- Developed a computer vision algorithm for analyzing user-uploaded test strip photos.
- Explored varied image analysis methods, including using a handle area as reference, color comparison, and flat comparator.
- Goal was to create a reliable algorithm with accurate results and a user-friendly experience.
Computer Vision Test Strip Analysis For Pet Health
Training a Computer Vision algorithm to quickly provide accurate, real-time feedback.
The Story
The company embarked on a project to develop a mobile app that utilizes computer vision and AI technologies for analyzing photos of test strips to evaluate pet dental health. Their goal was to create a user-friendly solution that would enable non-technical and non-medical users to interpret test results accurately and expand their user base. The project involved building and training a computer vision algorithm and developing a single-page web app for real-time result interpretation.
Technology: Computer Vision, AI, AWS services, MAUI
Challenges
During the project, the company encountered several challenges. From a business perspective, they needed to expand their product launch beyond periodontal disease tests for canines and felines offered through veterinarians. They also faced technical challenges related to collecting and preprocessing relevant data to ensure its quality and suitability for training the AI model. Additionally, they had to address process issues, such as determining the best approach for test strip analysis and ensuring a robust and user-friendly UX.
Key Features
Hitting The Target
The project successfully concluded with the deployment of the computer vision algorithm integrated into the single-page web app. Users can now upload a photo of a test strip and receive real-time results interpreted by the algorithm. The app simplifies the process of interpreting test strip results, eliminating the need for users to manually match colors or analyze the results themselves. The project's outcome paves the way for estimating the design and development of a mobile app to further expand the user base.
Hitting The Target
The project successfully concluded with the deployment of the computer vision algorithm integrated into the single-page web app. Users can now upload a photo of a test strip and receive real-time results interpreted by the algorithm.
The app simplifies the process of interpreting test strip results, eliminating the need for users to manually match colors or analyze the results themselves. The project's outcome paves the way for estimating the design and development of a mobile app to further expand the user base.
“We knew we didn’t want to bring on developers or a CTO on our first day. If you get that hire wrong, you could miss hitting key milestones early on when it’s crucial to get your product market fit right. We had great first-hand experience with Slingshot on another project, and knew they would deliver. And to add, Slingshot’s culture deeply resonated with us. They are an extension of the Armadillo family.”
Matan SlagterCEO of Armadillo
