The Ability of Deep Learning Models to Identify Total Hip and Knee Arthroplasty Implant Design From Plain Radiographs.

Christian KlemtAkachimere Cosmas UzosikeWayne Brian Cohen-LevyMichael Joseph HarveyMurad Abdullah SubihWayne Brian Cohen-Levy

Published in: The Journal of the American Academy of Orthopaedic Surgeons (2022)

This study developed and validated a convolutional neural network deep learning model for the identification of hip and knee total joint arthroplasty designs from plain radiographs. The study findings demonstrate excellent accuracy of the deep learning model for the identification of 24 THA and 14 TKA designs, illustrating the great potential of the deep learning model to assist in preoperative surgical planning of failed arthroplasty patients.

Keyphrases

deep learning
convolutional neural network
artificial intelligence
total hip
total knee arthroplasty
machine learning
end stage renal disease
chronic kidney disease
ejection fraction
newly diagnosed
prognostic factors
patients undergoing