This article is sponsored content brought to you by Zoetis.
Artificial intelligence (AI) is changing veterinary practices by providing tools that enhance diagnostic accuracy, improves efficiency, and addresses staffing constraints. AI’s ability to automate routine and time-consuming tasks at the same time as analysing large amounts of data with speed and consistency, helps veterinarians make informed decisions and ultimately deliver better patient care.
Since its introduction in 2020, Vetscan Imagyst®, Zoetis’s first image recognition AI application, has assisted thousands of veterinarians globally. Originally trained to identify common intestinal parasites in dogs and cats, it has evolved to now aid in evaluating blood smears, skin and ear cytology, urine sediment and intestinal parasites in horses. Using AI, Vetscan Imagyst® analyses samples with accuracy comparable to clinical pathologists (or parasitologists for intestinal parasites), providing reliable results to help veterinarians make informed decisions at any time.1-4
AI is a remarkable companion, but without human involvement, it holds no value. The development, training, and performance assessment of Zoetis AI algorithms rely entirely on the expertise of veterinary clinical pathologists. Their skills are crucial in designing, refining, and evaluating Zoetis’ AI systems to ensure they meet the desired standards and objectives.
Furthering Zoetis’ commitment to bringing meaningful value to practices, patients, and pet owners, Zoetis now introduces Vetscan OptiCell™. Vetscan OptiCell™ is a cartridge-based instrument that performs complete blood counts (CBC), including reticulocyte, using machine vision with AI. Vetscan OptiCell™ simplifies point-of-care diagnostics, delivering quick and reliable results with minimal sample preparation and maintenance. For the first time, veterinarians can use AI image recognition to characterise and count hundreds of thousands blood cells in minutes, which leads to highly accurate and fast haematology insights5.
Paired with Vetscan Imagyst® AI Blood Smear, Vetscan OptiCell™ delivers a comprehensive haematology picture, including both quantitative and qualitative assessments of blood cells. Additionally, recognising that AI does not cover every aspect of blood film evaluation, Zoetis provides the option for a clinical pathologist to review the digital scan, ensuring thorough case diagnosis, while treatment and management can be discussed with a specialist consultation as a complimentary service.
Dr Eric Morissette, DVM, DACVP (Clinical), Senior Manager of Zoetis’ AI Portfolio Development Platforms, comments that by embracing the latest diagnostic technology, clinics can continue to provide the best possible care – even when challenged with workforce complexities and increased demand. Specifically, when looking to AI, we can expect to see its integration widen across various diagnostic tools, treatment planning systems, and routine veterinary procedures, enhancing the efficiency and quality of care.
Discover how Vetscan OptiCell™ and Vetscan Imagyst® can transform your in-clinic diagnostic experience. Visit the Zoetis stand at the National AVA Conference, International Convention Centre, Sydney, 13-15th May 2025.
References:
1. Morissette, E., et al., Point-of-care platform integrated with deep-learning, convolutional neural network algorithms effectively evaluates canine and feline peripheral blood smears. Am J Vet Res, 2024: p. 1-11.
2. Mary Lewis, M., DVM, Diplomate ACVP (Clinical), et al., Deep Learning Artificial Intelligence (AI) for Rapid and Reliable Evaluation of Canine/Feline Urine Sediment Samples in American Collge of Veterinary Internal Medicine. 2024: Minneapolis, Minnesota.
3. Kristin Owens, D., Diplomate ACVP (Clinical), et al., Deep Learning Artificial Intelligence (AI) Based Approach for Efficient Evaluation of Canine/Feline Dermatologic Cytology Samples in American College of Veterinary Internal Medicine. 2024: Minneapolis, Minnesota.
4. Nagamori, Y., et al., Evaluation of the VETSCAN IMAGYST: an in-clinic canine and feline fecal parasite detection system integrated with a deep learning algorithm. Parasit Vectors, 2020. 13(1): p. 346.
5. Data on file. Study No. DHXMZ-US-24-235, 2024, Zoetis Inc.
