A Computer-Aided Approach to Canine Hip Dysplasia Assessment: Measuring Femoral Head–Acetabulum Distance with Deep Learning

Canine hip dysplasia (CHD) screening relies on radiographic assessment, but traditional scoring methods often lack consistency due to inter-rater variability. This study presents an AI-driven system for automated measurement of the femoral head center to dorsal acetabular edge (FHC/DAE) distance, a...

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Autores principales:	Pedro Franco-Gonçalo, Pedro Leite, Sofia Alves-Pimenta, Bruno Colaço, Lio Gonçalves, Vítor Filipe, Fintan McEvoy, Manuel Ferreira, Mário Ginja
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	MDPI AG 2025-05-01
Colección:	Applied Sciences
Materias:	canine hip dysplasia screening artificial intelligence computer vision regression network semantic segmentation
Acceso en línea:	https://www.mdpi.com/2076-3417/15/9/5087
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