Medical Test Report Analyzer

Description

Welcome to the Medical Test Report Analyzer, a powerful Python application designed to make medical test reports accessible to everyone. By uploading an image of a medical test report (e.g., blood tests, X-rays), users receive a clear, concise interpretation of their health status, including any abnormal results and actionable recommendations. This tool combines Optical Character Recognition (OCR) with advanced AI to extract and analyze text, presenting results in an intuitive Streamlit web interface. Whether you're a non-medical user seeking clarity or a developer exploring AI in healthcare, this project offers a practical and innovative solution.

Why Use This Project?

• Simplify Medical Reports: Get easy-to-understand explanations without medical expertise.
• AI-Driven Insights: Leverage advanced language models for accurate health interpretations.
• User-Friendly Interface: Upload images and view results effortlessly via Streamlit.
• Developer-Friendly: Open-source with clear setup instructions for contributions.
• Privacy-Focused: Runs locally with Ollama, ensuring data stays on your device.

Features

• Text Extraction: Uses Pytesseract for accurate OCR to extract text from image-based reports.
• AI-Powered Analysis: Employs Ollama with the "qwen2.5:latest" model to generate user-friendly health summaries.
• Intuitive Interface: Built with Streamlit, offering seamless image uploads and result displays.
• Structured Workflow: Utilizes LangGraph to manage the process from upload to interpretation.
• Robust Error Handling: Includes logging and checks for issues like failed OCR or AI processing.
• Structured Output: Provides a health status summary, abnormal results, and recommendations.

Installation

Follow these steps to set up the Medical Test Report Analyzer:

1. Install Python 3.8 or Higher:

   • Download from Python.org.
   • Verify installation:

     python --version

2. Install Required Libraries:

   • Install dependencies using pip:

     pip install streamlit pillow langgraph langchain-core langchain-ollama pytesseract

3. Install Tesseract OCR:

   • Windows: Download from Tesseract GitHub and add to your system PATH.
   • Linux/Mac: Install via package manager:

     # Ubuntu/Debian
     sudo apt-get install tesseract-ocr
     # macOS (with Homebrew)
     brew install tesseract

4. Set up Ollama:

   • Install from Ollama.com.
   • Pull the required model:

     ollama pull qwen2.5

   • Start Ollama:

     ollama serve

5. Clone the Repository:

   • git clone https://github.com/armanjscript/Medical-Test-Report-Analyzer.git

6. Navigate to the Project Directory:

   • cd Medical-Test-Report-Analyzer

7. Run the Application:

   • streamlit run main.py

Note: Ensure sufficient computational resources (e.g., 16GB RAM, CPU/GPU) for running the AI model. An internet connection is required for initial setup.

Usage

1. Launch the Application:

   • Run streamlit run main.py to open the app in your default web browser.

2. Upload an Image:

   • Click the "Upload Image" button and select a medical test report image (PNG, JPG, JPEG).

3. View Results:

   • The app displays:
     • Extracted text from the image.
     • An AI-generated interpretation, including:
       • Health status summary.
       • Abnormal results (if any).
       • Recommendations for next steps.

Example:

• Upload a blood test image with "Hemoglobin: 12.5 g/dL, WBC: 7.2 x10^9/L".
• The app extracts the text and provides: "Your hemoglobin is normal, but your WBC count is slightly elevated. Consult a doctor for further evaluation."

Technologies Used

Technology	Role
Python	Primary programming language.
Streamlit	Creates the interactive web interface.
PIL (Pillow)	Handles image processing and uploads.
LangGraph	Manages the workflow from upload to analysis.
LangChain	Integrates with the language model and structures prompts.
OllamaLLM	Runs the "qwen2.5:latest" model for medical interpretations.
Pytesseract	Performs OCR to extract text from images.
Logging	Tracks operations and aids debugging.
Regular Expressions	Cleans and processes extracted text.

Contributing

Contributions are encouraged! To contribute:

• Fork the repository on GitHub.
• Create a new branch for your changes.
• Submit a pull request with a clear description.
• For bug reports or feature requests, open an issue.

License

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

Contact

For questions or feedback, open an issue on GitHub or [[email protected]].