CureVision AI - A Step Towards Preventive Oncology
CustomAI solutions for early cancer detection and prevention.
Created on 2nd October 2024
•
CureVision AI - A Step Towards Preventive Oncology
CustomAI solutions for early cancer detection and prevention.
Describe your project
Our AI-powered project aims to detect cancer at an early stage by analyzing medical images, PDFs, and text data. First, the files are stored securely in the cloud using Cloudinary API. If it's a PDF, Pdf.co API converts it into image format, enabling further analysis. For images, the Groq API extracts any relevant text using advanced OCR capabilities.Using our AI-powered Vision Biopsy Model, built with TensorFlow Serving and cloud APIs, we provide healthcare professionals with accurate cancer classifications and early risk alerts. The system is efficient, secure, and designed to improve patient outcomes through timely intervention.All of this data, from patient records to AI-generated predictions, is securely stored in MongoDB. This ensures that both current and historical patient data is available for further analysis, helping professionals track patient progress over time.
In-Scope
Our AI-powered cancer detection system focuses on early-stage identification by analyzing multimodal data—medical images, genomic data, and EHRs. It features latent feature extraction for early detection, high-risk identification, precise cancer classification, and real-time alerts for rapid clinical response, improving patient outcomes.
Out of Scope
The system does not provide treatment recommendations, general health diagnostics, or interact directly with patients. It is exclusively designed for cancer detection and supports healthcare professionals in decision-making.
Future Opportunities
Future enhancements include integrating more data types (e.g., wearable data), expanding detection to other diseases like cardiovascular and neurodegenerative disorders, incorporating predictive analytics for patient outcomes, and leveraging cloud technology for global scalability, especially in underserved regions.
Challenges we ran into
During the development of our AI-Powered Cancer Detection system, we encountered several challenges that tested our problem-solving skills:
Data Privacy and Compliance: Ensuring HIPAA compliance and protecting sensitive patient data required implementing robust encryption and anonymization protocols, with legal consultations to meet regulatory standards.
- Data Gathering and Preprocessing: Accessing medical data was difficult due to limited availability and varying formats. Extensive preprocessing was required to standardize and batch the data for AI training, taking days of effort.
- API Integration: Integrating multiple APIs with inconsistent formats and response times was resolved by building a middleware layer to standardize communication and extensive testing for reliability.
- Model Accuracy: Training on imbalanced datasets posed challenges, which we addressed using data augmentation, SMOTE, and rigorous cross-validation to ensure accuracy.
- Real-Time Alerts: Developing a real-time alert system required implementing web sockets for immediate notifications, ensuring high traffic handling and minimal delays. We also had to resort to SMS, Whatsapp and Telegram APIs.
Tracks Applied (1)