English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Dynamic radiographic measurement of 3D TKA kinematics has provided importantinformation for implant design and surgical technique for over 30 years.However, current methods of measuring TKA kinematics are too cumbersome ortime-consuming for practical clinical application. Even state-of-the-arttechniques require human-supervised initialization or human supervisionthroughout the entire optimization process. Elimination of human supervisioncould potentially bring this technology into clinical practicality. Therefore,we propose a fully autonomous pipeline for quantifying TKA kinematics fromsingle-plane imaging. First, a convolutional neural network segments thefemoral and tibial implants from the image. Second, segmented images arecompared to Normalized Fourier Descriptor shape libraries for initial poseestimates. Lastly, a Lipschitzian optimization routine minimizes the differencebetween the segmented image and the projected implant. This technique reliablyreproduces human-supervised kinematics measurements from internal datasets andexternal validation studies, with RMS differences of less than 0.7mm and4{\deg} for internal studies and 0.8mm and 1.7{\deg} for external validationstudies. This performance indicates that it will soon be practical to performthese measurements in a clinical setting.

Joint Track Machine Learning: An autonomous method for measuring 6DOF TKA kinematics from single-plane x-ray images