Every year, over 1.6 million Americans and 17 million world citizens are diagnosed with cancer according to the American Cancer Society. The effect of diagnosing cancer earlier and detecting recurrence sooner in those at high risk and cancer patients and survivors is enormous. Affordable early screening offers them the opportunity to pursue treatments that are less invasive, improved survival, and to reduce the staggering direct and indirect costs associated with cancer treatment.
Current tests and procedures used in cancer screening and diagnosis include radiological imaging, endoscopy, biopsy, and cytology. These types of tests have a wide range of costs that go from hundreds to thousands of dollars. Tests such as CT, X-ray, and PET carry risks for radiation exposure that can cause cancer in healthy individuals. Since these methods of detecting cancer at an early stage rely on individual behaviors (such as scheduling the mammogram, colonoscopy, or pap smear, etc.), discomforts and risks can create fears and reluctance for getting screened promptly.
Besides, non-invasive diagnosis and early detection of cancer remain the main problems in health care today. New and emerging non-invasive tests that include biomarker/genetic tests, liquid biopsy, and breath biopsy are being developed. However, the high costs, lengthy testing procedures, and low positive test accuracy make them unfit for routine testing.
The current struggle in cancer detection now is that there’s no affordable and accessible solution to test early cancer presence. So people simply go without knowing if they are carrying cancer. We are developing a predictive diagnostic technology for early detection and monitoring of cancer using a real-time, cost-effective analysis method combined with artificial intelligence. Our invention is used to analyze blood samples for the presence of cancer for early detection and monitoring of cancer progression/recurrence. It is performed directly in blood samples and the results are interpreted by a trained predictive model to identify a specific type of cancer.
Our mission is to accelerate the development of AI applications in cancer detection. We hold the belief strongly that there should be a better way to detect cancer and we fundamentally view AI and machine learning as an enabler.
