Doctor AId

We created an Artificial Intelligence support for doctors to diagnose COVID-19 based on chest x-ray images in order to reduce test times and costs. Algorithms for more diseases were also developed.

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Inspiration

I recently made an appointment to the doctor that had the earliest availability two weeks from the appointment inquiry date! Healthcare services are expensive for uninsured people, who often have a higher likelihood of having special financial needs. Friends in other developing countries have told me that the most affordable COVID19 tests can take up to 12 days to be processed! With these and other additional examples, we believe the healthcare industry could use supporting tools based on tech, and especially Machine Learning, to speed the detection and treatment of several diseases and reduce the operational costs associated with making an accurate diagnosis.

What it does

We developed Doctor AId, which aims to serve as a support to speed-up the diagnosis of (for now) COVID-19, Diabetic Retinopathy and other pulmonary respiratory anomalies. It uses several Machine Learning methods, including CNN classifiers using VGG-19 and Res-Net-50. These predictions are served at scale using server-less functions. The results are stored in a Cloud database from which physicians can access the records and results for a specific patient and provide their verdict on the disease. Our platform can be used as a B2B (Product to Hospitals/Clinics) or B2C (Hospital to Customer/Patient).

The platform currently screens for positive or negative results of COVID-19 based on chest x-rays, the severity of Diabetic Retinopathy from fundoscopic imagies, and provides word diagnosis of lungs anomalies from chest X-Rays using NLP through Visual Attention on a CNN-RNN structure.

How we built it

  • The Front-End was built using HTML/CSS/JavaScript. It also uses Bootstrap 4.
  • The Back-End used primarily Node.js and Express.js among other libraries
  • The server less APIs were built in Python 3 via Tensorflow and Keras and deployed in Google Cloud Functions. This also required the Google Cloud Build API
  • Cloud Storage was used to store the information about the Classifiers and Machine Learning models to recreate the models in the cloud to make predictions.
  • The database used to store the records of the patients and their results was hosted in Firebase via Google Cloud Platform
  • We started to work in hosting the application through Google Cloud App Engine.

Challenges we ran into

  • Deploying the Google Cloud Functions effectively. Some libraries are not available in the cloud, so we had to do pip freeze and upload some of the dependencies as deployment packages.
  • Connecting the Node.js application to Firebase
  • Retrieving the data posted on Firebase

Accomplishments that we're proud of

  • Connecting the Node.js application to the Firebase database!
  • Successfully deploying the COVID19 cloud function.

What we learned

  • Many new ways to use Bootsrap 4
  • Using Firebase with Real Time Database
  • Hosting an application in Google Cloud App Engine
  • How Hackathons work; this is the first Hackathon for Elise and the second for Alfonso.

What's next for Doctor AId

  • Improving the detection algorithms to be more precise and accurate
  • Improving the access of physicians for individual patients and connecting to medical records from other platforms
  • Adding cybersecurity protocols
  • Expanding the Machine Learning detection algorithms to include more diseases.
  • Scaling the platform to be accessible for developing countries and measuring the impact on reducing costs for the healthcare system.

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