Inspiration
As a team of aspiring engineers who have interests across that field that all converge around a central use of applying web development and data analytics to real-world problems, in this project, we want to create an easier tool for doctors and researchers to visualize quantified cardiac MRI data that is outputted from image analysis software that does not easily demonstrate gaps in data with wide time points.
What it does
This tool allows you to input time-associate Left Ventricle End Diastolic Dimension (LVDD) data at different time points as taken from cardiac MRI data. This application will utilize these different time points and plot interpolated graphs of the end-diastolic size of the heart to demonstrate the contraction of the heart over time. This will allow doctors to assess fluctuations in contraction based on differences in diastolic dimension throughout the time interval of the MRI scan.
How we built it
We built our website with HTML, React, CSS to create the frontend components. We incorporated the Django framework and Python to develop the backend of the web application to handle logic and implementation.
Challenges we ran into
One of our biggest challenges during the project was deploying PropelAuth while also using the React library for login functionalities, due to the multiple dependencies and varying versions of Python libraries necessary for Node.js and other project components.
Accomplishments that we're proud of
We were able to clean, visualize, and analyze data from a medical dataset. Despite the majority of our group not being biomedical engineers, we successfully learned and applied a niche topic within the realm of biomedical engineering to complete our web application.
What we learned
One of the most valuable things we learned from building this web application was using React for the first time which provided us with several valuable lessons and insights. We were able to implement new features including PropelAuth and dynamic programming.
What's next for MedModeling
Medical modeling is a small-scale tool. We hope to see a tool like this play a bigger role in predictive analytics for public health and wellness. Medical modeling will could play a crucial role in public health efforts by predicting disease outbreaks, assessing population health trends, and optimizing resource allocation. Predictive analytics models can analyze vast amounts of data from sources such as electronic health records, environmental sensors, and social media to identify patterns and early warning signs of disease outbreaks or public health emergencies.
Log in or sign up for Devpost to join the conversation.