Radiol Artif Intell 2021 Mar 23;3(2):e200198. Epub 2020 Dec 23.
Department of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany (J.S., D.B.A., S.N.); Institute of Computer Vision and Imaging, RWTH University Aachen, Pauwelsstrasse 30, 52072 Aachen, Germany (J.S., D.M.); Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (D.T., M.P., F.M., C.K., S.N.); and Faculty of Mathematics and Natural Sciences, Institute of Informatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany (S.C.).
Purpose: To develop and validate a deep learning-based method for automatic quantitative analysis of lower-extremity alignment.
Materials And Methods: In this retrospective study, bilateral long-leg radiographs (LLRs) from 255 patients that were obtained between January and September of 2018 were included. For training data ( = 109), a U-Net convolutional neural network was trained to segment the femur and tibia versus manual segmentation. Read More