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Digital Marketing & PR | AI Aficionados

Digital Marketing & PR | AI Aficionados

LPancreas | CASE STUDY

Developing an ML predictive web app for diabetes diagnosis

LPancreas is a blood test that measures a panel of specific epigenetic motifs that reflect pancreatic cell-loss and use machine learning to predict diabetes

The Challenge

LPancreas is a blood test for diabetes, accompanied by an ML model, that complements current medical practice into diabetes diagnosis and monitoring. Currently there is no medical alternative for detecting pancreatic cell-loss. LPancreas is a product of ABCureD, a company we have worked with, during the previous year, to develop a company website and event banners. They asked us to develop a web app for LPancreas that would connect to the ML model and display calculated predictions for diabetes, once a physician or researcher entered blood analysis data.

LPancreas web app

The Strategy

Our strategy for developing the LPancreas web application was to create a seamless, user-friendly platform that could effectively leverage the power of machine learning for diabetes diagnosis and monitoring.

LPancreas, is a groundbreaking blood test developed by ABCureD, a biotech spin-off, that measures a panel of specific epigenetic motifs to reflect pancreatic cell loss and uses machine learning to predict diabetes. Given the lack of existing medical alternatives for detecting pancreatic cell loss, our goal was to design a tool that could integrate with this innovative blood test and enhance its utility for medical professionals.

To achieve this, we first designed and implemented an intuitive user experience for the web app using Bootstrap, ensuring that physicians and researchers could easily navigate and use the platform. We then integrated the machine learning model into the user interface through the robust Java Spring Boot framework, allowing real-time processing and display of diabetes predictions based on blood analysis data. To ensure the app’s accessibility and reliability, we deployed it on AWS using Python, making it available over the internet. To maintain exclusivity and data security, the app was placed behind a paywall, accessible only to authorized physicians and researchers. This strategic approach ensured that the web app not only met the high standards of medical professionals but also underwent thorough review by the team and scientific peers to confirm its accuracy and functionality.

The Results

The web application was developed as part of a comprehensive EU project that underwent rigorous peer review before its public release. Now as an integral part of the LPancreas kit, the team is actively highlighting its functionality and utility to physicians, showcasing how it can enhance diabetes prediction and care.

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