AI Nanotech Sensor Finds Symptoms of Cancer in the Blood.

 AI Nanotech Sensor Finds Symptoms of Cancer in the Blood.

AI Nanotech

A new study published in Nature Biomedical Engineering shows that artificial intelligence (AI) machine learning combined with nanotechnology can detect the symptoms of ovarian cancer in the blood with a high degree of accuracy. Cancer is the leading cause of death in the world that caused the deaths of nearly 10 million patients by 2020, according to the World Health Organization (WHO). For women diagnosed with gynecological cancer, cervical cancer is the leading cause of death, according to the National Library of Medicine. Early detection and intervention improve the outcomes and increase the chances of survival for those with cancer. Cervical cancer is difficult to diagnose early because it causes few symptoms, and many cases are diagnosed over time, leading to serious side effects. According to the American Cancer Society (ACS), 80% of cervical cancers are not detected early when the tumor is small and does not spread to nearby lymph nodes or tissues. Researchers from Memorial Sloan Kettering Cancer Center, Weill Cornell Medicine, Cornell University, University of Maryland, National Institute of Standards and Technology, Lehigh University, Hunter College High School, and Albert Einstein College of Medicine collaborated on the project. "Serum biomarkers tend to be less sensitive or direct to simplify cancer screening or diagnostic tests," the study authors wrote. "A few identified blood biomarkers for cervical cancer are quite specific, but they are not sensitive enough to detect infant disease and contribute to patient mortality rates." with this cancer." To address this shortage of ovarian cancer biomarkers, scientists are developing AI-powered nanosensor using carbon nanotubes. Using more than 260 blood serum samples, the researchers trained and validated several stages of machine learning to detect cervical cancer. Carbon nanotubes, also called buckytubes, are lightweight tubes that comprise nanoscale-wide carbon. These chemically neutral nanotube can be up to three nanometers wide and the length is usually a few micrometers. Consistent of double-sided coated graphene, these corrosion-resistant carbon nanotubes have a higher thermal conductivity and strength than steel. Scientists have developed models based on sensory networks (ANN), the Random Forest, the Vector Support Machine for classification, decision tree, and retrospect. Bayesian customization used, as well as Python and MATLAB custom code. According to researchers, their solution has 87% to 98% sensitivity, and can be modified to detect other types of cancer. This concept of evidence suggests that machine learning of AI increases the accuracy of cervical cancer detection compared to current biomarker-based methods. With the combination of new inventions for the study of artificial intelligence and nanotechnology, scientists have discovered a new way to detect cervical cancer that surpasses existing biomarkers.