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Validation of a machine learning technique for segmentation and pose estimation in single plane fluoroscopy.

Jordan S Broberg^1,2,3, Joanna Chen¹, Andrew Jensen⁴

¹Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Canada.

Journal of Orthopaedic Research : Official Publication of the Orthopaedic Research Society

|January 24, 2023

Summary

An automated single-plane fluoroscopic method using machine learning accurately measures total knee replacement (TKR) kinematics. This technique offers a faster, simpler alternative to traditional methods for clinical assessment.

Keywords:

kinematics machine learning single plane fluoroscopy total knee replacement validation

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Area of Science:

Orthopedic Surgery
Biomedical Engineering
Medical Imaging

Background:

Total knee replacement (TKR) kinematics are crucial for evaluating surgical success.
Current kinematic measurement methods are time-consuming and labor-intensive, limiting clinical application.

Purpose of the Study:

To validate a novel, automated single-plane fluoroscopic technique for measuring TKR kinematics.
To compare the accuracy and reproducibility of the automated method against biplane radiostereometric analysis (RSA).

Main Methods:

Utilized machine learning with two neural networks for automated segmentation and pose estimation of TKR components.
Simulated single-plane fluoroscopy using lateral images from biplane RSA data (n=113 image pairs from 24 knees).
Compared automated single-plane kinematics with manual biplane RSA using root-mean-square error and Bland-Altman analysis.

Main Results:

Achieved root-mean-square errors of 0.8 mm (AP), 0.5 mm (SI), 2.6 mm (ML) for translations and 1.0° (FE), 1.2° (AA), 1.7° (IE) for rotations.
Demonstrated high reproducibility with submillimeter errors for in-plane translations and <2° for all rotations between observers.
The automated technique showed strong agreement with the gold standard biplane RSA.

Conclusions:

The automated single-plane fluoroscopic technique provides accurate and reproducible TKR kinematic measurements.
This method significantly simplifies and speeds up TKR kinematic analysis, making it more accessible for clinical practice and research.
The findings support the integration of this automated technique into routine clinical workflows for improved TKR assessment.