The company is a startup founded by veterinary experts who wanted to create smart solutions for animal healthcare.
They aimed to create wearables and complete products dedicated to professional veterinarian caregivers like animal clinics, hospitals, etc.
We were tasked with taking care of the project as an interim product owner. We needed to create a viable product that could be tested. It involved both software and hardware aspects.
We were initially tasked with creating a machine algorithm for the device. After conducting discovery workshops, our team proposed a new roadmap for the product.
The team of experts proposed a deep-learning approach for the initial classification and assessment of the severity of the illness. The data could be later extracted and used to monitor the disease further.
After identifying the necessary steps to clean the provided data, we employed deep learning models and a deep convolutional neural network based on raw data for pattern recognition.
The startup was founded by veterinary experts with a plan to support professionals and veterinary clinics with technological solutions. They planned to develop an end-to-end solution that would help with OA disease detection in dogs.
The device’s design had to be compact and light enough to attach to the dog’s collar easily. It would then record the movements while walking or running to pinpoint indicators that could designate potential symptoms of OA, alerting the owner to look for further diagnosis. With the vast domain knowledge, our client needed technology experts to complete the product’s technical part.
The first significant challenge involved a tight budget and time. The company has already tried cooperating with other tech companies. However, the results could have been better. Thus, a lot of resources have been wasted. They needed a partner to help progress with the product to be viable enough for testing if it could work and be accurate enough to invest in developing the final product.
Before developing the algorithm, our team (also comprising PhD-level specialists) needed to evaluate the available data collected. Upon examining it, our team noticed that the data wasn’t correctly annotated. There was also an issue with the logistic regression model and overall technical trouble with computer-aided detection and data collection from the gyroscope.
We’ve decided to employ deep learning models and deep convolutional neural networks for imaging data to recognise patterns based on collected raw data. We wanted to prove the universality of solutions of the deep learning model for different breeds. At first, we relied on the previously collected training data provided to us by data scientists.
Our solution involved the development of the right processing artificial intelligence algorithms and AI models of the data from accelerometers and gyroscopes, then creating classification algorithms that operate on the collected data sets and adjusting the software/hardware interfacing to obtain stable results.
Machine learning and artificial intelligence were at the heart of the project. Our task was to conduct data analysis based on the provided data sets and input data, with the help of computing resources, artificial intelligence and machine learning model, with measurements of dog movements and to distinguish between dogs with and without OA based on the collected sensor’s raw data.
Based on the training data provided by data scientists, we started by monitoring dogs’ activity over a long period due to the importance of high precision of deep machine learning and digital imaging. After analysing different data points and input and output variables, we aimed to introduce significant improvements to the solutions regarding logistic regression models, machine learning and AI models. We sought discriminative features and data points crucial for determining dogs’ illnesses. Based on the results, using analytical methods, we were able to determine whether the dog had OA.
Our team proposed a deep neural network machine learning algorithm to increase the level of classified OA severity and the diagnostic accuracy of the device and AI models. The results of our efforts allowed us to sort through all the data and extract the deep features. We implemented artificial intelligence solutions and deep neural networks to distinguish between dogs with OA symptoms. We proposed a high-quality deep-learning model and other deep-learning technologies to generate results that could work with more data in the future.
Our experts and the client were happy with the results achieved during our cooperation. We were able to extract all the necessary data and implement further measures. The speed of the delivery and the quality of provided solutions left the product in a state that allowed for further testing and development of its final stages. Our team built a solid and reliable foundation for the company to proceed with the development upon receiving funds.
