Endometrics develops a non-invasive genomic biomarker test using menstrual blood samples for the early detection of endometriosis. This technology addresses the lengthy average diagnosis time of eight years, enabling timely intervention and improved treatment outcomes for women suffering from chronic pelvic pain and infertility.
Funding
$200K 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
Endometriosis diagnosis currently relies on invasive surgical procedures and often takes an average of eight years, leading to delayed treatment and prolonged suffering for women experiencing chronic pelvic pain and infertility. This delay can negatively impact treatment outcomes and overall quality of life.
Solution
Endometrics is developing a non-invasive diagnostic test for endometriosis utilizing menstrual blood samples and a proprietary genomic biomarker. This solution aims to significantly reduce the time to diagnosis, enabling earlier intervention and improved management of the disease. The test analyzes specific biomarkers in menstrual blood to identify the presence of endometriosis, providing a less invasive and more accessible alternative to traditional diagnostic methods. By offering a convenient and reliable diagnostic tool, Endometrics seeks to empower women and their healthcare providers to make informed decisions about treatment and care. The company's approach combines clinical validation with machine learning algorithms to ensure accuracy and effectiveness.
Target Audience
The primary target audience includes women experiencing symptoms of endometriosis, such as chronic pelvic pain and infertility, as well as gynecologists and other healthcare providers involved in the diagnosis and management of the condition.
Features
- Non-invasive diagnostic test using menstrual blood samples
- Proprietary genomic biomarker for endometriosis detection
- Clinically validated on hundreds of samples from women with and without endometriosis
- Aims to reduce the average diagnosis time of endometriosis
- Machine learning algorithms for enhanced accuracy