Like any tool, technology can be used for both good and bad. And sometimes, that bad is inadvertent; tech in the form of airplanes helped expedite the spread of the coronavirus around the world. But fortunately, technology will also aid in stopping this pandemic crisis. A few weeks ago, we wrote about how the San Francisco-based company BlueDot utilized artificial intelligence (AI) to warn the general public about the dangers of COVID-19 well ahead of health officials. In case you missed it, you can read it here.
Functional magnetic resonance imaging (fMRI) enables measuring human brain activity, in vivo. Yet, the fMRI hemodynamic response unfolds over very slow timescales ( 0.1-1 Hz), orders of magnitude slower than millisecond timescales of neural spiking. It is unclear, therefore, if slow dynamics as measured with fMRI are relevant for cognitive function. We investigated this question with a novel application of Gaussian Process Factor Analysis (GPFA) and machine learning to fMRI data. We analyzed slowly sampled (1.4 Hz) fMRI data from 1000 healthy human participants (Human Connectome Project database), and applied GPFA to reduce dimensionality and extract smooth latent dynamics.
A Data Science Technology Company helping enterprises harness their data and build AI-driven innovative solutions. Are you sure you want to view these Tweets? This #MachineLearning use case provides an in-depth analysis of a Transit system in San Francisco Bay Area. These insights will help the organization to smoothly plan and evaluate its services. If your #ATMs are down, what are the chances of your customers switching to your competitors?
The traditional medical methods or tools used by medical practitioners in identifying the existence of Alzheimer's have less accuracy, reliability, and scalability. While following traditional memory assessment tests, they may commit errors or make biased decisions, based on their previous work experience. To test the accuracy levels of prediction between humans and machines, world-leading medical universities conducted a study. And before conducting the study, machines are fed and trained with various images indicating the presence and absence of the disease. To process images and detect the presence of Alzheimer's, expert pathologist took around twenty minutes and identified with 68% accuracy.
DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence (AI) in Alzheimer's Applications" report has been added to ResearchAndMarkets.com's offering. Artificial intelligence (AI) is a term used to identify a scientific field that covers the creation of machines aimed at reproducing wholly or in part the intelligent behavior of human beings. These machines include computers, sensors, robots, and hypersmart devices. As shown in the figure below, the ultimate purpose of artificial intelligence is to create smart machines that, through the steps of learning, reasoning, and self-correcting, will eventually be able to make decisions, solve problems, and act like human beings.
There are currently developed algorithms for fractures and malignancies detection based on X-ray, CT and MRI image recognition. Also, there are a number of commercialized AI algorithms for predicting injury patterns and predicting postoperative complications following orthopedic and trauma procedures. This is one of the medical fields with the most abundant AI implementation. AI algorithms are being used for suicide prediction and for depression and anxiety treatment, a feature performed by chatbots. Used for early diagnostics of chronic diseases such as Multiple sclerosis, Alzheimer's disease, and Parkinson's disease, and for a number of acute neurological diseases such as brain tissue ischemia, intracranial hemorrhage, and hydrocephalus.
A new device that could spot the early signs of Alzheimer's is currently being developed. The Early Detection of Neurodegenerative diseases (Edon) project is being supported by Alzheimer's Research UK, and has already won funding from Microsoft's co-founder, Bill Gates. Researchers will start by analysing data from studies into the condition, which will then be used to design a prototype for a wearable design (like a smartwatch), in the next three years. The wearable device will collect data like gait, heart rate and sleep patterns, and scientists hope they can use it to spot the condition, years before symptoms appear. This is extremely important as advanced Alzheimer's is generally irreversible, which makes early diagnosis crucial.
Here is our annual list of technological advances that we believe will make a real difference in solving important problems. We avoid the one-off tricks, the overhyped new gadgets. Instead we look for those breakthroughs that will truly change how we live and work. We're excited to announce that with this year's list we're also launching our very first editorial podcast, Deep Tech, which will explore the the people, places, and ideas featured in our most ambitious journalism. Later this year, Dutch researchers will complete a quantum internet between Delft and the Hague. An internet based on quantum physics will soon enable inherently secure communication. A team led by Stephanie Wehner, at Delft University of Technology, is building a network connecting four cities in the Netherlands entirely by means of quantum technology.
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for segmentation tasks in computer vision and medical imaging. Recently, computational blocks termed squeeze and excitation (SE) have been introduced to recalibrate F-CNN feature maps both channel- and spatial-wise, boosting segmentation performance while only minimally increasing the model complexity. So far, the development of SE blocks has focused on 2D architectures. For volumetric medical images, however, 3D F-CNNs are a natural choice. In this article, we extend existing 2D recalibration methods to 3D and propose a generic compress-process-recalibrate pipeline for easy comparison of such blocks. We further introduce Project & Excite (PE) modules, customized for 3D networks. In contrast to existing modules, Project \& Excite does not perform global average pooling but compresses feature maps along different spatial dimensions of the tensor separately to retain more spatial information that is subsequently used in the excitation step. We evaluate the modules on two challenging tasks, whole-brain segmentation of MRI scans and whole-body segmentation of CT scans. We demonstrate that PE modules can be easily integrated into 3D F-CNNs, boosting performance up to 0.3 in Dice Score and outperforming 3D extensions of other recalibration blocks, while only marginally increasing the model complexity. Our code is publicly available on https://github.com/ai-med/squeeze_and_excitation .
Artificial Neural Networks (ANNs) implement a specific form of multi-variate extrapolation and will generate an output for any input pattern, even when there is no similar training pattern. Extrapolations are not necessarily to be trusted, and in order to support safety critical systems, we require such systems to give an indication of the training sample related uncertainty associated with their output. Some readers may think that this is a well known issue which is already covered by the basic principles of pattern recognition. We will explain below how this is not the case and how the conventional (Likelihood estimate of) conditional probability of classification does not correctly assess this uncertainty. We provide a discussion of the standard interpretations of this problem and show how a quantitative approach based upon long standing methods can be practically applied. The methods are illustrated on the task of early diagnosis of dementing diseases using Magnetic Resonance Imaging.