Mobile Application For Antigen Testing Viral Load

The project aims to develop a mobile application utilizing computer vision and machine learning techniques to automate antigen testing for viral load. Specifically, the application focuses on identifying COVID-19 test strips from images, detecting the location of COVID-19 results on various types of test strips, and accurately determining whether the person is COVID-19 positive or negative.

Key Challenges

  • Identifying Test Strips: Ensuring the accurate recognition of COVID-19 test strips from images taken across varied conditions and devices.
  • Result Localization: Detecting the specific result areas on diverse test strip formats, accommodating differences in design and layout.
  • Accurate Diagnosis: Leveraging advanced techniques to reliably determine whether the test result indicates a positive or negative COVID-19 diagnosis.

Our Solutions

  • Mobile Application Development: Designed a cross-platform app with an intuitive interface for capturing and processing antigen test results seamlessly.
  • Machine Learning Models: Deployed robust ML models to analyze test strips, ensuring precise detection and result classification.
  • Automated Antigen Testing: Integrated automation to minimize manual errors and accelerate the antigen testing workflow.
  • Improved Accessibility: Enabled healthcare professionals and individuals to easily access and interpret test results directly from their mobile devices.

Tech Stack

Conclusion

The developed mobile application demonstrates promising results in automating antigen testing for COVID-19. By leveraging computer vision and machine learning technologies, the application offers a reliable and efficient solution for identifying COVID-19 test strips, locating test results, and determining COVID-19 status accurately. This contributes to the broader efforts in combating the COVID-19 pandemic by facilitating widespread testing and early detection of infections.

Tell us your challenge, We're here to help you

Project Overview

IIFL, a leading financial institution in Asia-Pacific, required a solution to help customers track their income, expenses, and investments effectively. Our team developed an AI-powered mobile app that utilizes intelligent technologies like AI and ML to monitor financial transactions, suggest lucrative investment options, and provide insightful financial guidance.

Key Challenges

IIFL, a leading financial institution in Asia-Pacific, required a solution to help customers track their income, expenses, and investments effectively. Our team developed an AI-powered mobile app that utilizes intelligent technologies like AI and ML to monitor financial transactions, suggest lucrative investment options, and provide insightful financial guidance.

Our Solutions

IIFL, a leading financial institution in Asia-Pacific, required a solution to help customers track their income, expenses, and investments effectively. Our team developed an AI-powered mobile app that utilizes intelligent technologies like AI and ML to monitor financial transactions, suggest lucrative investment options, and provide insightful financial guidance.

Tech Stack

Conclusion

In conclusion, SOMPO Insurance has successfully addressed the challenges of providing seamless insurance experiences and supporting both D2C and B2B journeys. Through the development of a comprehensive Customer Web Portal and integration with key services and platforms, SOMPO Insurance has demonstrated its commitment to innovation and customer satisfaction in the insurance industry.

Other Case Study

Algorithma is an AI-based business intelligence software that leverages advanced...