Decentralized AI Diagnosis Web Application

This web application utilizes Nillion Decentralized AI to provide medical image classification and symptom-based diagnosis. The frontend is developed using Next.js for a responsive and interactive user experience, while the backend is powered by Flask, handling image and symptom data for Nillion-based AI processing.

Features

• Automatic Diagnosis: Given a description of the symptoms, we generate the 3 most likely diagnosis using Nillion's secure TinyBert model.
• Alzheimer's identification: Given a brain MRI scan, we generate a description of the stage of Alzheimer's that the brain is in, using LeNet5.
• Translation (TBD): Generic translation of text to allow doctors to understand patients from different countries

Technology Stack

• Frontend: Next.js
• Backend: Flask
• AI Model: Nillion Decentralized AI

Running the Project

Prerequisites

• Unix-based operating system (Prereq for Nillion)
• Node.js and npm for Next.js
• Python 3.x and pip for Flask
• Nillion access

1. Clone the Repository

git clone https://github.com/pedro123456789-wq/nillion-med-web-app.git

2. Change Directory

Change to the correct directory.

cd diffused-med

3. Frontend Setup (Next.js)

Run the following to install dependencies, and start the Next.js development server.

npm install
npm run dev

The Next.js app will be running on http://localhost:3000.

4. Backend Setup (AIVM)

Open a second shell session in terminal and run the following to create a virtual environment, install dependencies, and start the Flask server.

cd server
python3 -m venv .venv
source .venv/bin/activate 
pip install -r requirements.txt
aivm-devnet

5. Backend Setup (Flask)

Open a third shell, and run the following to start the Flask server.

cd server
source .venv/bin/activate 
python server.py

The Flask app will be running on http://localhost:8080.

6. Launch the Web App

Open a browser and visit http://localhost:3000. You will see the following landing page.

Automatic Diagnosis

1. Navigate to the Diagnosis Page

Click the "Start Diagnosis" button and you'll see the following page.

2. Describe Symptoms

Write about the symptoms you or your patient has been experiencing and click the submit button.

3. Get Likely Diagnosis

You'll see the three most likely diagnoses based on the symptoms.

Alzheimer's MRI Analysis

1. Navigate to the MRI Analysis Page

Click the "Analyze MRI" button and you'll see the following page.

2. Upload MRI Scan

Upload an MRI image of a brain. There are sample images in server/uploads.

3. Get the Stage of Dementia

You'll see the stage of Alzheimer's the brain is likely to be in. There are five possible outputs.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contributing

Contributions are welcome! Please submit a pull request or open an issue to suggest improvements or report bugs.