Hip dysplasia (HD) in dogs is a hereditary disease with a polygenic, additive nature and high prevalence in specific breeds. Diagnosis of HD involves radiographic examination to determine if dogs are affected. To streamline and automate this process, a technological platform is being developed, which will use computer vision techniques and machine learning models to support clinical decisions regarding radiographic reading, HD classification, and certification. The system aims to assess image quality and classify radiographs for both young and adult dogs.
Reference: POCI-01-0247-FEDER-046914
Funding source: ANI (National Agency for Innovation)
Financing: 538,010.61€
Financing for CECAV/UTAD: 173,558.50€
Leading institution: NEADVANCE – MACHINE VISION, S.A
Partners: HOSPITAL VETERINÁRIO MONTENEGRO, UTAD
