How can artificial intelligence contribute to orthopedic consultations for professionals in canine practice?
The AI integrated into Tendiboots™ canine technology is specifically trained for identifying and localizing lameness in dogs. This training is based on hundreds of gait analyses and the input of fifteen veterinarians.
The stakes of lameness detection
Lameness in dogs serves as a significant indicator of musculoskeletal or neurological issues. Its precise detection holds paramount importance for veterinarians as it directly impacts the well-being and health of the animal, as well as the required treatment. Early diagnosis is crucial for devising effective care strategies and preventing any exacerbation. This task proves particularly challenging in cases of low-grade lameness or when healthy dogs do not exhibit perfect left-right symmetry.
Traditional lameness detection typically relies on visual gait assessment or tools such as instrumented walkways. Symmetry evaluation is then performed by analyzing various locomotion parameters (stride duration, impact force, etc.).
Recent studies have delved into the utilization of artificial intelligence in veterinary diagnostics. This approach holds promise for enhancing efficiency and objectivity for animal healthcare professionals.
A condensed set of objective parameters
The artificial intelligence of Tendiboots™ Canine integrates 12 objective parameters for each of the animal's limbs. Each of these parameters is automatically weighted based on its significance in recognizing lameness or lack thereof. This identification is automated by the deep learning algorithms of this technology.
Data comparisons are systematically made between contralateral limbs. The front legs are compared to each other, while the hind legs are compared to each other.
The training of this technology is conducted on animals weighing from 7 to 65 kg and aids in the detection of lameness ranging from grade 1 to 4 (mild to severe lameness). The tool is also trained to recognize the absence of lameness.
The detection capacity reaches nearly 80%.
This new method for diagnosing lameness in dogs combines clinical observation with computer analysis to provide useful information to veterinarians. Its aim is to enhance the diagnostic capabilities of professionals and provide an additional tool to support their skills and experience.
Objective data assist veterinarians in making informed decisions regarding treatments, rehabilitation protocols, and long-term care plans for dogs.
The algorithms of Tendiboots™ Canine represent an advancement over traditional diagnostic approaches. They employ a data-driven methodology to detect subtle patterns and nuances in the gait and biomechanical parameters of dogs. Through advanced signal processing techniques and feature extraction algorithms, this model can extract important information from raw sensor data, enabling the classification of lameness and the localization of affected limbs with high precision and efficiency.
It has been demonstrated that these artificial neural networks can process complex datasets and are applicable to veterinary diagnosis in a non-invasive and reliable manner.
