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.
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.
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.
One of the most fundamental problems in causal inference is the estimation of a causal effect when variables are confounded. This is difficult in an observational study, because one has no direct evidence that all confounders have been adjusted for. We introduce a novel approach for estimating causal effects that exploits observational conditional independencies to suggest "weak" paths in a unknown causal graph. The widely used faithfulness condition of Spirtes et al. is relaxed to allow for varying degrees of "path cancellations" that imply conditional independencies but do not rule out the existence of confounding causal paths. The outcome is a posterior distribution over bounds on the average causal effect via a linear programming approach and Bayesian inference. We claim this approach should be used in regular practice along with other default tools in observational studies.