Month: May 2020

Physicians re-evaluate use of lead aprons during X-rays

When you get routine X-rays of your teeth at the dentist’s office or a chest X-ray to determine if you have pneumonia, you expect the technologist to drape your pelvis in a heavy radioprotective apron. But that may not happen the next time you get X-rays.

There is growing evidence that shielding reproductive organs has negligible benefit; and because a protective cover can move out of place, using it can result in an increased radiation dose to the patient or impaired quality of diagnostic images.

Shielding testes and ovaries during X-ray imaging has been standard practice since the 1950s due to a fear of hereditary risks — namely, that the radiation would mutate germ cells and these mutations would be passed on to future generations. This concern was prompted by the genetic effects observed in studies of irradiated fruit flies. However, such hereditary effects have not been observed in humans.

“We now understand that the radiosensitivity of ovaries and testes is extremely low. In fact, they are some of the lower radiation-sensitive organs — much lower than the colon, stomach, bone marrow and breast tissue,” said  Donald Frush, MD, a professor of pediatric radiology at Lucile Packard Children’s Hospital Stanford.

In addition, he explained, technology improvements have dramatically reduced the radiation dose that a patient receives during standard X-ray films, computerized tomography scans and other radiographic procedures. For example, a review paper finds that the radiation dose to ovaries and testes dropped by 96% from 1959 to 2012 for equivalent X-ray exams of the pelvis without shielding.

But even if the radioprotective shielding may have minimal — or no — benefit, why not use it just to be safe?

The main problem is that so-called lead aprons — which aren’t made of lead anymore — are difficult to position accurately, Frush said. Even following shielding guidelines, the position of the ovaries is so variable that they may not be completely covered.  Also,  the protective shield can obscure the target anatomy. This forces doctors to live with poor-quality diagnostic information or to repeat the X-ray scan, thus increasing the radiation dose given to the patient, he said.

Positioning radioprotective aprons is particularly troublesome for small children.

“Kids kick their legs up and the shield moves while the technologists are stepping out of the room to take the exposure and can’t see them. So the X-rays have to be retaken, which means additional dose to the kids,” Frush said.

Another issue derives from something called automatic exposure control, a technology that optimizes image quality by adjusting the X-ray machine’s radiation output based on what is in the imaging field. Overall, automatic exposure control greatly improves the quality of the X-ray images and enables a lower dose to be used.  

However, if positioning errors cause the radioprotective apron to enter the imaging field, the radiographic system increases the magnitude and length of its output, in order to penetrate the shield.

“Automatic exposure control is a great tool, but it needs to be used appropriately. It’s not recommended for small children, particularly in combination with radioprotective shielding,”  said Frush.

With these concerns in mind, many technologists, medical physicists and radiologists are now recommending to discontinue the routine practice of shielding reproductive organs during X-ray imaging. However, they support giving technologists discretion to provide shielding in certain circumstances, such as on parental request. This position is supported by several groups, including the American Association of Physicists in Medicine,  National Council on Radiation Protection and Measurements and American College of Radiology.

These new guidelines are also supported by the Image Gently Alliance, a coalition of heath care organizations dedicated to promoting safe pediatric imaging, which is chaired by Frush. And they are being adopted by Stanford hospitals.

“Lucile Packard Children’s revised policy on gonadal shielding has been formalized by the department,” he said. “There is still some work to do with education, including training providers and medical students to have a dialogue with patients and caregivers. But so far, pushback by patients has been much less than expected.”

Looking beyond the issue of shielding, Frush advised parents to be open to lifesaving medical imaging for their children, while also advocating for its best use. He said:

“Ask the doctor who is referring the test: Is it the right study? Is it the right thing to do now, or can it wait? Ask the imaging facility:  Are you taking into account the age and size of my child to keep the radiation dose reasonable?”

Photo by Shutterstock / pang-oasis

This is a reposting of my Scope story, courtesy of Stanford School of Medicine.

Physicists curate list of COVID-19 projects to join

As we continue to deal with the global COVID-19 pandemic, biomedical researchers are racing to understand the virus that causes the disease, to evaluate its spread, and to develop tests, treatments and vaccines.

Physicists are volunteering to assist in these efforts, using their skills in data analytics, machine learning, simulation, software, computing, hardware development and project management. And an organization called Science Responds is helping to match them with projects that need their support.

As Savannah Thais, a postdoctoral researcher in high-energy physics and a co-founder of Science Responds, reported at the April meeting of the American Physical Society, physicists are assisting with a variety of types of projects, divided into the following categories:

Epidemiology

Epidemiology is the branch of medical science that studies public health problems and events in order to understand what causes them, how they are distributed among populations and possible ways to control them. Epidemiologists investigate diverse problems including pollution, foodborne illnesses, natural disasters and infectious diseases such as COVID-19.

Science Responds is connecting physicists with epidemiological projects that are working to model how the virus that causes COVID-19 might spread. Physicists hope to help address a major problem that the experts making these models face: incorporating data from a multitude of dissimilar sources.

Thais says physicists have the background and experience needed to provide epidemiologists this kind of support.

“We don’t think physicists should be building their own epidemiological models from scratch, because they don’t have the domain expertise of an epidemiologist or biologist about infectious diseases,” she says. But “physicists can be most effective by providing their computing and statistics skills to interdisciplinary research.”

One epidemiology project Science Responds encourages volunteers to join is HealthMap, which displays data about COVID-19 cases across the globe over time via an openly accessible website and mobile app. HealthMap integrates and filters data from diverse, publicly available sources—including online news aggregators and reports from governments and agencies such as the World Health Organization—and then creates intuitive visualizations of the state of the outbreak by location.

Other modeling projects use analyses of the genomic features of previously studied viruses to help estimate unreported COVID-19 cases; integrate health and hospital resource data to inform localized risk predictions; and incorporate information from previous animal and human outbreaks to improve model accuracy.

Diagnosis

An important part of dealing with an epidemic is determining who has the disease, but shortages of testing supplies have made diagnosis a challenge. Science Responds promotes projects that are trying to address this gap in different ways.

Some projects use artificial intelligence to process visual or audio data. The project CAD4COVID, for example, builds off an existing technology that has been highly successful in diagnosing tuberculosis through the analysis of chest X-rays. The project COVID Voice Detector, on the other hand, is collecting audio recordings to develop an AI that can recognize signs of COVID-19 infection in a patient’s voice.

Other projects are building tools to predict who is likely to experience the most severe effects of COVID-19. These machine-learning-based efforts identify indicators such as markers that appear in blood tests or specific features from lung biopsies to predict the likelihood of long-term hospitalization or death.

Treatments and cures

The race to develop COVID-19 vaccines and treatments begins with understanding the physical structure of the virus. On this front, Science Responds collaborators are providing key support for an effort called Folding@Home, which uses computer simulations to map out the proteins the SARS-CoV-2 virus uses to reproduce and suppress a patient’s immune system. Physicists are helping to develop the protein-folding simulations, but they are also playing a pivotal role in looking for help from anyone with a computer that Folding@Home can use remotely to run folding simulations.

In addition, physicists are helping process the massive amount of data related to the SARS-CoV-2 genome. They’re hoping to identify molecules that are important to the growth and spread of the virus and to understand its mutations.

Science Responds collaborators are also aiding efforts to use machine learning to identify drugs that could be repurposed to treat COVID-19. For example, they are using natural-language-processing algorithms to comb through a massive database of scholarly articles, called CORD-19, for relevant ideas. Other projects are using deep-learning-based models with existing data to predict how commercially available drugs will interact with the virus.

Supporting hospitals and healthcare systems

Science Responds participants are volunteering on projects to support frontline workers who are providing medical care to COVID-19 patients. These efforts include developing models to help predict hospital overload and to allow for the sharing of resources such as mechanical ventilators and personal protective equipment.

One example is the CHIME project, which gathers information on hospital resources and predicts when the needs of patients will exceed an institution’s capacity. CHIME has already been deployed in several hospitals, including the University of Pennsylvania Health System.

Another project in this area is COVID Care Map, which is using open-source data to map existing supplies of hospital beds, ventilators and other resources needed to care for COVID-19 patients such as available staff.

Other projects highlighted by Science Responds are aimed at improving telehealth. Enhanced at-home care could reduce the spread of COVID-19 by eliminating unnecessary hospital visits and improving access to care for rural areas.

Researchers are helping to develop AI-based chatbots that can be used to assess possible infections, educate patients and call on human providers when necessary. Other projects are working to combine in-home sensors and cameras with AI-assisted technologies to remotely monitor the health of vulnerable populations.

Socio-economic response

Finally, Science Responds volunteers are also working to address what they call “second-order effects,” not directly related to healthcare.

Some projects deal with infodemiology, research into what we can learn from user-contributed, health-related content on the internet. Researchers are analyzing millions of real-time tweets related to COVID-19 to answer questions like: How are people reacting to the outbreak? How is Twitter being used to pass on vital information? How is Twitter being abused to spread false information, panic and hate?

Physicists with data-analysis and data-engineering expertise can volunteer for projects aimed at bringing attention to at-risk populations. Thais leads a project that is developing a COVID-19 Vulnerability Index, an AI-based predictive model used to identify communities at high risk of socio-economic and health impacts associated with the spread of COVID-19.

The index looks at a wide range of measures, such as whether community members have access to home Wi-Fi, whether they are affected by non-COVID health issues such as diabetes, and whether healthcare resources are available to them.

Are you a physicist looking to volunteer? Thais recommends checking out the Science Responds website, which lists projects organized by their required skills, highlights available data sources, computing resources and funding opportunities, and provides instructions for getting connected.

Illustration by Sandbox Studio, Chicago with Ana Kova

This is a reposting of my news feature, courtesy of Symmetry magazine.

 

Nerve interface provides intuitive and precise control of prosthetic hand

Current state-of-the-art designs for a multifunctional prosthetic hand are restricted in functionality by the signals used to control it. A promising source for prosthetic motor control is the peripheral nerves that run from the spinal column down the arm, since they still function after an upper limb amputation. But building a direct interface to the peripheral nervous system is challenging, because these nerves and their electrical signals are incredibly small. Current interface techniques are hindered by signal amplitude and stability issues, so they provide amputees with only a limited number of independent movements. 

Now, researchers from the University of Michigan have developed a novel regenerative peripheral nerve interface (RPNI) that relies on tiny muscle grafts to amplify the peripheral nerve signals, which are then translated into motor control signals for the prosthesis using standard machine learning algorithms. The research team has demonstrated real-time, intuitive, finger-level control of a robotic hand for amputees, as reported in a recent issue of Science Translational Medicine.

“We take a small graft from one of the patient’s quadricep muscles, or from the amputated limb if they are doing the amputation right then, and wrap just the right amount of muscle around the nerve. The nerve then regrows into the muscle to form new neuromuscular junctions,” says Cindy Chestek, an associate professor of biomedical engineering at the University of Michigan and a senior author on the study. “This creates multiple innervated muscle fibers that are controlled by the small nerve and that all fire at the same time to create a much larger electrical signal—10 or 100 times bigger than you would record from inside or around a nerve. And we do this for several of the nerves in the arm.”

This surgical technique was initially developed by co-researcher Paul Cederna, a plastic surgeon at the University of Michigan, to treat phantom limb pain caused by neuromas. A neuroma is a painful growth of nerve cells that forms at the site of the amputation injury. Over 200 patients have undergone the surgery to treat neuroma pain.

“The impetus for these surgeries was to give nerve fibers a target, or a muscle, to latch on to so neuromas didn’t develop,” says Gregory Clark, an associate professor in biomedical engineering from the University of Utah who was not involved in the study. “Paul Cederna was insightful enough to realize these reinnervated mini-muscles also provided a wonderful opportunity to serve as signal sources for dexterous, intuitive control. That means there’s a ready population that could benefit from this approach.”

The Michigan team validated their technique with studies involving four participants with upper extremity amputations who had previously undergone RPNI surgery to treat neuroma pain. Each participant had a total of 3 to 9 muscle grafts implanted on nerves. Initially, the researchers measured the signals from these RPNIs using fine-wire, nickel-alloy electrodes, which were inserted through the skin into the grafts using ultrasound guidance. They measured high-amplitude electromyography signals, representing the electrical activity of the mini-muscles, when the participants imagined they were moving the fingers of their phantom hand. The ultrasound images showed the participants’ thoughts caused the associated specific mini-muscles to contract. These proof-of-concept measurements, however, were limited by the discomfort and movement of the percutaneous electrodes that pierced the skin.

Next, the team surgically implanted permanent electrodes into the RPNIs of two of the participants. They used a type of electrode commonly used for battery-powered diaphragm pacing systems, which electrically stimulate the diaphragm muscles and nerves of patients with chronic respiratory insufficiency to help regulate their breathing. These implanted electrodes allowed the researchers to measure even larger electrical signals—week after week from the same participant—by just plugging into the connector. After taking 5 to 15 minutes of calibration data, the electrical signals were translated into movement intent using machine learning algorithms and then passed on to a prosthetic hand. Both subjects were able to intuitively complete tasks like stacking physical blocks without any training—it worked on the first try just by thinking about it, says Chestek. Another key result is that the algorithm kept working even 300 days later.

“The ability to use the determined relationship between electrical activity and intended movement for a very long period of time has important practical consequences for the user of a prosthesis, because the last thing they want is to rely on a hand that is not reliable,” Clark says.

Although this clinical trial is ongoing, the Michigan team is now investigating how to replace the connector and computer card with an implantable device that communicates wirelessly, so patients can walk around in the real world. The researchers are also working to incorporate sensory feedback through the regenerative peripheral nerve interface. Their ultimate goal is for patients to feel like their prosthetic hand is alive, taking over the space in the brain where their natural hand used to be.

“People are excited because this is a novel approach that will provide high quality, intuitive, and very specific signals that can be used in a very straightforward, natural way to provide high degrees of dexterous control that are also very stable and last a long time,” Clark says.

Read the article in Science Translational Medicine.

Illustration of multiple regenerative peripheral nerve interfaces (RPNIs) created for each available nerve of an amputee. Fine-wire electrodes were embedded into his RPNI muscles during the readout session. Credit: Philip Vu/University of Michigan; Science Translational Medicine doi: 10.1126/scitranslmed.aay2857

This is a reposting of my news brief, courtesy of Materials Research Society.