Deep Learning for Fully Automated Radiographic Measurements of the Pelvis and Hip.
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ABSTRACT:
SUBMITTER: Stotter C
PROVIDER: S-EPMC9914204 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
Publications
Deep Learning for Fully Automated Radiographic Measurements of the Pelvis and Hip.
Stotter Christoph C Klestil Thomas T Röder Christoph C Reuter Philippe P Chen Kenneth K Emprechtinger Robert R Hummer Allan A Salzlechner Christoph C DiFranco Matthew M Nehrer Stefan S
Diagnostics (Basel, Switzerland) 20230129 3
The morphometry of the hip and pelvis can be evaluated in native radiographs. Artificial-intelligence-assisted analyses provide objective, accurate, and reproducible results. This study investigates the performance of an artificial intelligence (AI)-based software using deep learning algorithms to measure radiological parameters that identify femoroacetabular impingement and hip dysplasia. Sixty-two radiographs (124 hips) were manually evaluated by three observers and fully automated analyses we ...[more]
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