Case Study

Challenge:

The challenge was to develop a comprehensive software solution capable of collecting and analyzing clinical data from IVF clinics worldwide. The platform needed to integrate various data sources, including video and imagery from microscopes, and use advanced algorithms to predict the likelihood of embryo success.

Key challenges included harmonizing clinical data from diverse sources, such as microscopy images and patient data, into a cohesive platform. Additionally, developing a highly accurate predictive algorithm for embryo viability was critical. The system also needed to process data in real-time to provide timely insights to doctors and ensure scalability to handle growing data from multiple clinics across different regions.

Solution:

Intertec provided a nearshoring team extension model, supplying a skilled DevOps engineer to assist in developing and implementing the software solution. Our team worked closely with the client to address technical challenges and deliver a robust, scalable platform. The solution included key features such as a predictive algorithm, which uses AI to analyze microscopic imagery and clinical data, helping doctors select the most viable embryos. We also designed a seamless system to collect and integrate clinical data from multiple sources, including microscopy images and patient histories. 

By leveraging AWS cloud services, we built a secure and scalable infrastructure capable of processing large volumes of sensitive clinical data. The platform was designed to process and analyze data in real-time, providing doctors with immediate insights to inform their decisions. 
Additionally, we ensured the platform had an intuitive, user-friendly interface for doctors and embryologists, making it easy to integrate into their daily workflows.

Key Features Delivered

Intertec’s team played a key role in delivering several essential features that enhanced the functionality and effectiveness of the platform. 

These features included:

• Predictive Algorithm: Leveraging AI, we developed a predictive algorithm that analyzed microscopic imagery and clinical data to assess embryo viability, helping doctors select the most promising embryos for IVF procedures.
• Data Integration: We created a seamless system that integrated diverse clinical data sources, including microscope videos, patient histories, and other relevant medical data, into a unified platform.
  Real-time Analytics: The platform was designed to process and analyze data in real time, enabling doctors to receive immediate, actionable insights that improved decision-making.
• Scalable Infrastructure: Built on AWS cloud services, the system was designed to handle large volumes of sensitive clinical data and scale across multiple fertility clinics worldwide.
• User-Friendly Interface: We ensured that the platform had an intuitive and easy-to-navigate interface, enabling doctors and embryologists to seamlessly incorporate it into their daily workflows.
• Continuous Feedback Loop: The platform continuously tracked outcomes and integrated feedback from users, allowing for ongoing improvements to its predictions and functionalities.

Results That Matter

Through our collaboration, the client was able to enhance the IVF process and significantly increase success rates at fertility clinics using their platform. The system’s predictive capabilities provided valuable insights into embryo viability, allowing doctors to make more informed decisions and improve patient outcomes. 
Key results included increased IVF success rates, as the predictive algorithm effectively identified viable embryos, leading to higher success rates in IVF procedures. The platform also improved efficiency by streamlining data collection and analysis, reducing the time required to make clinical decisions and enhancing clinic operations. 

The software enabled data-driven decision-making by providing actionable insights, helping doctors make better, data-informed decisions about embryo selection. Additionally, the platform was designed to be scalable, handling the growing data needs of multiple clinics and ensuring long-term success.