At first glance, it seems counterintuitive. To learn more about how cells go awry causing disease, it seems logical to focus on the minutiae of a cell's molecular components: the specific genes, proteins, and small molecules that change over time leading to a disease state. Instead, Ernest Fraenkel, MIT associate professor in the Department of Biologic Engineering, first takes a macro view for finding new ways to understand and cure diseases. "When you are faced with making any sense of the 10,000 or 20,000 molecules that are present within a cell and evolve during disease, you need an entirely new approach to figure out what is really important among all the changes you see," Fraenkel explains. He develops computational and laboratory experimental methods to uncover the molecular pathways that go awry in disease and search for new strategies and intervention targets.
A deep learning algorithm can detect metastases in sections of lymph nodes from women with breast cancer; and a deep learning system (DLS) has high sensitivity and specificity for identifying diabetic retinopathy, according to two studies published online Dec. 12 in the Journal of the American Medical Association.
A deep learning algorithm can detect metastases in sections of lymph nodes from women with breast cancer; and a deep learning system (DLS) has high sensitivity and specificity for identifying diabetic retinopathy, according to two studies published online December 12 in the Journal of the American Medical Association.
IBM Watson Health has formed a medical imaging collaborative with more than 15 leading healthcare organizations. The goal: To take on some of the most deadly diseases. The collaborative, which includes health systems, academic medical centers, ambulatory radiology providers and imaging technology companies, aims to help doctors address breast, lung, and other cancers; diabetes; eye health; brain disease; and heart disease and related conditions, such as stroke. Watson will mine insights from what IBM calls previously invisible unstructured imaging data and combine it with a broad variety of data from other sources, such as data from electronic health records, radiology and pathology reports, lab results, doctors' progress notes, medical journals, clinical care guidelines and published outcomes studies. As the work of the collaborative evolves, Watson's rationale and insights will evolve, informed by the latest combined thinking of the participating organizations.
Toya T Peterson speaks on medical devices from the Sci-Fi world that could one day become reality. The handheld medical device used in the popular Star Trek Enterprise might soon become a reality. With Qualcomm having began a contest to see if anyone can create a working tricorder (that weighs less than 5 pounds and fits in the palm!), the healthcare industry might be able to benefit from this great innovation. With the ability to diagnose different conditions (ranging from anemia, diabetes, pneumonia, sleep apnea, and chronic diseases amongst others) and monitor vital signs (like blood pressure, heart rate, temperature and respiratory rate) of patients, the tricorder can be used by patients in the comforts of their homes, without having to visit the doctor. While Ender's Game featured a surgical robot performing brain surgery, robotic medical assistants majorly enable safe patient lifting, reducing incidents of workplace injuries, and hence improved clinician staff retention and satisfaction as well as patient satisfaction.