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Patellar tilt calculation utilizing artificial intelligence on CT knee imaging.

Johannes Sieberer1, Albert Rancu2, Nancy Park2

  • 1Yale School of Medicine - Orthopaedics & Rehabilitation, 47 College Street, New Haven, CT, USA; Yale School of Engineering and Applied Science - Department of Mechanical Engineering and Material Science, 17 Hillhouse, New Haven, CT, USA.

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Summary
This summary is machine-generated.

This study automated patellar tilt measurement in 3D using AI, finding it comparable to manual methods for diagnosing patellar instability. The AI approach offers a faster, more accurate way to assess this key surgical decision-making metric.

Keywords:
Artificial IntelligencePatellar tiltThree-dimensional (3D)

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Area of Science:

  • Orthopedic surgery
  • Medical imaging
  • Biomechanical analysis

Background:

  • Three-dimensional (3D) imaging aids patellar instability diagnosis but manual measurements are time-consuming and error-prone.
  • Current 2D measurements on 3D objects limit accuracy and efficiency.
  • Automating 3D patellar tilt measurement is crucial for improved diagnostic workflows.

Purpose of the Study:

  • To measure patellar tilt in 3D using an automated approach.
  • To evaluate the accuracy and reliability of AI-driven landmark placement for patellar tilt calculation.
  • To compare AI-based measurements with traditional manual methods.

Main Methods:

  • CT scans from 30 patients with patellar instability and 30 controls were analyzed.
  • Patellar tilt was measured using established manual methods and AI-assisted 3D landmark placement.
  • Interclass correlation coefficients and statistical tests (Kruskal-Wallis, Mann-Whitney U) were used for comparison.

Main Results:

  • No significant differences were found between the manual and AI-based patellar tilt measurement methods.
  • High interclass correlation coefficients (0.86-0.90) indicate strong agreement between measurement techniques.
  • Significant differences in patellar tilt were observed between patients and controls across all methods (p < 0.001).

Conclusions:

  • A 3D automated approach using AI for patellar tilt calculation is comparable to traditional manual measurements.
  • Commercial AI software can accurately identify anatomical landmarks for patellar tilt assessment.
  • This automated method offers a potential pathway to streamline surgical decision-making metrics in patellar instability.