Let's face it, we humans make a lot of bad decisions. And even when we are deeply aware that our decisions are hurting ourselves -- like destroying our environment or propagating inequality -- we seem collectively helpless to correct course. It is exasperating, like watching a car heading for a brick wall with a driver that seems unwilling or unable to turn the wheel. Ironically, as individuals, we are not nearly as dysfunctional, most of us turning the wheel as needed to navigate our daily lives. But when groups are involved, with many people grabbing the wheel at once, we often find ourselves in a fruitless stalemate headed for disaster, or worse, lurching off the road and into a ditch, seemingly just to spite ourselves.
Exoskeleton devices work, researchers say, for a variety of uses such as speeding up our walking or making running easier. Yet they don't know what exactly makes exoskeletons effective. What is the benefit of customization, for example? And how much does simply getting used to the exoskeleton matter? Researchers in the Stanford Biomechatronics Laboratory at Stanford University examined these questions and found that training plays a remarkably significant role in how well exoskeletons provide assistance.
Artificial intelligence is transforming industries around the world -- and health care is no exception. A recent Mayo Clinic study found that AI-enhanced electrocardiograms (ECGs) have the potential to save lives by speeding diagnosis and treatment in patients with heart failure who are seen in the emergency room. The lead author of the study is Demilade "Demi" Adedinsewo, a noninvasive cardiologist at the Mayo Clinic who is actively integrating the latest AI advancements into cardiac care and drawing largely on her learning experience with MIT Professional Education. A dedicated practitioner, Adedinsewo is a Mayo Clinic Florida Women's Health Scholar and director of research for the Cardiovascular Disease Fellowship program. Her clinical research interests include cardiovascular disease prevention, women's heart health, cardiovascular health disparities, and the use of digital tools in cardiovascular disease management.
Suppose you ran a randomized experiment. For example, you rolled out a new feature of your product to a random subset of your customers and measured customer retention. Or to take an example from public policy, some randomly selected individuals in a city were offered a free (vegan) sausage if they get vaccinated against Covid-19. After enduring the blood, sweat, and tears of data collection and cleaning, you finally calculate the average treatment effect (ATE) by comparing average outcomes in the treatment and control group. Individuals in the treatment group were 10% more likely to be vaccinated -- hooray!
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that affects several brain regions in a distinctive propagation pattern, with emphasis on the motor neurons.1 To diagnose ALS as early as possible is a task of high clinical relevance for the optimized patients' care and the opportunity to be enrolled in clinical trials. With advances in neuroimaging in neurodegenerative diseases like ALS,2,3 it has been speculated that cerebral magnetic resonance imaging (MRI) may be able to provide insights that support an early diagnosis. Multiparametric, quantitative MRI has been discussed as a way to achieve a composite neuroimaging index.3 However, the amount of biomarkers, as well as their (non-linear) interactions, makes a straightforward approach likely unsuccessful. Machine learning (ML) might be the missing piece to integrate multiparametric MRI data into a useful classifier.4
Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. However, obtaining example registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for unsupervised affine and deformable image registration. In the DLIR framework ConvNets are trained for image registration by exploiting image similarity analogous to conventional intensity-based image registration. After a ConvNet has been trained with the DLIR framework, it can be used to register pairs of unseen images in one shot. We propose flexible ConvNets designs for affine image registration and for deformable image registration. By stacking multiple of these ConvNets into a larger architecture, we are able to perform coarse-to-fine image registration. We show for registration of cardiac cine MRI and registration of chest CT that performance of the DLIR framework is comparable to conventional image registration while being several orders of magnitude faster.",
The growing popularity of 3D printing for manufacturing all sorts of items, from customized medical devices to affordable homes, has created more demand for new 3D printing materials designed for very specific uses. To cut down on the time it takes to discover these new materials, researchers at MIT have developed a data-driven process that uses machine learning to optimize new 3D printing materials with multiple characteristics, like toughness and compression strength. By streamlining materials development, the system lowers costs and lessens the environmental impact by reducing the amount of chemical waste. The machine learning algorithm could also spur innovation by suggesting unique chemical formulations that human intuition might miss. "Materials development is still very much a manual process. A chemist goes into a lab, mixes ingredients by hand, makes samples, tests them, and comes to a final formulation. But rather than having a chemist who can only do a couple of iterations over a span of days, our system can do hundreds of iterations over the same time span," says Mike Foshey, a mechanical engineer and project manager in the Computational Design and Fabrication Group (CDFG) of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and co-lead author of the paper.
Recommender systems attempt to identify and recommend the most preferable item (product-service) to individual users. These systems predict user interest in items based on related items, users, and the interactions between items and users. We aim to build an auto-routine and color scheme recommender system for home-based smart lighting that leverages a wealth of historical data and machine learning methods. We utilize an unsupervised method to recommend a routine for smart lighting. Moreover, by analyzing users’ daily logs, geographical location, temporal and usage information, we understand user preferences and predict their preferred light colors. To do so, users are clustered based on their geographical information and usage distribution. We then build and train a predictive model within each cluster and aggregate the results. Results indicate that models based on similar users increases the prediction accuracy, with and without prior knowledge about user preferences.
Researchers at King's College Hospital and Queen Mary University of London have developed an AI algorithm which can prescribe the most effective treatment plan for patients diagnosed with primary liver cancer. The computer-based algorithm, named Drug Ranking Using Machine Learning (DRUML), classifies drugs used to treat bile duct cancer (a type of primary liver cancer), based on their efficacy in reducing cancer cell growth. The research into DRUML was recently published in Cancer Research, an American Association of Cancer Research journal. Researchers say that the software could be used in the future to predict individual patient responses to therapies to enable them to select the most effective treatment plan. Professor Pedro Cutillas, researcher at Queen Mary University of London, said: "Patients who are diagnosed with primary liver cancer often have a very poor prognosis. Hence why a one-size-fits-all approach to treatment is not the most effective way to reduce cancer cell growth and why we applied DRUML to this type of cancer."
Heart failure (HF) is a leading cause of death. Early intervention is the key to reduce HF-related morbidity and mortality. Data from the baseline visits (1987–89) of the Atherosclerosis Risk in Communities (ARIC) study was used. Incident hospitalized HF events were ascertained by ICD codes. Participants with good quality baseline ECGs were included. Participants with prevalent HF were excluded. ECG-artificial intelligence (AI) model to predict HF was created as a deep residual convolutional neural network (CNN) utilizing standard 12-lead ECG. The area under the receiver operating characteristic curve (AUC) was used to evaluate prediction models including (CNN), light gradient boosting machines (LGBM), and Cox proportional hazards regression. A total of 14 613 (45% male, 73% of white, mean age standard deviation of 54 5) participants were eligible.