The US Food and Drug Administration has approved a new product aimed at automating blood draws in outpatient situations. Aletta is a standalone robotic device capable of drawing blood from a patient's arm. It uses near-infrared light and Doppler ultrasound to identify a vein and then automates the other processes of a blood draw, such as applying a tourniquet, inserting and disposing of a needle and placing a bandage on the patient. A trained phlebotomist must begin the procedure and oversee the device while it is in use, but a single person can monitor up to three Aletta robots at once. "Blood draws are one of the most commonly performed medical procedures in the United States, yet patients may face delays due to a growing shortage of trained phlebotomists." said Michelle Tarver, director of the FDA's Center for Devices and Radiological Health. The approval for Aletta was granted based on clinical data showing it was capable of successful blood draws at rates comparable to or better than a phlebotomist.

Google AI has made a breakthrough: successfully predicting cardiovascular problems such as heart attacks and strokes simply from images of the retina, with no blood draws or other tests necessary. This is a big step forward scientifically, Google AI officials said, because it is not imitating an existing diagnostic but rather using machine learning to uncover a surprising new way to predict these problems. What's more, the new system shows what parts of the eye image lead to successful predictions, giving researchers new leads into what causes cardiovascular disease. The results of the Google AI research have been published in an article entitled "Prediction of Cardiovascular Risk Factors from Retinal Fundus Photographs via Deep Learning" in Nature Biomedical Engineering. "Using deep learning algorithms trained on data from 284,335 patients, we were able to predict CV risk factors from retinal images with surprisingly high accuracy for patients from two independent data sets of 12,026 and 999 patients," Lily Peng, MD, product manager and a lead on these efforts within Google AI, wrote in the Google AI official blog.

I'd be willing to bet that if you were asked to list ten uses for your smartphone, you probably wouldn't include "medical device" in your answer. But as smartphones become increasingly capable, highly-portable computing platforms, researchers are looking to the computer in everyone's pocket as a way to improve global health. As Wired UK declared earlier this year, the next revolutionary medical device is likely to be your smartphone. Scientists have already developed smartphone-based apps that can monitor asthma, detect skin cancer, and diagnose traumatic brain injuries. The latest app that joins the "doctor in your pocket" list is helping screen for anemia.

We describe an application of probabilistic modeling and inference technology to the problem of analyzing sensor data in the setting of an intensive care unit (ICU). In particular, we consider the arterial-line blood pressure sensor, which is subject to frequent data artifacts that cause false alarms in the ICU and make the raw data almost useless for automated decision making. The problem is complicated by the fact that the sensor data are averaged over fixed intervals whereas the events causing data artifacts may occur at any time and often have durations significantly shorter than the data collection interval. We show that careful modeling of the sensor, combined with a general technique for detecting sub-interval events and estimating their duration, enables detection of artifacts and accurate estimation of the underlying blood pressure values. Our model's performance identifying artifacts is superior to two other classifiers' and about as good as a physician's.