Artificial intelligence in equine lameness workups

Home » All Posts » Horses » Artificial intelligence in equine lameness workups

Lameness in horses is one of the most common presentations faced by equine vets. A thorough lameness workup requires clinical expertise, imaging such as ultrasonography and/or radiography, and local regional assessment. The lameness exam is an incredibly important aspect of equine veterinary, and clinicians perfect this skill after years of training and experience. However, as a large part of the lameness exam relies on personal assessment, subjectivity can occasionally cause problems. Therefore, recent advances in artificial intelligence (AI) aim to eliminate this problem by providing objectivity in lameness assessments. This article aims to explore such recent advances in artificial intelligence.

How artificial intelligence aims to revolutionise lameness evaluation

AI uses computer programs to analyse large amounts of data, leading to the recognition of patterns that may have been missed by the human eye. It aims to eliminate the inconsistency in human evaluation. Some ways AI aims to improve lameness examination include:

1. Gait analysis

AI powered cameras can carefully track how a horse moves and spot small irregularities in its gait that may be attributed to lameness. High speed cameras record a horse’s movements, then analyse the footage to measure stride length, weight distribution and overall equilibrium. Then, the system compares the horse’s movements to a database of healthy and lame horses to assess if issues are present. Overall, this aims to provide an objective assessment, reducing inconsistencies generated by different vets.

2. Wearable sensors

Special devices, such as motion sensors and pressure-sensitive pads, can be attached to a horse’s body or placed within its shoes to measure movement. The data collected by the senses are then assessed by artificial intelligence to detect subtle signs of lameness before they become obvious to the human eye. If such assessment is repeated, AI can track changes in a horse’s movement over time, making it easier to monitor and detect the onset of conditions such as osteoarthritis, allowing for early intervention.

Specific AI systems

Currently there are several AI-based tools breaking into the equine lameness detection and diagnosis sphere. One notable example is Sleip; a scientifically validated equine gait analysis app. It is an AI-powered tool designed to assess equine gait abnormalities using video recordings. The general premiss of the tool is as follows:

Equine vets and horse owners can simply record a horse trotting using their smartphone camera.
The Sleip AI tool analyses the movement in real-time and provides an objective assessment of if lameness is present, by detecting subtle gait asymmetries that may not be visible to the human eye.

The system aims to provide a user-friendly and affordable alternative to expensive motion capture technology by aiding to detect early lameness and additionally allowing remote monitoring, reducing the need for frequent in-person evaluations.

Other AI tools are in development focussing on different areas of equine lameness including diagnostics, early lameness detection and patient specific rehabilitation programmes.

What other areas of equine veterinary medicine is artificial intelligence aiming to enhance?

Diagnostic imaging

AI is being incorporated into the specialty of diagnostic imaging, so modalities such as x-ray, ultrasound and MRI/CT, aiming to enhance the speed and accuracy of image analysis. By using deep learning algorithms, AI can quickly detect abnormalities in such modalities, helping vets diagnose issues earlier and more precisely. This aims to reduce the incidence of human error and allows for more timely decision-making in equine healthcare.

1. X-ray

AI programs can examine radiographs / x-rays to detect joint problems, fractures, and other subtle abnormalities. AI is able to highlight small changes in bone density and joint spaces that could indicate early signs of disease. By comparing current images with historic cases, AI can provide a probability based diagnosis, helping vets make informed decisions.

2. Ultrasound

Ultrasound assesses the soft tissues of the body, therefore is very suited to detecting injuries to tendons and ligaments in horses. AI aims to identify such damaged areas and allow for consistent, highly accurate interpretations. Additionally, recent advances aim to predict healing times and potential reinjury risks.

3. MRI and CT

MRI and CT scans are advanced imaging modalities providing detailed images of bones and soft tissues, but they require expertise to interpret correctly. AI speeds up the process by scanning large amounts of image data to detect small issues like stress fractures or tendon damage. It aims to improve accuracy by reducing false positives and negatives, helping vets make more reliable diagnoses. Additionally, AI-assisted imaging aims to help vets create more targeted treatment plans.

Treatment and recovery planning.

Artificial intelligence is not just useful for diagnosis, it also aims to help vets predict recovery times and plan treatments.

1. Prediction of recovery time

AI analyses past injuries to estimate how long a horse might take to recover. It takes into consideration factors such as the horse’s age, severity of the injury, and any previous treatments. Vets are then able to use this AI prediction to create a patient specific rehabilitation plan and set realistic recovery goals.

2. Rehabilitation and performance monitoring

AI helps track a horse’s recovery and subsequently adjustments to the rehabilitation plan. Smart treadmills and exercise trackers collect movement data during rehabilitation sessions, which the AI analyses to map trends. From this it can assess if the horse is improving or if its rehabilitation plan needs change. AI in this area also aims to reduce reinjury risk by ensuring horses don’t return to activity too soon.

Overall benefits of AI in lameness evaluations

AI aims to provide vets with an advanced level of objectivity, to provide more accuracy and reliability in diagnosis. Overall, the benefits include:

Better accuracy – AI provides more reliable, objective data, eliminating the risk of human error.
Early detection – AI identifies gait changes before a lameness becomes obvious to the human eye.
Faster diagnosis – AI speeds up the analysis of medical images, reducing time required to provide a diagnosis.
Continuous monitoring – AI-powered devices track a horse’s movement over time, allowing issues to be detected earlier and treatment to be implemented earlier.

Challenges of AI in lameness evaluation

Despite its benefits, AI still faces some challenges:

Data accuracy – AI needs high-quality data to make correct predictions, and errors in data can lead to mistakes.
Learning Curve – Vets, farriers and trainers, need training to effectively use AI tools in their daily practice.
Cost – AI-powered diagnostic tools and sensors can be expensive, making them less accessible for some clients.
Regulations – AI-based medical tools must meet veterinary safety and ethical standards.

The future of AI in equine lameness workups

AI is constantly improving, and its future in equine veterinary looks promising to technological advancement:

Remote AI-based lameness checks: AI could allow horse owners to perform basic lameness evaluations at home using smartphone apps.
Genetic AI analysis: AI might help predict a horse’s risk of lameness based on its genetics.
AI-Driven rehabilitation adjustments: Future AI systems could automatically adjust rehabilitation programs in response to a horse’s real-time progress.

AI and the threat to job security

As AI becomes more advanced, there are concerns about its impact on reducing the jobs required to perform similar roles, and this may occur within the equine industry if AI continues to develop in this industry. AI-driven diagnostic tools may reduce the need for vets to perform certain tasks, such as gait analysis or image interpretation. Automated systems could allow horse owners to conduct preliminary assessments without a vet’s presence, potentially decreasing demand for traditional lameness evaluations. Currently however, the focus is not to replace jobs, but to shift the vets’ roles towards interpreting AI data, refining diagnoses, and focusing on treatment and rehabilitation.

Conclusion

Artificial intelligence is an incredible technological advancement, aiming to make human lives easier. Within equine veterinary, AI aims to transform the lameness evaluation by providing earlier, objective and more accurate diagnoses. It aims to improve gait analysis, imaging interpretation, and recovery tracking, as well as monitoring a horse’s gait prior to lameness occurring. While challenges such as cost and data accuracy remain, AI is expected to become a key part of equine veterinary care, ultimately improving the health and performance of horses worldwide. However, the last sentence of this article should highlight that whilst AI has the potential to enhance efficiency and accuracy, it ultimately cannot replace the expertise, hands-on care, and decision-making abilities of equine vets.