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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Detaylı Bibliyografya
Asıl Yazarlar: Christoph Stotter, Thomas Klestil, Christoph Röder, Philippe Reuter, Kenneth Chen, Robert Emprechtinger, Allan Hummer, Christoph Salzlechner, Matthew DiFranco, Stefan Nehrer
Materyal Türü: Makale
Dil:English
Baskı/Yayın Bilgisi: MDPI AG 2023-01-01
Seri Bilgileri:Diagnostics
Konular:
femoroacetabular impingement
hip dysplasia
X-ray
radiographs
artificial intelligence
machine learning
Online Erişim:https://www.mdpi.com/2075-4418/13/3/497

Internet

https://www.mdpi.com/2075-4418/13/3/497

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