If you wanted to simplify, CES 2019 could be neatly divided into self-driving cars, giant TVs and health tech. A lot of what we've seen of the latter either tracks your movement or is hardware ingrained into workouts. Spryng is a little different, aimed at speeding up your recovery, reducing muscle soreness by stimulating blood circulation. While looking like an unassuming pair of chunky shinguards, these tech-laced wraps expand and contract around your calves, improving blood flow and feeling very nice in the process. The startup showcased the portable muscle recovery tool at CES, and it's backed up by several scientific papers that elaborate on the medical applications of active compression.

The Food and Drug Administration is sticking its toe in the water of artificial intelligence, providing its first guidance on the emerging development of applications for the technology in healthcare. The FDA released model 1.0 of its software precertification pilot Monday to provide an initial tool to test these programs. The agency noted that with AI and machine learning technology advancing rapidly, the health IT community must move quickly to ensure their safety in practical applications. "Software is increasingly used in healthcare to treat and diagnose conditions and diseases, aid clinical decision making and manage patient care," the FDA wrote in its working model (PDF). "Under this program, software developers would be assessed (by FDA or by an FDA-accredited third party) for the rigor of their practices in software design, testing, clinical assessment, and real-world performance monitoring, along with other appropriate capabilities."

-- Self - monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) are commonly used by type 1 diabetes (T1D) patients to measure glucose concentrations. The proposed adaptive basal - bolus algori thm (ABBA) supports inputs from either SMBG or CGM devices to provide personalised suggestions for the daily basal rate and prandial insulin doses on the basis of the patients' glucose level on the previous day. The ABBA is based on reinforcement learning (RL), a type of artificial intelligence, and was validated in silico with an FDA - accepted population of 100 adults under different realistic scenarios lasting three simulated months. The scenarios involve three main meals and one bedtime snack per day, alo ng with different variabilities and uncertainties for insulin sensitivity, mealtime, carbohydrate amount, and glucose measurement time. The results indicate that the proposed approach achieves comparable performance with CGM or SMBG as input signals, witho ut influencing the total daily insulin dose. The results are a promising indication that AI algorithmic approaches can provide personalised adaptive insulin optimisation and achieve glucose control - independently of the type of glucose monitoring technolo gy. Manuscript received August 30, 2018 This research was carried out within the framework of the MyTreat research and development project, supported by the Swiss Commi ssion of Technology and Innovation (CTI) under Grant 18172.1 PFLS - LS. Q.

Called IQcast, the feature tells users whether they have a low, medium or high chance of dropping below the target blood glucose range within the next one to four hours. These individual-specific predictions are generated by analyzing data collected through Sugar.IQ app and the Guardian Connect device. The Sugar.IQ app is currently available in the App Store for free download. The FDA-cleared app uses IBM Watson Health's AI and analytics tools to help users see how their glucose levels change during the day, and includes a smart food logging system, motivational insights, a glycemic assistant, a data tracker and a glycemic insights feature. Hypoglycemia -- defined by the American Diabetes Association as a blood glucose level lower than 70 mg/dL -- can lead to symptoms ranging from lightheadedness and lethargy to vision impairment and seizures.

FDA Commissioner Dr. Scott Gottlieb's recently announced appointment of Dr. Amy Abernethy to serve as his first lieutenant perfectly exemplifies the sort of thinking that has enabled Gottlieb to earn nearly universal praise at a time of unprecedented partisanship. He has focused his attention on high-priority, immediate concerns like the targeting of e-cigarettes to children, the opioid epidemic, and the need for a more robust market in affordable generic medications, while also seeking to prepare the agency to engage with emerging technologies, from consumer wearables to applications of artificial intelligence, to expand the breadth of evidence generation and accelerate the speed of analysis. Amy Abernethy, MD, PhD, formerly Chief Medical Officer, Chief Scientific Officer, and Senior Vice President, Flatiron Health, and recently named Deputy Commissioner of the FDA.A. Abernethy Abernethy combines the pragmatic humanism of an oncologist (which she is) with the technological sophistication of someone who spent their teenage years attending math camp and programming computers for NASA – after originally learning math by helping her mother edit a nursing textbook, as she recently told Lisa Suennen and me on our Tech Tonics podcast. The key challenge that Dr. Abernethy has focused on throughout her career – first in academia, at Duke, and more recently in business – is how to close the gap between clinical practice and clinical research. While at Duke, it struck her as odd, if not absurd, that she'd see patients in her oncology practice on a Monday, and then they'd need to come back on Tuesday to participate in a clinical trial she was conducting, because the two activities were considered so distinct.

In the past several years, I have prepared a "top 10" list of biomedical advances. For 2018, I am instead highlighting a few areas that are fast- moving, are attracting considerable attention, and have transformative potential. Those three areas are genome editing, artificial intelligence, and the gut microbiome. There has been steady progress with illumination of biology using CRISPR (among other editing tools, such as transcription activator-like effector nucleases [TALENs] and zinc finger nucleases). One example was taking the BRCA1 gene and systematically editing its nucleotides and assessing functional changes.[1] In just one study, we could ascertain functional effects of hundreds of mutations that took more than a decade for a genomics company (Myriad Genetics; Salt Lake City, Utah) to determine via family studies.

Digital technology is playing an increasingly important role in healthcare, and in the pharma industry as it searches for ways to find new drugs, while cutting costs and reducing expensive trial failures. The UK government's life sciences tsar, Sir John Bell, set the tone for 2018, when he went on record to say that artificial intelligence (AI) could save billions of pounds for the NHS. Bell told the BBC that researchers at an Oxford hospital have AI technology for diagnosing heart disease that could shave £2.2 billion from the NHS' pathology spend. Another AI system developed by a company called Optellum could allow more than 4,000 cancer patients a year to be diagnosed earlier. This could save £10 billion if adopted in the US and EU, according to the company's science and technology officer, Dr Timor Kadir.

Background: Pharmacokinetic evaluation is one of the key processes in drug discovery and development. However, current absorption, distribution, metabolism, excretion prediction models still have limited accuracy. Aim: This study aims to construct an integrated transfer learning and multitask learning approach for developing quantitative structure-activity relationship models to predict four human pharmacokinetic parameters. Methods: A pharmacokinetic dataset included 1104 U.S. FDA approved small molecule drugs. The dataset included four human pharmacokinetic parameter subsets (oral bioavailability, plasma protein binding rate, apparent volume of distribution at steady-state and elimination half-life). The pre-trained model was trained on over 30 million bioactivity data. An integrated transfer learning and multitask learning approach was established to enhance the model generalization. Results: The pharmacokinetic dataset was split into three parts (60:20:20) for training, validation and test by the improved Maximum Dissimilarity algorithm with the representative initial set selection algorithm and the weighted distance function. The multitask learning techniques enhanced the model predictive ability. The integrated transfer learning and multitask learning model demonstrated the best accuracies, because deep neural networks have the general feature extraction ability, transfer learning and multitask learning improved the model generalization. Conclusions: The integrated transfer learning and multitask learning approach with the improved dataset splitting algorithm was firstly introduced to predict the pharmacokinetic parameters. This method can be further employed in drug discovery and development.

Deep generative models such as generative adversarial networks, variational autoencoders, and autoregressive models are rapidly growing in popularity for the discovery of new molecules and materials. In this work, we introduce MOlecular SEtS (MOSES), a benchmarking platform to support research on machine learning for drug discovery. MOSES implements several popular molecular generation models and includes a set of metrics that evaluate the diversity and quality of generated molecules. MOSES is meant to standardize the research on the molecular generation and facilitate the sharing and comparison of new models. Additionally, we provide a large-scale comparison of existing state of the art models and elaborate on current challenges for generative models that might prove fertile ground for new research. Our platform and source code are freely available at https://github.com/molecularsets/

The Propeller spirometer and app uses advanced analytics to help patients identify triggers, symptoms, trends and other personalized insights. Also, Propeller's Air is an open API that uses machine learning from Propeller devices and environmental sources and can predict how asthma may be affected by local environmental conditions.