As the emphasis of veterinary diagnostics shifts away from curative medicine and towards prevention, early detection, and management of diseases, it has become clear that the standard diagnostic tools used in veterinary care have yet to embrace today’s technological advancements. The typical methods of diagnosis lack the innovation that exists in the human medical field, and it is time to bring these technological and scientific advancements to the veterinary sphere.
Vets today fall into two primary classes: those treating farm animals in the field, and those treating companion animals in a clinic. Each of these requires its own set of expertise and tools, and each face unique diagnostic challenges.
The methods of diagnosis differ greatly in these two groups. Vets treating livestock and other farm animals generally draw blood samples in the field and send them to a centralized laboratory for analysis and await results. They often perform an additional on-site blood smear, which is also sent to the lab, although these tests are often of low-quality because of the conditions in which they are performed.
Smaller vet clinics treating companion animals usually utilize in-house hematology analyzers, which are smaller versions of their lab-grade counterparts, to detect pathogens and cell abnormalities. These analyzers require maintenance, calibration, and may be financially draining for low-volume clinics.
The use of new technologies, such as point-of-care devices powered by AI and machine vision algorithms, offer solutions to the most pressing problems in veterinary diagnostics, whether in the field or in a clinic. By leveraging these new methods, diagnostics can become more efficient, accurate, and cost-effective.
Lack of Efficiency
Technological advancements and the search for greater efficiency led diagnosis and detection of pathogens to take place in large centralized labs. These labs have large hematology analyzers that can work through high volumes of samples each day. Vets practicing in the field generally take samples from the animals and send them to these labs for testing, which take time to process and may delay diagnosis. The earlier a pathogen is identified and diagnosed, the better the outcome will be, but often treatment is delayed due to a long turnaround time between the initial sending of a sample from the field and the confirmation of test results.
For clinics, the process of sending a sample to a centralized lab and waiting for results takes valuable time away from diagnosis and treatment of the companion animal. Thus, most clinics have in-house analyzers in which they can perform the tests. These analyzers, while providing quicker diagnostic information than centralized labs, require routine maintenance, calibration, and expertise to operate, and often do not deliver the most accurate results as they employ a relatively basic technology that is limited in its ability to discern different pathologies. .
Consequently, many vets will perform a manual blood smear and inspect the sample under a microscope because the results are not always reliable from in-house analyzers, leading to the unnecessary duplication of tests and wasting of materials.
Moreover, the costs of these devices add up for clinics, as the reagents needed to perform the assays can be expensive and they often expire faster than they can be used.
For low-volume clinics that run a handful of samples a day, these hematology analyzers are a costly luxury that are often deemed impractical.
Lack of Accuracy
Another major challenge when it comes to veterinary diagnostics is that of accuracy and reliability. Most of the technology used today was developed and designed for human samples, and eventually converted for veterinary use. In contrast to the human medical sphere, regulation and performance validation of veterinary hematology analyzers are very low, and often these tools are not as precise as they ought to be.
Since standard analyzer technology and the reagents used with them were developed initially for human samples, they are not optimized for testing animal samples, let alone multiple species. Given these limitations, these analyzers can extract a handful of features from animal blood samples, and often miss valuable and telling information.
As many vets don’t feel comfortable relying on these analyzers, they often prefer blood smears, which provide more accurate information. A trained professional performs this test by inspecting a sample of blood under a microscope looking at the morphological characteristics of the cells and their staining properties. These blood smears are still considered the gold-standard and are routinely used to detect pathologies such as blood cancers and blood parasites. This testing method and is more accurate than its automatic counterparts, yet blood smear analysis is laborious, time-consuming and requires the expertise of a trained professional.
New developments in microfluidics, engineering, artificial intelligence and medicine are paving the way for diagnostic devices that address each of these issues, both in the field and in veterinary clinics.
Point of care (POC) devices are designed to perform diagnostic tests near the patient, in or out of a clinical setting, and deliver virtually immediate results. Having on-demand diagnostic information can allow vets to quickly prescribe medication or map out a treatment plan, beginning the process to recovery as quickly as possible.
In comparison to centralized laboratory testing, POC devices offer the substantial advantage of timeliness – instead of requiring a follow up visit or a phone call a week after the initial test, the animal can be diagnosed on the spot and treatment can begin immediately. In addition, due to the miniaturization of new technologies, as well as their robust design, these devices may be taken into the field for farm animal diagnostics, or implemented in individual clinics with a very small footprint.
POC devices, which utilize “lab on a cartridge” technology, do not require the use of wholesale reagents and are simple to use because each test is run on a factory calibrated, self-contained reagent cartridge that is simply inserted into the POC instrument for analysis. This saves not only the cost of materials, but also time, as these devices require no calibration or maintenance. Unlike tabletop analyzers, no expertise is needed to run these tests, so they can be done quickly, efficiently, and cost effectively.
Much of the technology used today to analyze blood samples was developed in the 1960s, and although there have been some improvements since, the basic underlying technology has not matured, leaving much to be desired in the way of both efficacy and precision.
POC alone is not enough to overcome the diagnostic challenges outlined above. By utilizing artificial intelligence and machine vision algorithms, new diagnostic instruments can be taught to recognize hundreds of parameters in each sample, wringing out the most information from a minimal amount of blood.
Crucial to accurate performance in a veterinary setting, in which a vet may treat many different species of animals, AI algorithms can be taught and trained to analyze and differentiate between different animal species’ blood samples. The accuracy and specificity that AI enables will eventually surpass the accuracy of a manual blood smear analysis. Instead of performing a blood smear for each sample, the algorithms can learn to identify dog, cat, or cow cells, and spot abnormalities within each group.
These tests also only require very small amounts of blood, as AI-driven analysis is much more sensitive than the human eye to identifying anomalies and can inspect significantly more cells. The sample is added to the cartridge, which flows the cells in a single cell plane for analysis – a phenomenon known as Viscoelastic Focusing – allowing machine vision algorithms to identify each and every cell, making the most of the resources available and using a minimal amount of reagents and materials.
Ultimately, veterinary medicine should shift towards the implementation of AI-powered, clinically validated POC technology. The performance of these devices is significantly more cost-efficient, providing greater accuracy than the tests performed in centralized labs. AI-driven hematology analyzers are unquestionably more accurate than current tabletop analyzers currently used within many veterinary clinics. The advantages of having immediate, reliable, and cost-effective diagnostic information can save treatment time, resources, and even the lives of the pets and animals we love. It is time to take advantage of the new, cost-effective and resource-efficient tools that are available to improve care for all species.