Deep Learning for Fully Automated Radiographic Measurements of the Pelvis and Hip

Deep Learning for Fully Automated Radiographic Measurements of the Pelvis and Hip

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 m...

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Bibliographic Details
Main Authors:	Christoph Stotter, Thomas Klestil, Christoph Röder, Philippe Reuter, Kenneth Chen, Robert Emprechtinger, Allan Hummer, Christoph Salzlechner, Matthew DiFranco, Stefan Nehrer
Format:	Article
Language:	English
Published:	MDPI AG 2023-01-01
Series:	Diagnostics
Subjects:	femoroacetabular impingement hip dysplasia X-ray radiographs artificial intelligence machine learning
Online Access:	https://www.mdpi.com/2075-4418/13/3/497

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