You die at the beginning of Mass Effect 2. It's 2183, and you--Commander Shepard--have just saved every space-faring species in the Milky Way from an extra-galactic threat. In the resulting explosion, you're flung into the void, drifting as you struggle to breathe. The military logs you as "killed in action." But of course, a deceased protagonist does not a sequel make. Your corpse is soon found and brought back to life.
Quantum computing pioneer D-Wave Systems today announced a new business unit – Quadrant – to provide machine learning services based on lessons from its quantum computing research. Quadrant will specialize in the use of generative learning models which require smaller sets of labelled data to generate models than typical discriminative methods. As a proof point of the approach's power, D-Wave is calling attention to its winning effort in a recent Siemens medical imaging grand challenge – CATARACT – to automate identification of surgical instruments used in cataract surgery. "D-Wave is committed to tackling real-world problems, today. Quadrant is a natural extension of the scientific and technological advances from D-Wave as we continue to explore new applications for our quantum systems," said Vern Brownell, CEO at D-Wave in today's announcement.
Hospitals have to solve a thousand logistical challenges every day, but perhaps none are more difficult than operating room schedules. Surgeries can be difficult to predict -- in fact, less than half of surgeries in the U.S. start and end on time. That can create chaos for patients and doctors, and costs hospitals $5.2 billion every year, according to University of Washington spinout Perimatics. The startup, which develops a variety of technologies for hospitals, is taking aim at the operating room problem with a new AI technology that uses data on patients and surgeons to more accurately predict how long each surgery will take. The startup recently deployed the technology at a large academic medical institution in Seattle.
From Quadrant (a D-Wave business), this whitepaper "Data-Efficient Machine Learning" describes a practical impediment to the application of deep neural network models when large training data sets are unavailable. Encouragingly however, it is shown that recent machine learning advances make it possible to obtain the benefits of deep neural networks by making more efficient use of training data that most practitioners do have. Quadrant leverages generative machine learning, which requires much less labeled data than common discriminative models. This is incredibly useful in countless applications, including medical imaging which is often limited to relatively small data sets (i.e. For a first case study, Siemens Healthineers partnered with Quadrant to identify surgical tools used in cataract surgery with 99.71% accuracy.
Artificial intelligence could prove to be a self-running growth engine for the health care sector in the not-so-distant future. A recent report from Accenture analyzed the "near-term value" of AI applications in health care to determine how the potential impact of the technology stacks up against the upfront costs of implementation. Results from the report estimated that AI applications in health care could save up to $150 billion annually for the U.S. health care economy by 2026. The report focused on 10 AI applications with potential for near-term impact in medicine and analyzed each application to derive an associated estimated value. Researchers considered the impact of each application, likelihood of adoption, and value to the health economy in their evaluation.
The presence of robots in our hospitals may once have been the preserve of science fiction, but in recent years the ongoing development of robotics technology has led some to suggest that it may now be closer than we think. Could 2018 be the year that we see robots begin to replace human surgeons? Surgical robotics, in one form or another, has actually been in use for longer than you might think. In the United States, the Food and Drug Administration approved an early robotic surgical technology, called the da Vinci Surgical System, consisting of mechanical arms operated by a human surgeon, back in 2000 . Since then new developments and an increasing complexity in the field of robotics has led to more and more devices and tools finding their way into operating theatres, to such an extent that the future for robotic surgery looks extremely bright.
Abstract: "Tendon-transfer surgeries are performed for a variety of conditions such as stroke, palsies, trauma, and congenital defects. The surgery involves re-routing a tendon from a nonfunctioning muscle to a functioning muscle to partially restore lost function. However, a fundamental aspect of the current surgery, namely the suture that attaches the tendon(s) to the muscles, can lead to poor post-surgery function. For example, in the hand tendon-transfer surgery for high median-ulnar palsy, one muscle is sutured to all four finger flexor tendons. This couples finger movement, prevents the fingers from adapting to an object's shape while grasping, and leads to poor hand function overall.
If you think your on-the-job training was tough, imagine what life is like for newbie surgeons. Under the supervision of a veteran doctor, known as an attending, trainees help operate on a real live human, who might have a spouse and kids--and, if something goes awry, a very angry lawyer. Now add to the mix the da Vinci robotic surgery system, which operators control from across the room, precisely guiding instruments from a specially-designed console. In traditional surgery, the resident gets hands-on action, holding back tissue, for instance. Robotic systems might have two control consoles, but attendings rarely grant residents simultaneous control.
It was the last question of the night and it hushed the entire room. An entrepreneur expressed his aggravation about the FDA's antiquated regulatory environment for AI-enabled devices to Dr. Joel Stein of Columbia University. Stein a leader in rehabilitative robotic medicine, sympathized with the startup knowing full well that tomorrow's exoskeletons will rely heavily on machine intelligence. Nodding her head in agreement, Kate Merton of JLabs shared the sentiment. Her employer, Johnson & Johnson, is partnered with Google to revolutionize the operating room through embedded deep learning systems.
When we think of breakthroughs in healthcare, we often conjure images of heroic interventions -- the first organ transplantation, robotic surgery, and so on. But in fact many of the greatest leaps in human health have come from far more prosaic interventions -- the safe disposal of human excrement through sewage and sanitation, for example, or handwashing during births and caesarians.