The startup develops compact bio-crystallography technology utilizing ultra-low-dose x-ray diffraction and machine learning to detect breast cancer at early stages. This approach aims to provide accurate and comfortable breast exams, reducing unnecessary medical procedures and healthcare costs while alleviating patient anxiety.
Funding
$6M 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.
Founders
Product
Problem
Current breast cancer screening methods often involve painful, invasive procedures and can lead to long waiting times for results, causing anxiety and increased healthcare costs. Existing methods may also lack the sensitivity needed for early detection, resulting in delayed diagnoses and poorer patient outcomes.
Solution
EosDx is developing a non-invasive breast cancer detection platform based on compact bio-crystallography and AI-powered analysis of the extracellular matrix (ECM). The technology uses ultra-low-dose X-ray diffraction to analyze the ECM's molecular structure, identifying subtle changes indicative of pre-symptomatic cancer. This approach aims to provide a fast, painless, and objective alternative to traditional methods, reducing the need for invasive biopsies and enabling earlier, more accurate diagnoses. The platform's AI algorithms compare diffraction patterns to a growing database of samples, classifying them as normal or malignant within minutes. The goal is to improve early detection rates, reduce healthcare disparities, and alleviate patient anxiety associated with current screening processes.
Target Audience
The primary target audience includes women undergoing routine breast cancer screening, as well as healthcare providers seeking a more accurate, comfortable, and cost-effective diagnostic tool.
Features
- Compact bio-crystallography technology for molecular-level analysis of the extracellular matrix
- Ultra-low-dose X-ray diffraction, delivering 1/1000th the radiation of a conventional mammogram
- AI-powered machine-learning algorithms for automated classification of tissue samples
- Cloud-based, HIPAA-compliant database for secure storage and analysis of scan data
- Non-invasive Quantum Biopsy™ approach, eliminating the need for tissue compression or injections
- Objective digital data analysis, removing subjective human interpretation
- Intended to achieve greater than 90% sensitivity and specificity
- Exam time of approximately 10 minutes