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Teaching the computer to recognise suboptimal skeletal growth/osteochondrosis, to use this in selection for sustainable legs


The skeleton in the spine and limbs grows by endochondral ossification. The most important disease that affects this process is osteochondrosis, which can be heritably predisposed. Osteochondrosis in joints can give loose fragments (osteochondrosis dissecans); osteochondrosis in growth plates can give angular limb deformities, while osteochondrosis in the spine can give hunchback and other deformities. Loose fragments can progress to painful osteoarthritis. Angled limbs or spine can also lead to abnormal loading and predispose for osteoarthritis, or claw problems that lead to early loss of breeding sows. In sum, there are few other diseases that influence the health, welfare, performance and longevity of pigs to the same extent as osteochondrosis. It is therefore extremely important to prevent this disease through selective breeding. All Norwegian potential breeding boars undergo computed tomographic (CT) scanning for automated quantification of lean meat and fat percentage for selection. Osteochondrosis is also manually evaluated in eight different places, something which takes a relatively long time per pig. Meat and fat percentage can be collected based on grey values in the CT scan, but for osteochondrosis it is unfortunately not that simple. We therefore plan to use machine learning, or artificial intelligence, because the technique can solve more complex tasks. In this project, machine learning will consist of a veterinarian marking osteochondrosis lesions in a number of CT scans. Thereafter, the computer will compare the images with and without markings and use so-called neural networks to teach itself to make the same markings as the veterinarian in unlimited numbers of CT scans. According to the aims, the computer will first be taught to recognise osteochondrosis in joints, then in growth plates, and finally in the spine, before the three machine learning protocols are joined together to make one model for automated whole-body scoring of osteochondrosis.

Project leader: Kristin Olstad

Started: 2019

Ends: 2022

Category: Universiteter

Sector: UoH-sektor

Budget: 7202000

Institution: NMBU Veterinærhøgskolen

Address: Ås