MarginScan

Elevator Pitch: MarginScan revolutionizes breast-conserving surgery with an AI-powered tool that instantly and accurately evaluates surgical margins, reducing the risk of cancer recurrence and empowering surgeons to make informed decisions in real-time.

Concept

Real-time AI-based intra-operative tool for breast-conserving surgery margin analysis

Objective

To provide surgeons with instant, accurate assessments of surgical margins during breast-conserving surgeries.

Solution

Utilizing a deep learning model, SegNet, MarginScan analyzes specimen mammographies in real-time to identify and evaluate tumour margins, ensuring they meet the desired width.

Revenue Model

Subscription-based service for hospitals and surgical centers, along with per-use fees for the analysis.

Target Market

Healthcare providers, specifically oncology departments and breast cancer surgery centers.

Expansion Plan

Initially target leading cancer treatment centers, then expand to general hospitals and global markets while continuously refining the AI model with more data.

Potential Challenges

Technical accuracy in diverse clinical settings, integration with existing hospital systems, and achieving regulatory approvals.

Customer Problem

Improves surgical outcomes by reducing the risk of local recurrences in breast conservation surgery by ensuring negative surgical margins.

Regulatory and Ethical Issues

Must comply with healthcare regulations such as HIPAA and GDPR for patient data and undergo rigorous FDA (or equivalent) approval process.

Disruptiveness

Transforms traditional pathology by providing instant, accurate intra-operative analysis, reducing wait times for results and potentially the need for re-surgery.