Open AccessAssessment of knee pain from MR imaging using a convolutional Siamese network
Author(s) -
Gary Han Chang,
David T. Felson,
Shangran Qiu,
Ali Guermazi,
Terence D. Capellini,
Vijaya B. Kolachalama
Publication year - 2020
Publication title -
european radiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.606
H-Index - 149
eISSN - 1432-1084
pISSN - 0938-7994
DOI - 10.1007/s00330-020-06658-3
Subject(s) - medicine , knee pain , osteoarthritis , magnetic resonance imaging , sagittal plane , neuroradiology , deep learning , convolutional neural network , womac , radiology , knee joint , physical therapy , physical medicine and rehabilitation , artificial intelligence , surgery , computer science , neurology , pathology , alternative medicine , psychiatry
It remains difficult to characterize the source of pain in knee joints either using radiographs or magnetic resonance imaging (MRI). We sought to determine if advanced machine learning methods such as deep neural networks could distinguish knees with pain from those without it and identify the structural features that are associated with knee pain.