categorical classifications.

Purpose of the Study:

  • To develop an automated algorithm for measuring mJSW in native hips.
  • To enable accurate mJSW assessment on antero-posterior (AP) pelvis radiographs.

Main Methods:

  • An end-to-end algorithm combining deep learning segmentation and computer vision was created.
  • A dataset of 300 radiographs was annotated for model training and validation.
  • External validation was performed on 75 images from the Osteoarthritis Initiative (OAI).

Main Results:

  • The algorithm achieved a mean absolute error of 0.87 ± 1.05 mm compared to human measurements.
  • 70% of algorithm measurements were within 1 mm of human measurements.
  • External validation on OAI data showed a mean absolute error of 0.86 ± 0.69 mm.

Conclusions:

  • An automated model for mJSW measurement on AP pelvis radiographs has been successfully developed and validated.
  • The algorithm demonstrates sub-millimeter accuracy, potentially improving longitudinal patient monitoring.
  • This tool can streamline clinical research on the natural history of hip joint osteoarthritis.