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Geometric Transformation Algorithm for Acetabular Cup Orientation: Converting 2D Radiographic Projections to 3D

Gökmen Aktas1, Lukas Hensler2, Sabine Dippel2

  • 1Department of Trauma Surgery, Hannover Medical School, Hannover, Germany; aktas.goekmen@mh-hannover.de.

In Vivo (Athens, Greece)
|April 28, 2026
PubMed
Summary
This summary is machine-generated.

A new software tool accurately measures hip replacement cup positioning using computer vision on X-rays. This automated method shows excellent agreement with standard techniques, improving surgical outcomes.

Keywords:
Artificial intelligencearthroplastycomputer visionhipsurgerytraumatology

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Area of Science:

  • Orthopedic surgery
  • Medical imaging
  • Computer vision

Background:

  • Total hip arthroplasties are common, but inadequate acetabular cup positioning causes complications.
  • Accurate measurement of cup inclination and anteversion is crucial for optimal surgical outcomes.
  • Existing measurement systems often require extra hardware, specialized training, or lack integration.

Purpose of the Study:

  • To develop and validate software for automated measurement of acetabular cup positioning.
  • To compare the software's performance against standard computer-aided design (CAD) measurements.
  • To enhance the accuracy and efficiency of postoperative assessments in hip replacements.

Main Methods:

  • Developed Python-based software using computer vision for edge detection and ellipse fitting.
  • Analyzed standard anteroposterior (AP) X-ray images of a pelvic phantom with a conventional acetabular cup.
  • Validated the software against MediCAD (CAD software) measurements, using intraclass correlation coefficient (ICC).

Main Results:

  • Analyzed 140 AP X-ray images.
  • Inclination averaged 50.69°±15.86° (software) vs. 49.40°±15.24° (CAD).
  • Anteversion averaged 14.36°±9.08° (software) vs. 14.75°±8.88° (CAD).
  • Achieved excellent agreement: ICC=0.994 for inclination and ICC=0.992 for anteversion (p<0.001).

Conclusions:

  • The computer-assisted measurement technique shows excellent concordance with standard methods.
  • The software offers workflow advantages for automated spatial positioning recognition.
  • Future work includes enhancing automation, validating on diverse populations/implants, and comparing with 3D CT measurements.