Metfora Diagnostics offers an AI-enabled blood test that analyzes circulating metabolites using high-performance liquid chromatography and mass spectrometry to detect chronic lung and heart disorders, as well as cancers, in their early stages. This technology enables timely diagnosis and intervention, reducing patient mortality and improving quality of life by identifying disease-driven metabolic changes before symptoms escalate.
Funding
$130K 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
Chronic diseases often progress undetected for years, with symptoms only appearing in later stages when treatment options are limited. Traditional diagnostic methods can be costly, invasive, and time-consuming, leading to delayed or incorrect diagnoses and ultimately impacting patient outcomes.
Solution
Metfora Diagnostics offers an AI-enabled blood test that analyzes circulating metabolites using high-performance liquid chromatography and mass spectrometry to detect chronic diseases in their early, more treatable stages. The technology identifies disease-driven metabolic changes, termed "metabolic fingerprints," through machine learning algorithms. By detecting these fingerprints, the test enables timely diagnosis and intervention, potentially reducing patient mortality and improving quality of life. The process involves a simple blood draw, analysis of circulating metabolites, and identification of metabolic fingerprints using proprietary machine learning algorithms.
Target Audience
The primary target audience includes healthcare providers, hospitals, and diagnostic laboratories seeking advanced tools for early disease detection, as well as individuals undergoing annual exams or experiencing early symptoms of chronic conditions.
Features
- AI-enabled blood test for early detection of chronic diseases
- Analysis of circulating metabolites using HPLC and mass spectrometry
- Identification of "metabolic fingerprints" via machine learning
- Non-invasive diagnostic approach using a standard blood draw