A novel device designed to help stroke patients recover wrist and hand function has been approved by the US Food and Drug Administration (FDA). Called IpsiHand, the system is the first brain-computer interface (BCI) device to ever receive FDA market approval. The IpsiHand device consists of two separate parts – a wireless exoskeleton that is positioned over the wrist, and a small headpiece that records brain activity using non-invasive electroencephalography (EEG) electrodes. The system is based on a discovery made by Eric Leuthardt and colleagues at the Washington University School of Medicine over a decade ago. It is well known that each side of the brain controls movement on the opposite side of the body, so if a stroke damages motor function on the right side of the brain movement on a person's left side will be affected.
Artificial intelligence companies are developing audio transcription tools that can create searchable archives of calls and meetings, WIRED reported April 15. Artificial intelligence companies have greatly improved their automated audio transcription in recent years, and the technology is now able to produce transcripts with impressive accuracy, according to WIRED. One example is Stedi, a company that makes business-to-business software. It developed a tool called Rewatch that records meetings and uses voice-dictation AI to transcribe it, providing employees with a searchable record of everything said during the meeting. AI companies Otter.ai and Trint also offer voice-dictation to produce meeting transcripts, and Zoom has built-in wares that offer meeting notes.
Mapping of spatial hotspots, i.e., regions with significantly higher rates or probability density of generating certain events (e.g., disease or crime cases), is a important task in diverse societal domains, including public health, public safety, transportation, agriculture, environmental science, etc. Clustering techniques required by these domains differ from traditional clustering methods due to the high economic and social costs of spurious results (e.g., false alarms of crime clusters). As a result, statistical rigor is needed explicitly to control the rate of spurious detections. To address this challenge, techniques for statistically-robust clustering have been extensively studied by the data mining and statistics communities. In this survey we present an up-to-date and detailed review of the models and algorithms developed by this field. We first present a general taxonomy of the clustering process with statistical rigor, covering key steps of data and statistical modeling, region enumeration and maximization, significance testing, and data update. We further discuss different paradigms and methods within each of key steps. Finally, we highlight research gaps and potential future directions, which may serve as a stepping stone in generating new ideas and thoughts in this growing field and beyond.
Imbio has gained FDA 510(k) clearance for its RV/LV AnalysisTM algorithm, a leading supplier of artificial intelligence (AI) solutions for medical imaging evaluation. The RV/LV Analysis algorithm is a quick and easy way to check for right ventricular dilation. The tool efficiently and precisely evaluates the heart's ventricles to calculate the proportion of the right to left ventricle's maximum diameter. The RV/LV Analysis results are readily accessible for clinicians without any extra work, including a detailed report of quantitative findings directly attached to the patient imaging study in minutes. David Hannes, Imbio Chief Executive Officer, stated that their automated RV/LV Assessment has the control to supply factual information and notify risk stratification in many acute cases. Imbio is proud to offer this AI-driven algorithm to physicians and partners to support acute cases and facilitate critical treatment decisions for patients.
In analyzing the news and media landscape, the report states that the nature of subscription services has changed, and will continue to evolve as consumers will be asked to pay for virtual fashion, experiences and games. The report cites subscription service platform Zuora that the media business has an average subscription dropout rate of 34 percent, the highest of any industry sector studied. It stresses that local newspapers are not just competing with the likes of The Washington Post or The New York Times, but every audience-funded business.
Human mortality patterns and trajectories in closely related populations are likely linked together and share similarities. It is always desirable to model them simultaneously while taking their heterogeneity into account. This paper introduces two new models for joint mortality modelling and forecasting multiple subpopulations in adaptations of the multivariate functional principal component analysis techniques. The first model extends the independent functional data model to a multi-population modelling setting. In the second one, we propose a novel multivariate functional principal component method for coherent modelling. Its design primarily fulfils the idea that when several subpopulation groups have similar socio-economic conditions or common biological characteristics, such close connections are expected to evolve in a non-diverging fashion. We demonstrate the proposed methods by using sex-specific mortality data. Their forecast performances are further compared with several existing models, including the independent functional data model and the Product-Ratio model, through comparisons with mortality data of ten developed countries. Our experiment results show that the first proposed model maintains a comparable forecast ability with the existing methods. In contrast, the second proposed model outperforms the first model as well as the current models in terms of forecast accuracy, in addition to several desirable properties.
With my co-authors Pablo Torres, Sergio Hoyas (both from Instituto Universitario de Matemática Pura y Aplicada, Universitat Politécnica de Valencia, Spain) and Ricardo Vinuesa (from Engineering Mechanics, KTH Royal Institute of Technology, Sweden), we have written a book chapter which focuses on the key role of machine learning (ML) methods to analyze air quality and air flow in urban environments (especially in dense cities) which might be an indicator of public health . We have provided a review of the ML methods used in this field and we have highlighted the relevance of the urban air quality and air flow to the number of hospitalizations and respiratory diseases as they were reported in the literature. Here is a mini lecture which summarizes our book chapter in a video .
During the COVID-19 pandemic, a significant effort has gone into developing ML-driven epidemic forecasting techniques. However, benchmarks do not exist to claim if a new AI/ML technique is better than the existing ones. The "covid-forecast-hub" is a collection of more than 30 teams, including us, that submit their forecasts weekly to the CDC. It is not possible to declare whether one method is better than the other using those forecasts because each team's submission may correspond to different techniques over the period and involve human interventions as the teams are continuously changing/tuning their approach. Such forecasts may be considered "human-expert" forecasts and do not qualify as AI/ML approaches, although they can be used as an indicator of human expert performance. We are interested in supporting AI/ML research in epidemic forecasting which can lead to scalable forecasting without human intervention. Which modeling technique, learning strategy, and data pre-processing technique work well for epidemic forecasting is still an open problem. To help advance the state-of-the-art AI/ML applied to epidemiology, a benchmark with a collection of performance points is needed and the current "state-of-the-art" techniques need to be identified. We propose EpiBench a platform consisting of community-driven benchmarks for AI/ML applied to epidemic forecasting to standardize the challenge with a uniform evaluation protocol. In this paper, we introduce a prototype of EpiBench which is currently running and accepting submissions for the task of forecasting COVID-19 cases and deaths in the US states and We demonstrate that we can utilize the prototype to develop an ensemble relying on fully automated epidemic forecasts (no human intervention) that reaches human-expert level ensemble currently being used by the CDC.