Predicting Therapeutic Response to Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) in Dogs Using Deep Neural Networks

Background and Objective: Prescribing Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) is a cornerstone of veterinary orthopedic treatment. However, individual variations in treatment response, side effects, and variable efficacy pose significant challenges in veterinary pharmacology. This study aims to develop a personalized prediction model based on artificial intelligence to forecast the clinical response to NSAIDs in dogs. Materials and Methods: Clinical data will be collected from the electronic records of ۵۰۰ dogs treated with the most common NSAIDs (including carprofen, meloxicam, and firocoxib). Input features will include breed, weight, age, history of renal and hepatic diseases, concurrent medications, and biochemical laboratory results. The model's output will be defined as the treatment response (successful, unsuccessful, or accompanied by adverse effects). A Deep Neural Network model will be implemented using the PyTorch framework and trained using ۵-fold cross-validation. Expected Results: The final model is expected to predict treatment response with an accuracy exceeding ۸۷%. Additionally, the model will be able to identify and rank the most important features influencing treatment success or adverse effects (such as age and kidney function). This system will serve as a decision-support tool for selecting the optimal drug and dosage for each specific patient. Conclusion: This research will demonstrate that artificial intelligence has the potential to transform veterinary pharmacology from a "one-size-fits-all" approach to a "personalized treatment" paradigm. Such systems can maximize treatment efficacy and minimize drug-related adverse effects.

کلیدواژه ها:

Artificial intelligence ، Non-steroidal anti-inflammatory drugs (NSAIDs) ، Personalized medicine ، Treatment response prediction ، Deep neural network