CAD THAT WORKS®
Early and Accurate Detection of Breast Cancer in Mammograms.
CureMetrix is committed to the advancement of technology that will improve cancer survival rates worldwide. Our research is focused on leveraging artificial intelligence and deep learning to develop the next generation of medical image analysis technology that radiologists and patients can confidently rely on.
Today, we are honing our powerful image analysis platform into an adjunct, precision tool for mammography. Our goal is to equip radiologists with the objective, data-driven answers they need to support patients and their healthcare team as they make decisions about breast cancer screening, treatment and diagnosis. At CureMetrix, we believe that providing radiologists with the most advanced technology to support their evaluation of mammograms will lead to improved clinical outcomes, reduced healthcare costs and increased assurance that patients are getting the highest standard of care available from screening through post-biopsy follow-up.
We have surveyed radiologists globally and they have expressed the need to improve the performance of their existing Computer Aided Detection (CAD) software. At CureMetrix, we are building a ‘CAD that works.’
To support our efforts, CureMetrix has partnered with esteemed institutions such as University of California, San Diego, MD Anderson Cancer Center, Hoag Health Network and Johns Hopkins University to train and validate our algorithm’s ability to recognize different types of anomalies. We’ve collected more than 500,000 images to build up advanced, highly-complex deep learning networks. We are working on developing technology to:
- Accurately detect, quantify and classify anomalies in screening and diagnostic mammograms
- Differentiate between various types of anomalies to help radiologists focus first on suspicious lesions, while discarding verifiably benign ones
- Reduce the rates of false positives and false negatives to improve clinical efficacy, operational efficiency and financial outcomes – while, most importantly, easing the anxiety that such diagnoses can cause patients