🧠 Endometriosis AI Detection Assistant

Healthcare-focused AI project exploring symptom analysis and explainable NLP-based decision support for endometriosis awareness.

Users enter symptom descriptions, and the system analyzes the input using Natural Language Processing (NLP) techniques to generate structured, explainable insights.

Disclaimer

⚠️ This tool is not a diagnostic system. It is designed for educational and awareness purposes only and encourages users to consult healthcare professionals.

🚀 Live Demo

Try the app here:
https://endometriosis-ai-eqwtqeuqnkzgrzh49pur85.streamlit.app/

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Why This Matters

Endometriosis affects millions of individuals worldwide and is often underdiagnosed due to delayed recognition of symptoms.

This project explores how AI-assisted symptom analysis and natural language processing could help improve awareness, early guidance, and healthcare accessibility.

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The Vision

Endometriosis is often underdiagnosed and can take years to identify.

This project aims to:

• Help users better understand symptom patterns
• Provide structured, explainable AI insights
• Encourage earlier medical consultation
• Demonstrate responsible AI use in healthcare

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Project Goal

The goal of this project is to explore how AI systems can analyze symptom-related input and generate structured, explainable healthcare-oriented insights.

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The Architecture (The Brains 🧠)

The system follows a simple AI pipeline:

1. User Input

   • Text-based symptom descriptions → NLP Model
   • (Future) Medical Images → Computer Vision Model

2. Preprocessing

   • Text cleaning
   • Tokenization
   • Encoding into embeddings

3. Model Layer

   • Machine Learning / NLP model (TensorFlow / future transformer-based model)
   • Pattern recognition for symptom indicators

4. Output Layer

   • Confidence score
   • Explanation of findings
   • Recommendation for medical consultation

5. Frontend

   • Streamlit interface for interaction

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Technologies Used

• Python
• Streamlit
• TensorFlow (baseline model)
• Pandas / NumPy
• (Planned/Future) Hugging Face Transformers
• GitHub for version control

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Features

• Accepts natural language symptom input
• Generates structured, explainable output
• Displays confidence levels
• Provides medical guidance recommendations
• Simple and interactive UI using Streamlit

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Future Improvements

• Expanded symptom datasets
• Structured medical data integration
• Explainable AI enhancements
• Multi-language support
• Improved NLP pipelines
• Clinical collaboration research

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Privacy & Security

This project follows a privacy-first design:

• No permanent storage of user input
• Data processed in temporary session memory
• No personal health information retained
• Optional export features (future enhancement)
• Medical disclaimer included in outputs

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How to Run the Project

1. Clone the repository

git clone https://github.com/samirag2010/Endometriosis-AI.git
cd Endometriosis-AI

2. Install dependencies

pip install -r requirements.txt

3. Run the application

streamlit run app.py

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Example Output

Input

Results

Presentation

View Project Presentation