StratifAI develops multimodal artificial intelligence models that extract prognostic insights from histology images and clinical data to inform treatment decisions in oncology. Their technology enables the identification of biomarkers and personalized treatment plans, reducing unnecessary chemotherapy and improving patient outcomes.
Funding
$1.7M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
MINVFounders
Product
Problem
Oncologists face challenges in accurately predicting cancer recurrence and determining the necessity of adjuvant chemotherapy, leading to potential overtreatment and increased healthcare costs. Traditional genomic assays can be slow and costly, hindering timely treatment decisions.
Solution
StratifAI offers multimodal AI models that analyze routinely available histology images and clinical data to provide prognostic insights for oncology treatment decisions. Their AI-powered breast cancer recurrence test assesses the benefit of adjuvant chemotherapy directly from hematoxylin & eosin-stained whole-slide images, delivering rapid results compared to traditional genomic assays. The platform identifies novel biomarkers and offers customizable solutions for biopharma partners, enhancing clinical trial optimization and biomarker discovery. By providing personalized treatment recommendations, StratifAI aims to reduce unnecessary chemotherapy, lower costs, and improve patient outcomes through data-driven guidance.
Target Audience
The primary target audience includes oncologists seeking precise prognostic models for treatment planning, biopharma companies focused on biomarker discovery and clinical trial optimization, and payers aiming to reduce healthcare costs through optimized resource allocation.
Features
- AI-driven analysis of hematoxylin & eosin-stained whole-slide images for breast cancer prognostication
- Multimodal AI models integrating histology images and clinical data
- Biomarker discovery and validation tools for biopharma R&D
- Clinical trial optimization through AI-driven patient selection and monitoring
- Identification of spatial insights from the tumor microenvironment
- Web-based platform providing rapid results and treatment recommendations