Next generation blood analytics boosted by AI
We helped Fepod strengthen their artificial intelligence and machine learning capabilities needed for analytics, and enhance affordable and user-friendly diagnostics that benefit both patients and health care professionals.
Fepod is developing a point-of-care solution for measuring the blood concentration of pain medicines. It gives results in just seconds and enables faster and more accurate treatments. Together with Fepod, we strengthened their AI and machine learning capabilities for handling and analysing blood sample data.
Next generation point-of-care blood analytics method
Making measuring the blood concentration of paracetamol, opioids and other pain medicines from a drop of blood quick and easy
Modern cloud, AI and ML technology for analytics enables effectiveness and scalability
Huge benefit potential for patients, health care sector and the whole society
Fepod is a company developing next generation analytics solutions for measuring the real blood concentration of paracetamol, opioids and other pain medicine directly from a drop of blood at the point-of-care. The result is available in seconds – no need to send the patient or blood samples to the lab, saving both patients and healthcare professionals time and effort.
AI and machine learning at the core of fast and user-friendly point-of-care testing
The testing solution consists of a mobile application, small potentiostat and disposable sampling sensors.
Our role was to help Fepod to develop their artificial intelligence and machine learning capability for handling and analysing the blood sample data.
These capabilities we developed with Solita are at the core of our product.
Jussi Pyysalo Founder & CEO, Fepod
Together with Fepod, we designed and implemented a cloud-based machine learning operations platform (MLOps) using Azure and Databricks technologies. On top of that, we created a machine learning solution that receives patient measurement data from the blood sample through the mobile application and estimates the concentration of paracetamol or other drugs within that sample as accurately as possible. The doctor or nurse will get the results immediately from the application on their mobile phones.
“These capabilities we developed with Solita are at the core of our product. Fast, reliable and secure analysis of blood sample data is crucial for utilising the huge potential this affordable and user-friendly diagnostics method opens to modern health care. Our solution benefits both patients, health care professionals and even the whole society when resulting in streamlined testing processes, faster diagnosing and more accurate treatments”, says Jussi Pyysalo, CEO, Fepod.
Results in a tight timeline
There was a tight deadline for Fepod to be able to showcase and demo their end-to-end working solution already during a medical expo organised in July 2023 when we started our work in the beginning of April.
“A partner with extensive knowledge of AI/ML and experience in developing solutions within the healthcare industry was crucial to us. As the timeline was only 2,5 months and we had a limited dataset to work with, we needed a good team and seamless cooperation to make things happen and get a full working solution ready fast”, says Antti Rasi, CIO, from Fepod.
“A cloud-based machine learning operations (MLOps) platform provides scalability and flexibility that is needed for all of the varying future needs. Using machine learning is a must in order to be able to provide fast and actionable results for medical professionals from complex blood sample data. It has been great to work with the Fepod team and their cutting-edge solutions bringing point-of-care testing to a new era”, says Mikael Ruohonen from Solita.
A partner with extensive knowledge of AI/ML and experience in developing solutions within the healthcare industry was crucial to us.
Antti Rasi Co-founder & CIO, Fepod
Fepod’s technology is based on years of research at Aalto University. Fepod continues developing and commercialising the solution. They will, for example, perform more clinical testing and go through the Medical Device Certification process before being ready for the market entry. Scalable architecture and platform enables expanding the solution to new application areas.
Technologies we used:
- Microsoft Azure
- Databricks Unity Catalog
- Databricks Workflow
- Databricks Realtime Endpoints
- Delta Lake
Contact us – Want to know more?
Mikael Ruohonen Business Lead, Data Science & AI, Solita
[email protected] +358 414 516 808