Our client
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.
Challange
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.
Solution
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 diagnose osteoarthritis disease among 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.
Results
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.
Important figures
Details about the organizator
Outline of the problem
The challenge was to accurately classify the origins of blood clots in ischemic stroke using histopathological images. The task involved differentiating between two major acute ischemic stroke etiology subtypes: cardiac and large artery atherosclerosis.
The goal was to provide physicians with a reliable tool to assist in stroke etiology classification and prescribe appropriate treatments.
Proposed solution
The proposed solution involved developing a deep learning-based model that accurately classified the etiology of ischemic strokes based on histopathological images.
Before the project, stroke etiology classification was primarily carried out by highly specialized doctors. Mechanical thrombectomy, a standard treatment for acute ischemic stroke, allowed for the analysis of retrieved clots. However, applying deep learning-based methods to predict stroke etiology and clot origin presented challenges due to the unique data formats and large image sizes.
The objective is to enable healthcare providers to better identify the origins of blood clots in deadly strokes, leading to improved post-stroke therapeutic management and reduced chances of recurrent strokes.
Next, their knowledge of deep learning and neural networks was crucial in constructing and training highly accurate classification models. They used these models to predict the origins of blood clots in stroke cases based on the digital histopathological images.
To ensure the reliability of their models, the team also had strong skills in model training and testing. They used techniques like cross-validation to evaluate how well their models performed on new and unseen data.
Moreover, the team’s decision-making abilities in regard to deep learning models played a vital role in their success. They knew how to select the right hyperparameters, optimization algorithms, and loss functions, which significantly influenced the model’s performance and the overall results achieved.
The team divided themselves into subgroups based on their tasks and interests. One subgroup took charge of image preprocessing, ensuring the images were prepared and suitable for analysis. Another subgroup focused on building and training the deep learning models, trying out different configurations to improve accuracy. The third subgroup was responsible for submitting their predictions and ensuring that the model’s performance met the competition’s goals and that the model can handle unseen data.
“It was our first encounter with such large images. Some of these images were massive, reaching up to 2 GB in size. It was quite a challenge even to open them, let alone process, analyze, and evaluate them to determine the type of stroke. Handling these images required innovative approaches and techniques to ensure accurate classification.” –
Expert Insights: Consulting with a Specialized Professor
We spoke with a professor who specialized in a similar field of research for our consultations. In order to better comprehend the difficulty of the work, we sought her expertise and showed her the histopathological images.
Visualizing Complexity: Presenting Histopathological Images
We used online resources to further our understanding by reading medical literature. For instance, we discovered that sometimes certain characteristics, such as for example higher percentage of white blood cells, may reveal the stroke’s origin. Although we gleaned significant knowledge from these sources, we mostly concentrated on creating computer models.
Model Development: Focused on Computer-Based Solutions
As we went along, we focused on selecting the best neural network to handle the photos and developing effective techniques for processing huge (2GB and more) images that couldn’t be input straight into algorithms. For the purpose of removing one-color backgrounds and slicing remaining tissue, we used intelligent methods suited and optimized for the available data.
Advancing Analysis: Refining Picture Classification
As we learned more, we shifted our attention to optimizing the picture analysis and classification algorithms.
Integration Phase: Testing and Component Alignment
The following phase entailed testing and integrating numerous components. The group worked on creating programs to efficiently process the big pathological images. To make the photographs easier to handle for additional investigation, they developed a script to slice the images. The neural network was trained using a different script using the preprocessed, lower resolution photos. Finally, they created an inferencing script that enabled the trained model to make predictions on photos that it had not seen before.
Processed Data Repository: Storage for Experimentation
The team kept these processed images in a folder and used them both for training and validating purposes during the experiments. We trained the neural network, iteratively analyzed the photos, and then used the inferencing script to test the model’s efficacy on Kaggle’s servers.
Results
The team’s efforts resulted in a deep learning model capable of accurately classifying the origins of blood clots in ischemic stroke based on whole slide digital pathology images. Our solution ranked 46th among 896 teams in the Kaggle competition and earned a silver medal. The model’s success indicated its potential to assist healthcare providers in stroke etiology classification.
Key numbers
TAs it involved working with images, the key expertise required was in image processing and normalization. The images often came from different institutions, with variations in lighting and staining, making it essential to apply normalization techniques for consistency. Knowledge of image processing methods, normalization, neural networks capabilities and suitability and decision-making techniques was crucial.
In conclusion, our primary aim was to create a tool that would assist healthcare providers in their decision-making process. Our intention was not to replace the expertise of a doctor but rather to complement their skills and knowledge. The developed algorithm could indicate potential stroke etiologies with a high level of confidence, providing valuable insights to aid doctors in making faster and more informed decisions.
If you have an idea for your own healthcare solution, feel free to reach out to us. We believe in the power of technology and collaborative efforts to enhance medical practices and ultimately improve patient outcomes. Together, we can explore innovative solutions that make a positive impact on the field of healthcare.
Line of business
E-commerce.
Challenge
In e-commerce, companies acknowledge the need to enhance communication between the company and its clients to improve response speed.
Solution
DAC.digital implemented a proof of concept (PoC) for an AI chatbot on its website. The primary objective was to assess how the chatbot could enhance communication, improve response times, and effectively handle customer queries and concerns.
The companies face challenges in effectively addressing customer queries, concerns, and feedback in a timely manner. Many users ask the same questions repeatedly, resulting in a need for a solution to handle such repetitive inquiries.
Implementing a chatbot was seen as a way to address these issues effectively. Currently, numerous employees are dedicated to answering live chat questions. Automating this process through a chatbot would reduce the need for human intervention and free up employees for more strategic tasks.
Moreover, training employees to handle the extensive information required for live chat responses is time-consuming. On the other hand, training a chatbot is much faster and more efficient, allowing for quicker implementation.
Due to the implemented chatbot, companies can work towards achieving complete automation of their customer support process. By utilizing a chatbot, they can streamline communication, reduce reliance on human resources, and provide rapid and accurate responses to customer inquiries.
Our team was composed of a diverse set of experts, including two UX/UI specialists, one front-end developer, one DevOps specialist, and four specialists in Natural Language Processing and backend development.
In the project, we employed a range of technologies for different aspects:
For design, we used Figma. React was utilized for frontend development. The backend consisted of Firebase as well as Python 3 with LangChain. FastAPI was implemented to handle requests efficiently. For service management, we employed Kubernetes.
We have started with research on the feasibility of using various LLMs as a backbone of our chatbot, and ultimately chose OpenAI to power our chatbot.
We carefully designed the chatbot, ensuring it aligned perfectly with our brandbook. Once the design was complete, we developed the backend. In just two weeks, we performed the integration with the frontend successfully.
Having a successful chatbot heavily depended on the data it received, as wrong context could lead to poor performance. So, we carefully curated the ideal dataset for fine-tuning to ensure its optimal functionality.
After successful deployment, we collected internal feedback to evaluate the chatbot’s performance in handling both simple and complex questions, and also run a set of automated tests to evaluate its performance.
Chatbots are a great solution that go beyond e-commerce, demonstrating their efficacy in a variety of industries like banking and healthcare. Users frequently grow impatient while waiting for simple question answers, and they eagerly look forward to prompt responses.
Chatbots in the banking industry provide a helpful solution to this frequent problem. Customers can get prompt answers to their questions by utilizing chatbot technology, which will reduce their dissatisfaction and improve their overall experience.
By giving doctors helpful support, chatbots can also have a big impact in the field of medicine. These intelligent systems can talk to patients, learn about their symptoms, and then transmit the information instantly to doctors.
Customer.
Computer vision processing
Artificial Intelligence & Machine Learning
Mobile application development
The services are performed by DAC.digital developers chosen to form an interdisciplinary, independent team. The core areas of support were based on Data Science with Python and Image Analysis knowledge and experience and DevOps support and were aligned during the so-called “Block Planning Sessions” or prioritized and assigned to our team via email. The initial collaboration began with KickerAce mobile app development and further collaboration on Shot Analyzer software.
The customer has been provided with fully scalable and functional software, meeting the deadlines, requirements, and specifications presented towards the beginning of the project. The collaboration between DAC.digital and customers’ teams has been based on transparency, openness, and honesty resulting in solid trust. Our problem-solving approach and excellent understanding of both technology and business allowed the Sports Computing team to feel comfortable and confident in the results of our work.
