From breast cancer For brain aneurysms, artificial intelligence continues to impose itself as a valuable diagnostic tool. Researchers at Stanford University have has created predictive AI to detect the likelihood of aneurysms during brain scans with great precision.
Although relatively rare, brain aneurysms present suddenly and have a very short time for treatment before becoming fatal. According to the Foundation of Brain AneurysmsAbout 30,000 people in the United States suffer from this disease each year, with 15% going to the hospital and 40% of all cases resulting in death. Of the survivors, 66% suffer from a decrease in neurological function. This constitutes an accurate and early diagnosis of the most important factor in the prevention of cerebral aneurysm, but this has proven to be a very difficult task for health professionals.
Research at Stanford University has recognized the difficulty of this problem and created a tool to solve it. Kristen Yeom, associate professor of radiology and co-lead author of the paper, explains why AI is an essential part of the diagnostic process:
Searching for an aneurysm is one of the most critical and intensive tasks. Given the significant challenges posed by complex neurovascular anatomy and the possible fatal outcome of a missed aneurysm, this has prompted me to apply advances in computer science and vision to neuroimaging.
Brain aneurysms occur when the wall of a brain artery swells, but the real problems do not begin until these bulges begin to flow or burst. The larger they become, the more difficult the treatment becomes if they break. While other diagnostic efforts seek to detect the signs of a disease before it can noticeably manifest themselves, brain aneurysms have various origins, ranging from addiction to cancer, going through various issues related to blood. The main problem remains the detection of existing aneurysms. Before breaking, cerebral aneurysms usually do not require any symptoms and their removal requires corrective surgery. This leads to a careful and thorough diagnostic process, with clinicians not wishing to undergo brain surgery without the assurance of its necessity.
The results of the creation of their artificial intelligence tool HeadXNet. The importance of avoiding misdiagnosis meant that HeadXNet could not exercise undue influence over the physician's decisions. In addition, cerebral sweeps are complete three-dimensional models, much more complex than flat images that convolutional neural networks are generally trained to understand. To solve these problems, the Yeom team manually tagged each voxel in the learning data to specify whether it contained an aneurysm. After the training, HeadXNet provided its answer only in the form of an overlay to locate the locations of the brain with the highest probability of aneurysm without actually exerting an additional influence likely to occur. ############################################################################ 39, influence a misdiagnosis.
The Yeom team tested HeadXNet with eight clinicians and 115 brain exams. This little test gave positive results. Using this tool, clinicians have correctly identified more aneurysms and reduced the number of diagnostic discrepancies between them. Even with these promising results, HeadXNet will not be part of the diagnostic process in the near future. Beyond the need to continue developments and testing to ensure its safe use in larger populations, brain content readers are not designed to integrate with such machine learning technologies as than HeadXNet. The widespread use of this technology requires more data, testing and development before the general population can benefit.
HeadXNet nonetheless represents a significant step forward in the resolution process of a complicated and fatal problem of underfunded research. It also demonstrates the greatest benefit of using artificial intelligence as a collaborative partner and not a replacement for the human. With the right motivations and the right implementations, the AI has a lot to offer to humanity. Although we must also consider the worstwe continue to see the results of hoping for the best.
Top picture credit: Storyblocks
Modern artificial intelligence uses complex algorithms to perform all kinds of tasks in an instant, for example to determine the feeling of a client based on his or her examination or to identify specific features of an image . However, the brightest moments of artificial intelligence come from the creativity with which we use these algorithms. People used the AI for generate new sports, transform scribbles into realistic landscapesand now, MIT has found a way to detect breast cancer up to five years in advance using an in-depth image classification model.
The MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Massachusetts General Hospital (MGH) have used mammograms and known results from more than 60,000 patients to form their new model to the smallest visual detail from the human eye. Well-trained doctors do not miss these predictive patterns simply because they may seem too small to be noticed, but because more subtle patterns just do not attract enough attention. An image classification model that can categorize thousands of scans down to the smallest detail can quickly solve this daunting task.
Regina Barzilay, a professor at MIT (and breast cancer survivor), explains how this new model can improve treatment plans:
Rather than taking a single approach, we can customize the screening for cancer risk in women. For example, a doctor might recommend to a group of women to have a mammogram every two years, while another higher-risk group could undergo additional MRI screening.
When doctors can order mammograms according to the needs of the patient, they can avoid unnecessary exposure to radiation and the cost of potentially unnecessary examinations. Although existing models can accurately identify 18% of patients in the high-risk category, this new model increases this number by up to 31%. Its success is based on the team's approach to its development. For the first time, a model of breast cancer prevention targets women individually. It also takes into account racial diversity, where earlier models focused mainly on white populations. This not only contributes to increased accuracy, but significantly reduces the breast cancer mortality rate among African-American women.
As demonstrated by MIT and HGM, well-trained image classification models can help doctors save lives. Although no AI gives perfect results, image classification algorithms have matured and become reliable in many applications, especially in specific models such as this one. You need a little more than a good idea, relevant data and a little time to create a successful image recognition model. Services like Clarifai, Microsoft Azure, IBM Watson, Vize and others offer free, bespoke customized training platforms that require no programming skills. Thanks to these algorithms, accessible to all, we have all the necessary resources to train AI to solve problems and help others. It takes time and care to safely integrate a successful experience into the practice of diagnostic medicine; This approach will likely see many revisions as it develops outside of a single hospital. But the first results are promising.
Antibiotics were miracle drugs for most of the twentieth century, but they are no longer a magic bullet for treating any infection. The overuse of antibiotics has promoted the proliferation of life-threatening resistant organisms, particularly for those whose immune systems are already weakened. In the UK, a teenager was on the verge of death after a transplant, but a last effort using genetically modified viruses you saved his life. Doctors say that it is a decisive moment for the use of so-called bacteriophages in medicine.
Isabelle Carnell-Holdaway, 17, started having problems when she was small. The doctors diagnosed him with cystic fibrosis, a genetic disease that halves life expectancy. The only long-term treatment for cystic fibrosis is a lung transplant. Carnell-Holdaway was able to get a transplant at age 15, but a low-grade chronic infection flourished during convalescence and immunosuppressive medication. The body, a strain of Mycobacterium Abcessus, has been proven resistant to all antibiotic treatments.
After exhausting all conventional treatment options, doctors turned to bacteriophages. These microscopic viruses Do not infect human cells, but they are potentially deadly to bacteria. Doctors have used bacteriophages to treat infections in the past, but it is difficult and time-consuming to find phage strains that can effectively fight an infection. In this case, the doctors had access to a library of 15,000 phages gathered for a research project.
The team identified a phage called "Muddy" as the best candidate (see above). It was discovered in 2010 attacking bacteria in a decaying eggplant. The doctors discovered two other phages that could help and altered their tiny genomes, thus making them more similar to Muddy – virulent and deadly. Mycobacterium Abcessus.
As of June 2018, physicians administered the mixture of three bacteriophages to Carnell-Holdaway twice daily. In the following months, the infection decreased until it disappeared completely. It should be noted that Carnell-Holdaway's body has not been hit by billions of phage particles each day.
Doctors ensure that their patient is not "cured". It still carries the dangerous bacteria and the return of the treatment to the bacteria could be advanced if the treatment is interrupted too early. For the moment, Carnell-Holdaway continues to receive daily injections of the phage cocktail. However, she was able to resume her normal daily routine without the disabling effects of cystic fibrosis. The results of his treatment were published in the journal Nature.
There is no fountain of youth to go back in time, but it may be possible to remember the ravages of the age. An ESA experiment that has just arrived on the International Space Station (ISS) is going test nanoparticles as a way to eliminate the body from free radicals. This could prevent some of the cellular damage associated with aging, but it could also help astronauts on long-term space missions to stay healthy.
There is no single cause for aging, but free radicals are an essential piece of the puzzle. A free radical is just a molecule with an unpaired electron in its outer layer. They are very reactive, which means that they will steal electrons from other molecules, which may prevent them from working. Over time, this causes cellular damage known as oxidative stress associated with aging. It turns out that similar constraints affect astronauts in space.
Doctors advise people to make sure that their diet contains antioxidants such as vitamin A, vitamin C and beta-carotene to neutralize some of these molecules. These substances can not neutralize antioxidants forever, so you must continue to take them. The European Space Agency (ESA) Nano Antioxidants will test a type of ceramic nanoparticle called "nanoceria" (the green dots above) to see if it absorbs free radicals any longer in order to reduce the heart risk. Parkinson's disease and muscle loss.
The nanoceres have enzymatic activity inside the cells, allowing them to neutralize free radicals for weeks between doses. It's much longer than natural antioxidants. Short-term studies have shown that nanocerias protect living cells from oxidative stress. In 2017, researchers discovered that the particles remained stable and could protect the muscle cells aboard the ISS. The new study will track the effects of nanoceria longer.
The nanoceria and the host cells are housed in a device called the Kubik Incubator. It keeps the samples at a constant temperature of 30 degrees Celsius (86 degrees Fahrenheit). The experience lasts six days, twice as long as the last. After that, the ISS crew will freeze the samples for further analysis on Earth. Scientists will compare samples of space to controls that did not go into space. The comparison should help the team determine the effects of microgravity and cosmic radiation on nanoceria.
Someday, ESA may be able to use nanoceria as a supplement protecting astronauts from cellular damage associated with free radicals. Here on Earth, nanoceria could also contain oxidative stress and reduce the incidence of age-related diseases.
A laser tenth of solar power on Earth officially debuted in March when Romanian researchers conducted the first successful test at 10 petawatts (PW). The laser is one of three of an international project in Europe called Extreme Light Infrastructure. To date, it is the most powerful laser ever built and the most concentrated power on the planet.
It is hard to exaggerate the enormity of 10PW, which equates to 10 trillion watts. It's only a few years ago that this site was referring to a simple 1PW laser like a death star. The new is 10 times stronger. In comparison, laser pointers sold in the United States are limited to a maximum of 0.005 watts for security reasons.
The laser in question is part of the Extremely Light Infrastructure Project (ELI), an effort initiated by scientists in Europe in the mid-2000s and led by French scientist and Nobel laureate Gerard Mourou. The project aims to advance not only the research infrastructure for lasers, but also applied sciences.
The program was funded by the European Commission and in just a few years, three countries have been selected to host three new lasers: Romania, Hungary and the Czech Republic. At present, the project has received more than 850 million euros, mainly from the European Regional Development Fund.
The 10PW laser resides in a newly built lab called Extreme Light Infrastructure – Nuclear Physics installation (ELI-NP) in a town called Măgurele, not far from the capital Bucharest. ELI-NP is dedicated to the study of photonuclear physics and its applications. The other two laboratories are called ELI-Beamlines in the Czech Republic, focusing on secondary sources of radiation and short-pulse particles, and ELI-ALPS or Attosecond Light Pulse Source in Hungary.
With 10 PW of power, scientists can literally vaporize matter, opening up new perspectives on what happens during a supernova. This is only one example, albeit rather epic. This type of power in a laser also helps to study how heavy metals are formed.
In terms of more practical research, ELI-NP will work to advance medical research on proton cancer treatment as well as research into new methods of treating radioactive waste. This could also help to create new ways to find and characterize nuclear material, allowing security teams to analyze, for example, incoming shipping containers in search of dangerous and illegal content. The formal research phase of the project is scheduled to begin in early 2020.
The power of lasers has increased in recent decades. "The laws of light-matter interaction change fundamentally because of the predominance of relativistic effects in the dynamics of charged particles under the influence of laser light," according to ELI. From this fundamental change, scientists can develop new methods for generating X-rays, gamma rays, and high-energy particles. These new methods, in turn, open up new opportunities for use in different scientific fields, whether it is medical research or materials science.
As much as anyone would want to imagine a giant satellite antenna pulling from its center (ahem, Death Star), the reality of a 10PW laser is a little more mundane. The laser itself is inside a room and the scientists who operate it are of course behind a computer.
For the laser to have both power and precision, it relies on two systems working together. One is the high-powered laser system, which itself includes two laser arms. They are what the laser pulses deliver. The second piece is the laser beam transport system, which directs the pulses where they need to go with micrometric precision. This second component is not a laser at all, but rather "one-meter wide adjustable aperture mirrors installed in a vacuum system of pipes and housings", according to ELI-NP information. The entire system requires a highly controlled environment with respect to air quality and vibration, at least one image in a clean room, in the scientific sense of the term.
If we could take a look at the inside of the protective chamber while the laser was working, the beam would be visible to the human eye and reddish, although it is close of the infrared radiation limit, according to Dr. Nicolae Zamfir, project manager at ELI-NP. A high energy laser pump would be visible to the eye, too, appearing in green.
Mr. Zamfir also explained the size of the laser beam, about 60 cm or just under two feet in diameter, and the area that he can target: "The beam is focused on mm2 only in the Interaction (chamber), "he added.
If everything goes as planned, the ELI-NP 10PW laser will be even more powerful. According to optics.org (as well as a Job offer for an engineer at ELI-NP – a simple bachelor's degree required, two 10PW lasers will combine together "deliver focused laser intensities up to 1023 watts per square centimeter, at a wavelength of 820 nanometers and a pulse duration of 25 femtoseconds. "The intensity of the current is 1015.
In addition to the three sites in Romania, Hungary and the Czech Republic, ELI plans to create a fourth installation and a laser, with a location to be determined. This should be an order of magnitude stronger than that of Romania.
Top image credit: Getty Images
As the AI hype cycle is built, we have been treated to a plethora of claims about the improvements and breakthroughs that technology can bring. One of the most fundamental – and potentially important – has been the idea that we can use AI to find new drugs and treatments for existing conditions for which current options have been lacking.
This promise has himself now come short. IBM has announced that it will stop selling its Watson AI system as a drug discovery tool. This is a well-publicized retreat for the company, which has aggressively commercialized artificial intelligence, which has proven useful for these purposes and has encountered problems last year when reports indicated that his systems had formulated inappropriate and dangerous recommendations for patients with effect).
While IBM cites slowing sales as a reason for withdrawal, deeper issues are potentially responsible. A recent deep diving by IEEE Spectrum puts the context around these issues. Result: After years of work and a number of moonshot projects, IBM has remarkably little to offer for its efforts. And the company has created some ill-will toward itself, writes IEEE, because it has adopted an aggressive, marketing-focused approach, focused on AI and Watson, promising grandiose achievements that did not accurately describe what the system could really achieve.
Watson seduced the world with his performance on Jeopardy and his ability to analyze the relationships between words rather than treat them as search terms. In theory, Watson could use his engine to sort quantities of medical data in the same way, finding the hidden signal in a system full of noise. Reality did not cooperate. Watson, from IBM, has not been among the few researches into the use of AI to improve outcomes for patients.
The IEEE party is struggling to note that IBM has had to face tremendous challenges in trying to put its artificial intelligence program online and used it effectively for medicine human. Nothing like Watson (or what Watson was supposed to be) has ever existed before. Nobody knew how to build it. Yoshua Bengio, an Artificial Intelligence Researcher at the University of Montreal, summarized efforts to help AI understand medical texts and terminology in the following way: "Our NLP has seen a tremendous improvement compared to five years ago humans. "
Watson's problem was not that it did not work. The problem is that Watson is not doing the right thing Things. Although he quickly learned to ingest and process large amounts of data, he had a hard time identifying the pieces of information contained in a study that could lead physicians to actually change their process. care. This is especially true if the relevant information was incidental to the main subject of the research.
Because patients' data was not always properly formatted or even chronologically ordered, the software was unable to understand the patient history. And the system was unable to compare new cancer patients with old patient databases to uncover hidden treatment patterns, as such practices would not be considered evidence-based. Making a strong recommendation from an evidence-based medicine requires double-blind studies, meta-analyzes, and analyzes of systemic evidence, not an AI system claiming to have found a similarity between different types of patients .
It is not clear what is next for Watson, if anything. The tool has had some success in narrow and customized applications with less ambiguity. But despite the dozens of planned initiatives, waves of media hype and considerable investment, IBM's Watson for Drug Discovery has clearly missed its own goals.