AI Transforms Lab Animal Monitoring with 24/7 Behavioral Tracking.

Advances in artificial intelligence and continuous video monitoring are redefining how scientists study laboratory animals. With round-the-clock home-cage surveillance powered by machine learning, researchers can now capture detailed behavioral data, drive more accurate discoveries, and promote animal welfare—all with minimal human interference.

Neurogeneticist Vivek Kumar’s early experiences highlight the limitations of traditional behavioral analysis methods. While screening thousands of genetically altered mice during his postdoctoral research at the University of Texas Southwestern Medical Center, Kumar relied on basic computer tracking to monitor short episodes of activity. These simplistic systems often reduced animal behavior to a moving dot on a screen, missing the complex subtleties of actions like grooming or social interactions.

Realizing the shortcomings of such methods, Kumar became invested in creating more sophisticated tools for behavioral research. Over the years, he has spearheaded the development of AI-integrated environments that monitor nearly every facet of rodent behavior in real-time. Today, he and a growing community of scientists are applying continuous monitoring techniques to not only enhance research precision but also improve lab animals' living conditions.

The rise of computational ethology—a discipline that blends behavioral science with computational tools—has propelled this transformation. Talmo Pereira, a computational neuroscientist at the Salk Institute, described how technology has replaced traditional observational methods, once dependent on researchers’ manual note-taking. Now, using machine learning and computer vision, scientists can capture animal behaviors with unparalleled accuracy and scale.

Pereira himself contributed significantly to the field through his work on the SLEAP (Social LEAP Estimates Animal Poses) algorithm. Developed during his PhD at Princeton, SLEAP uses deep learning to track and label animal body parts across video frames. Applicable to a wide range of species, SLEAP has been instrumental in research involving everything from mouse social behavior to whale shark movement.

To further boost behavioral tracking capabilities, Kumar and his team at The Jackson Laboratory developed the DAX system—an advanced monitoring unit incorporating cameras, lighting, and computational tools into a self-contained enclosure. The DAX system, connected to a cloud-based Digital In Vivo System (DIV Sys), can track over 60 variables including body mass, posture, grooming, freezing, and shaking. Its machine learning algorithms are trained to distinguish individual animals, even across strains with varying physical traits.

Building on this foundation, Kumar's team partnered with Allentown, a leading animal housing manufacturer, to launch a commercial version of the platform called Envision. Future plans include opening the system to external data streams and additional behavioral classifiers.

At University College London, neuroscientist Julija Krupic took a similar path. Her research into brain regions affected by Alzheimer’s led her to develop smart-Kage, a fully automated housing system for mice. This platform integrates cognitive tests like T-mazes and object recognition tasks, allowing researchers to study hippocampal function over long periods without manual intervention. The availability of low-cost hardware such as Arduino and Raspberry Pi in recent years made this innovation feasible.

krupic co-founded Cambridge Phenotyping to commercialize smart-Kage, which is already being used internationally to study neurodegenerative and developmental conditions in mice.

In addition to generating robust datasets, continuous monitoring minimizes the stress caused by traditional testing methods. Lab animals typically endure frequent handling and unfamiliar environments, which can distort experimental results. Home-cage systems, by contrast, allow for natural behavior observation and reduce interference, thereby improving reproducibility and animal welfare.

Such systems also support early health detection. Behavioral shifts indicating illness, such as decreased movement, can trigger alerts for timely intervention. Moreover, longitudinal data collection on the same subjects may reduce the total number of animals needed for research, as noted by animal welfare expert Sara Fuochi.

However, challenges remain. Despite growing interest, the cost of commercial systems can be prohibitive, and open-source alternatives require technical expertise many labs lack. Another hurdle is accurate identity tracking during group housing—an error rate as small as 0.1% can compromise results by misidentifying animals.

Both Pereira and Kumar emphasize the importance of data sharing to advance the field. Centralized repositories of behavioral classifiers and video datasets could reduce animal use and improve collaboration, though this will require clear guidelines on data ownership and intellectual property.

Looking ahead, Krupic advocates for more discussions around standardizing home-cage monitoring practices. Whether the field should converge on a few key systems or maintain a diverse toolkit remains an open question—one, she believes, that has yet to receive the attention it deserves.

Source: https://www.the-scientist.com/ai-powered-tech-enables-continuous-lab-animal-monitoring-72714

This is non-financial/medical advice and made using AI so could be wrong.