Artificial Intelligence-Driven Predictive Modeling for Canine Pain Assessment and Personalized Analgesic Recommendations

Accurately assessing and managing pain in dogs is one of the most challenging aspects of veterinary medicine because animals cannot verbally communicate their discomfort. Current methods rely heavily on behavioral observations and standardized scoring tools, which are often subjective and prone to inconsistencies. This project aims to address these limitations by developing an AI-powered predictive modeling framework that leverages publicly available veterinary datasets to improve pain detection and optimize treatment strategies for companion animals. Through advanced machine learning techniques, the project will analyze clinical records, treatment histories, and behavioral data from open-access sources such as VetCompass, CBPI, and C-BARQ to create predictive models capable of assessing pain severity, forecasting recovery patterns, and recommending personalized analgesic treatments. The proposed framework will be validated against widely used pain assessment tools, ensuring higher accuracy and reliability in veterinary decision-making. This research will enhance animal welfare, empower veterinarians with data-driven decision-support tools, and promote innovations in veterinary informatics

Faculty Supervisor:

Rasha Kashef

Student:

Partner:

King Salman International University

Discipline:

Computer science

Sector:

Artificial Intelligence; Health and Related Sciences and Technology; Technology

University:

Toronto Metropolitan University

Program: