As veterinary practices continue to embrace digital transformation, the integration of Artificial Intelligence (AI) into diagnostic workflows is becoming a game-changer. That’s why VetIT has partnered with Vetology AI, a pioneer in veterinary AI, to bring advanced radiology support tools to practices across the UK. Through this collaboration, veterinary practices in the UK can now access Vetology AI, which is a powerful platform that combines automated radiology reports with the option for board-certified teleradiology service.
In the article below, we take a closer look at one of the core components of this technology: AI classifiers. So, over to Vetology…
Understanding Classifiers in Veterinary AI Imaging: How They Work and Their Role in Clinical Practice
Artificial Intelligence (AI) has become an integral tool in veterinary imaging, enabling faster and more consistent analysis of radiographs. At the heart of this technology lies the concept of classifiers – AI models trained to recognise specific patterns or anomalies in imaging studies. Understanding how classifiers work, their limitations, and how they complement the veterinary workflow is key to appreciating the value AI imaging support brings to patient care.
What Are Classifiers?
Classifiers are AI algorithms designed to identify specific features or abnormalities in diagnostic images. For example, a classifier might detect signs of pulmonary patterns, foreign bodies, or changes in organ size. However, it is essential to understand that Vetology’s classifiers generate screening results, not diagnostic conclusions. Their purpose is to aid veterinarians by highlighting potential areas of concern and guiding further investigation or treatment planning.
How Are Classifiers Trained?
The effectiveness of a classifier depends entirely on the quality and breadth of its training. In Vetology’s approach, classifiers are trained using what is called a golden set of radiographs. A golden set consists of high-quality imaging studies that have been meticulously reviewed and confirmed by radiologists to feature the specific condition of interest. This ensures that the AI learns from confirmed, representative examples.
For instance, in developing a classifier to detect hepatomegaly in feline patients, we curate a golden set that includes radiographs of cats with confirmed hepatomegaly. This focused training enables the AI to recognise the subtle features specific to the condition in cats, such as changes in liver size or contour. Conversely, for conditions such as fractures, we use images from both dogs and cats to develop a single, cross-species classifier.
All Classifiers are Not Created Equal
Classifiers are shaped by their training data and the methods used to create them. Differences in image quality, diversity of cases, and even the radiographic positioning in the golden set can influence the classifier’s performance. Consequently, the robustness of a classifier depends on meticulous preparation during its development phase. This is why Vetology invests significant time and resources in ensuring that our classifiers are trained using peer-reviewed datasets and rigorous validation protocols.
The Importance of Image Quality
Vetology’s AI platform can only analyse what it “sees.” This means that the quality of submitted radiographs plays a critical role in the accuracy and reliability of AI-generated reports. Well-positioned, properly exposed, and collimated images yield the best results. For example, a thoracic radiograph with proper positioning and exposure settings allows the AI to accurately assess structures such as the heart, lungs, and thoracic wall. Conversely, poorly positioned images, fewer than two views, or if organs are obscured or stretched, suboptimal performance and inconclusive results can result.
How AI Classifiers Work
When a series of images featuring a lateral and a VD view are uploaded and transferred to our servers, a series of classifiers are initiated. These initial classifiers confirm the patient species, assess for proper positioning (e.g., rotation), and determine which classifiers to initiate for the evaluation. Critically, what it lacks is access to clinical context such as the patient’s signalment, history, or laboratory results. This means that while the AI can identify radiographic features consistent with certain conditions, it does not “diagnose” the patient. Instead, its role is to provide a detailed screening report that supports the veterinarian’s interpretation and decision-making process.
For example, if the submitted lateral and VD images are collimated down to the abdomen, classifiers relevant to abdominal structures, such as the liver, spleen, or gastrointestinal tract will run. The classifiers can identify an enlarged liver, but it doesn’t correlate this with the clinically relevant bloodwork or the owner’s observations that the patient has been deteriorating for a week, isn’t eating, and doesn’t show any interest in his or her usual activities. The AI report will present its findings, conclusions, and recommendations; in our view, interpreting these results and developing a treatment plan should firmly remain in the capable hands and medical expertise of a practitioner.
The AI report isn’t intended to replace the five senses or skill of a veterinarian any more than x-ray equipment replaces your eyes. The AI report is a clinical support tool, it’s not a board-certified radiologist, though Vetology’s AI was born and raised by radiologists.
Enhancing the Veterinary Workflow
Classifiers serve as a powerful tool in the veterinary diagnostic workflow. Consider them as an answer sheet to a multiple-choice test. While the veterinarian remains the ultimate decision-maker, the AI report provides a second opinion, which can:
- Confirm Your Diagnoses: AI findings can validate the veterinarian’s own radiographic interpretations.
- Guide Your Next Steps: Screening results may suggest further imaging, laboratory tests, or therapeutic interventions.
- Increase the Number of Tools at your Disposal: By automating the initial screening process, Vetology’s AI radiology report supports the patient journey and backs the veterinarian’s clinical judgment and patient care.
Vetology: Innovating Veterinary AI
At Vetology, we are proud to be at the forefront of veterinary AI innovation. Our AI platform leverages a combination of expertly trained classifiers and advanced machine learning techniques to deliver reliable and actionable insights. Recently, Vetology achieved a significant milestone with the approval of a patent for our proprietary AI technology. This recognition underscores our commitment to advancing veterinary medicine through cutting-edge solutions that prioritize patient outcomes and clinical accuracy.
Why Classifiers Matter
Classifiers are more than just algorithms; they are the culmination of meticulous training, validation, and refinement. By leveraging high-quality data, targeted training methods, and a species-specific approach, Vetology ensures that our classifiers deliver meaningful and reliable results. However, it is important to remember that the AI radiology report is a support tool, not a replacement for veterinary expertise. The best outcomes are achieved when AI and human intelligence work together, combining the precision of machine learning with the nuanced understanding of veterinary professionals.
Conclusion
In the rapidly evolving field of veterinary imaging, classifiers represent a significant step forward in supporting diagnostic accuracy and efficiency. By understanding how classifiers work, their strengths, and their limitations, veterinarians can make informed decisions about incorporating AI into their practice. At Vetology, our mission is to aid veterinary teams with tools and platform support that assist human skills and patient care. Whether you are confirming a diagnosis or planning the next steps in a diagnostic pathway, Vetology’s AI radiology report, board-certified teleradiology team, and customer support team are dedicated to ensuring your success.
Ready to See the Difference AI Can Make?
As veterinary imaging continues to evolve, integrating AI into your diagnostic process isn’t just a technological upgrade, it’s a clinical advantage. If you’re ready to see how Vetology AI can support your team with faster insights, improved workflow, and greater diagnostic confidence, we invite you to explore the platform in more detail. Visit the dedicated VetIT Vetology AI page on VetIT website to learn more and book a demo to experience firsthand how AI-powered radiology can elevate your practice.
