Development of a neural network algorithm to quantify chronic osteoarthritic pain in rats

The progression of pain research has been limited because of the overreliance on nociceptive assessment tools. These nociceptive assessment tools only assess the sensory component of pain and neglect its emotional component. It has been suggested that behavioural tools, such as the grimace scales, can assess the emotional component of pain. The use of various molecular markers are also promising new avenues to assess pain in various experimental models. The concurrent use of all three types of assessment methods will build a more accurate and complete picture of pain. Different types of pain assessment tools will be used to assess for the presence or absence of pain in an osteoarthritis pain model in rats. Osteoarthritic pain is the greatest cause of morbidity, and chronic pain in Western countries results in enormous financial losses from reduced productivity. It is also common in companion animals. The data gathered will be used to establish a neural network algorithm to build a sensitive and specific pain quantification profile. This will be compared to previously established (invasive) standards that have been validated by our laboratory. Once the algorithm has been established, it will also be tested in dogs.

Faculty Supervisor:

Éric Troncy

Student:

VIVIAN SZE-YUEN LEUNG

Partner:

ArthroLab Inc

Discipline:

Animal science

Sector:

Life sciences

University:

Université de Montréal