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

Abstract

AbstractKinematics of total knee replacements (TKR) play an important role in assessing the success of a procedure and would be a valuable addition to clinical practice; however, measuring TKR kinematics is time consuming and labour intensive. Recently, an automatic single‐plane fluoroscopic method utilizing machine learning has been developed to facilitate a quick and simple process for measuring TKR kinematics. This study aimed to validate the new automatic single‐plane technique using biplanar radiostereometric analysis (RSA) as the gold standard. Twenty‐four knees were imaged at various angles of flexion in a dedicated RSA lab and 113 image pairs were obtained. Only the lateral RSA images were used for the automatic single‐plane technique to simulate single‐plane fluoroscopy. Two networks helped automate the kinematics measurement process, one segmented implant components and the other generated an initial pose estimate for the optimization algorithm. Kinematics obtained via the automatic single plane and manual biplane techniques were compared using root‐mean‐square error and Bland–Altman plots. Two observers measured the kinematics using the automated technique and results were compared with assess reproducibility. Root‐mean‐square errors were 0.8 mm for anterior–posterior translation, 0.5 mm for superior–inferior translation, 2.6 mm for medial–lateral translation, 1.0° for flexion–extension, 1.2° for abduction–adduction, and 1.7° for internal–external rotation. Reproducibility, reported as root‐mean‐square errors between operator measurements, was submillimeter for in‐plane translations and below 2° for all rotations. Clinical Significance: The advantages of the automated single plane technique should aid in the kinematic measurement process and help researchers and clinicians perform TKR kinematic analyses.