Hema.to provides AI-driven cytometry solutions to automate population classification and reporting within laboratory workflows. Their platform accelerates analysis time while ensuring expert-level consistency and reproducibility across experiments. This cloud-based system offers panel-independent analysis, enabling secure, real-time collaboration for hematology diagnostics.
Funding
$3.9M 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
Clinical cytometry labs face challenges in blood cancer detection due to subjective analysis, staffing shortages, and the need for increased diagnostic throughput. Conventional cytometry analysis techniques can be time-consuming and may exhibit variability, impacting the consistency and reliability of results.
Solution
Hema.to offers an AI-powered platform that automates blood cancer detection from flow cytometry data, providing rapid and reproducible diagnostic support. The platform utilizes AI algorithms for automated single-cell classification and population detection, streamlining analysis workflows and improving accuracy. By minimizing subjectivity and accelerating the time from raw data to analysis, Hema.to enhances lab efficiency and enables clinicians to make faster decisions. The system can be trained on lab-specific data and is compatible with standard operating procedures and panel configurations, ensuring adaptability and optimized performance.
Target Audience
The primary target audience includes clinical cytometry labs and hematopathologists seeking to reduce subjectivity, increase diagnostic throughput, and improve the accuracy of blood cancer detection.
Features
- AI-driven algorithms for automated single-cell classification and population detection
- Support for various standard operating procedures (SOPs) and panel configurations
- Ability to train the system on lab-specific data for optimized performance
- Client-side anonymization to ensure GDPR compliance
- IVDD compliance, conforming to ISO 13485, ISO 14971, ISO 62366, ISO 62304, and IEC 82304 standards
- Demonstrated expert-level performance in differential diagnoses of common B-NHL types with high accuracy
- Integration of decision support system into clinical flow cytometry lab space