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PARIS, March 2, 2022 /PRNewswire/ -- GLEAMER, a French medtech company pioneering the use of artificial intelligence technology in the practice of radiology, announced today that the United States Food and Drug Administration has cleared its BoneView AI software for use by U.S. healthcare specialists to aid in diagnosing fractures and traumatic injuries on X-rays. In a U.S. study recently published by Boston University School of Medicine, BoneView was shown to help detect and localize fractures over the entire appendicular skeleton, rib cage, thoracic and lumbar spine, improving sensitivity and specificity, while reducing reading time.BoneView received the CE mark class 2a certification in the European Union in March 2020 and has been widely adopted in more than 300 institutions across 13 countries.

GLEAMER developed BoneView to aid radiologists, orthopedic surgeons, emergency physicians, rheumatologists, family physicians and physician assistants, all of whom read X-rays in clinical practice to diagnose fractures in their patients. BoneView detects fractures in X-ray images and submits them to radiologists for final validation, providing healthcare professionals with a safe, reliable, time-saving and user-friendly tool. The BoneView AI algorithm is cleared as a CADe/CADx (computer assisted detection and diagnosis) by the FDA and highlights regions of interest with bounding boxes around areas where fractures are suspected so radiologists can prioritize reading those X-rays.

The Study conducted between July 2020 and January 2021, used images acquired in the US from multiple centers on instruments from a wide variety of manufacturers and involved readers from Boston University School of Medicine (MA), Stony Brook University Renaissance School of Medicine (NY), and Massachusetts General Hospital - Harvard Medical School (MA). Results showed that BoneView AI assistance provided a 10.4 percent improvement of fracture detection sensitivity and shortened the radiograph reading time by 6.3 seconds per patient. The BoneView AI algorithm's standalone performance for fracture detection had an AUC of .97.

Across the six types of specialists participating in the Study, the combination of AI and health professionals' interpretations lowered the false negative rate (undetected fractures) on X-rays by 29 percent, while reducing reading time by 15 percent on exams specifically selected for their difficulty. BoneView also improved the specificity of fracture detection by radiologists and non-radiologists involving many anatomical locations, including foot/ankle, knee/leg, hip/pelvis, hand/wrist, elbow/arm, shoulder/clavicle, rib cage and thoracolumbar spine.

"BoneView can change everything about the way X-ray reading is done today," said Christian Allouche, CEO and co-founder of GLEAMER. "In the value-based U.S. health care system, providers tell us they want to improve the radiographic diagnostic process which accounts for a huge part of their workload and optimize patient management. We are delighted and proud to offer clinicians and patients BoneView AI for this state-of-the-art advancement in radiology and patient care."

Traumatic skeletal injuries are aleading source of consultation in emergency departments, representing one-third of annual visits. Fracture interpretation errors can represent up to 24 percent of harmful diagnostic errors seen in the ER and are more common during the evening and overnight hours, most likely related to non-expert reading and fatigue.

"Radiologists' workload has doubled in the past two decades, and despite technological progress, they must analyze hundreds more images every day, requiring the readings to be highly reliable," explained Ali Guermazi, MD, PhD, Chief of Radiology at VA Boston Healthcare System and Professor of Radiology and medicine at Boston University School of Medicine, and leader of the U.S. study. "The assistance of AI should allow usto improve the specificity of the complementary exams prescribed after the radiography, to avoid delays in care, and to direct patients into the right therapeutic pathway. Our study was focused on fracture diagnosis, and a similar concept can be applied to other diseases and disorders."

To date, BoneView has analyzed more than three million images around the world and is deployed in more than 13 countries across Europe, the Middle East, Asia-Pacific and North America. More than 3,500 radiologists and emergency physicians now relyon BoneView in their clinical routines.The solution is now available in the U.S. directly via GLEAMER and through other platforms including Fujifilm, Aidoc, Ferrum Health, Blackford Analysis.

About GLEAMER

GLEAMER's first globally available AI software, BoneView, recently received clearance by the U.S. Federal Food and Drug Administration and CE mark class 2a certification in Europe. Studies by world-leading radiologists and academic medical doctors have shown that BoneView improves detection of fractures in X-ray images, providing healthcare professionals with a safe, reliable, time-saving second reading. GLEAMER develops a suite of AI solutions for Radiology that encapsulate medical-grade expertise. The company wants to support imaging users to secure diagnoses for all patients and at all times, while improving efficiency. GLEAMER's AI Companions are directly integrated in the users' usual reading environment and act as an automated and transparent second reading to improve diagnostic accuracy in X-ray imaging. GLEAMER's solutions are currently being used across 13 countries in more than 300 institutions.

For more information: http://www.gleamer.ai

Media ContactIvy CohenIvy Cohen Corporate Communications[emailprotected](212) 399-0026

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GLEAMER Receives FDA Clearance for Its Artificial Intelligence Software to Help Detect and Localize Fractures - PR Newswire

This article features various ways AI can be applied to audit carbon credits

The total quantity of carbon dioxide (CO2) and other greenhouse gases (GHG) emitted in the lifecycle of the product or service, or in any specific financial year, is referred to as a carbon footprint. The measurement is commonly represented in kilos of CO2 equivalents, accounting for the impacts of various greenhouse gases on global warming.

A carbon credit is a marketable permit or certification that entitles the holder to emit one tonne of carbon dioxide or the equivalent of some other greenhouse gas it is effectively a carbon offset for greenhouse gas producers. The primary purpose of carbon credits is to help reduce greenhouse gas emissions from industrial activity in order to mitigate the impacts of global warming. They can also sell excess carbon credits.

Companies are thus motivated to cut greenhouse emissions on two levels: first, they will be penalized if they exceed the quota, and second, they may profit by preserving and reselling part of their emission permits.

a. A carbon offset that is traded in the voluntary market for credits is known as a voluntary emissions reduction (VER).

b. Emission units (or credits) produced within a legal framework with the goal of offsetting a projects emissions are known as certified emissions reductions (CERs).

Emerging IoT-powered devices can assist businesses in tracking and monitoring emissions throughout their whole carbon footprint. These IoT devices may help businesses gather and organise data regarding their activities and operations, as well as from every component of their supply chain, including materials.

Embodied carbon measurement is difficult because it necessitates tracing materials via complex manufacturing supply networks. AI can aid in the calculation of overall materials embodied carbon emissions, which can be difficult to track for big work sites.

Carbon offset monitoring necessitates meticulous documentation of all the many sorts of operations carried out by a corporation to offset carbon emissions. AI, object recognition, cloud computing, and other technologies can assist businesses in automatically recording and analysing data with minimum human intervention.

Artificial intelligence (AI) can assist companies in measuring and forecasting air quality and pollution levels, as well as tracking and predicting the increase and decrease of air pollution on job sites.

AI can learn to enforce on-site sorting and prevent illegal disposal of the wastes, which will aid in the reduction of carbon emissions and pollution in general.

AI & predictive analytics can assist businesses in conducting hassle-free carbon credit trading, therefore empowering the whole carbon credit trading industry.

Manual asset management becomes inefficient when the number of machines employed on job sites grows, as it is impossible for humans to monitor each and every machine at all times. AI technology may be utilised to continuously monitor operation hours, fuel use, and instances of wasteful equipment utilisation without missing a beat, assisting in the optimization of machinery usage.

AI, IoT, & cloud computing can all work together to maintain track of a companys carbon credits in an automated manner.

Predictive AI can assist businesses in estimating future emissions throughout their carbon footprint, taking into account current efforts, new carbon reduction strategies, and future demand.

AI, as well as other technologies such as IoT, may be used to track carbon pollution from various sources on job sites. This can assist businesses in identifying high-emitting & low-emitting fuels and, as a result, setting objectives, making decisions about their use, and reducing emissions.

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Applications of Artificial Intelligence in Carbon Credit Auditing - Analytics Insight

Telecommunications giant Bell is entering a partnership with Vector Institute to advance research and applications relating to artificial intelligence.

The institute is dedicated to studying AI and works with various companies and organizations to drive research and development.

Bell notes this partnership will help the company continue innovating in the telecom sector and be a part of emerging AI technologies in Canada and across Bell.

Bell is thrilled to collaborate with Vector and the work theyre doing in developing new research and expertise in artificial intelligence inCanada, John Watson, group president of customer experience, said in a statement.

Fostering the development of new technologies within our borders helps Canadian industry, and in turn, benefits Canadians. We are proud to help accelerate innovation in this field so that we can harness AI for applications at Bell.

The company says its currently using AI in all lines of business and will continue to do so to identify areas to improve its operations and customer experience.

Source: Bell

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Bell to work with Vector Institute on artificial intelligence research - MobileSyrup

WASHINGTON, March 1, 2022 /PRNewswire/ --HaystackID,a specialized eDiscovery services firm supporting law firms and corporate legal departments,announced today that the company has deepened its partnership with Reveal, a leading provider of AI-powered eDiscovery software. A long-time Brainspace provider, HaystackID can now leverage Reveal's entire end-to-end, cloud-based AI platform, which includes industry-leading processing, early case assessment, document review, investigations, production functionality, and a customizable API-enabled back-end, all packaged with superior visual analytics.

"When our clients are hit with litigation or an investigation, they need to find, understand, and learn from data quickly," HaystackID CEO Hal Brooks said. "We call that 'Discovery Intelligence.' Reveal's platform will help us to enhance this offering."

HaystackID's Discovery Intelligence approach ties together AI, data science, machine learning, and the human touch to gain early insight into information and to drive intelligent decisions in audits, investigations, and litigation. Reveal's out-of-the-box, pre-trained AI models can be added to HaystackID's workflows, making it easier for its clients to harness the power of AI throughout the discovery and/or investigation process.

Reveal's platform also enables HaystackID to continue delivering on its ReviewRight Protect powered by the Protect Analytics tool for the detection, identification, review, and notification of sensitive privacy or data-related breaches and disclosures. Enabled by a collection of proprietary workflows, which now also includes Reveal, Protect Analytics can proactively or reactively help find sensitive data concentrations, locations, and relationships to inform transfer impact assessments, privacy assessments, notification lists, exposure assessments, and discovery targeting.

"As we continue to expand the depth and breadth of Reveal's marketplace offerings, we are especially excited to partner with HaystackID, a demonstrated leader in areas ranging from AI to cyber discovery," said Wendell Jisa, Reveal's founder & CEO. "By taking full advantage of Reveal's platform, the company now has access to the industry's leading SaaS-based AI platform with full integration into its already robust technology stack."

The combination of Reveal's platform and HaystackID's current offerings allows legal professionals to immediately learn more from their data, while also enjoying the flexibility and scalability needed in today's data-centric world.

"We believe that a better understanding of data allows our clients to improve their practice of law, without having to focus too much on processes or data decisions," added HaystackID's Brooks. "We are thrilled to deepen our partnership to help us deliver that for our global clients."

About HaystackIDHaystackID is a specialized eDiscovery services firm that helps corporations and law firms securely find, understand, and learn from data when facing complex, data-intensive investigations and litigation. HaystackID mobilizes industry-leading cyber discovery services, enterprise solutions, and legal discovery offerings to serve more than 500 of the world's leading corporations and law firms in North America and Europe. Serving nearly half of the Fortune 100, HaystackID is an alternative cyber and legal services provider that combines expertise and technical excellence with a culture of white-glove customer service. In addition to consistently being ranked by Chambers USA, the company was recently named a worldwide leader in eDiscovery services byIDC MarketScape and a representative vendor in the 2021 Gartner Market Guide for E-Discovery Solutions. Further, HaystackID has achieved SOC 2 Type II attestation in the five trust service areas of security, availability, processing integrity, confidentiality, and privacy. For more information about its suite of services, including programs and solutions for unique legal enterprise needs, go toHaystackID.com.

About RevealReveal, with Brainspace technology, is a global provider of the leading AI-powered eDiscovery platform. Fueled by powerful AI technology and backed by the most experienced team of datascientists in the industry, Reveal's software offers a full suite of eDiscovery solutions allon one seamless platform. Users of Reveal include law firms, Fortune 500 corporations, legal serviceproviders, government agencies and financial institutions in more than 40 countries across fivecontinents. Featuring deployment options in the cloud or on-premise, an intuitive user design andmultilingual user interfaces, Reveal is modernizing the practice of law, saving users time, money andoffering them a competitive advantage. For more information, visithttp://www.revealdata.comor followus onTwitter,FacebookandLinkedIn.

HaystackID Media Contact:Leora Goldfarb [emailprotected]858-603-5123

Rob Robinson[emailprotected]512-934-7531

HaystackID on Social Media+ Twitter (@HaystackID)+ LinkedIn

Reveal Media Contact:Liz Whelan[emailprotected]

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HaystackID Expands Artificial Intelligence Capabilities with Reveal - PR Newswire

Machine learning makes it possible to generate more data than mathematician can in a lifetime

For the first time, mathematicians have partnered with artificial intelligence to suggest and prove new mathematical theorems. While computers have long been used to generate data for mathematicians, the task of identifying interesting patterns has relied mainly on the intuition of the mathematicians themselves. However, its now possible to generate more data than any mathematician can reasonably expect to study in a lifetime. Which is where machine learning comes in.

Two separate groups of mathematicians worked alongside DeepMind, a branch of Alphabet, Googles parent company, dedicated to the development of advanced artificial intelligence systems. Andrs Juhsz and Marc Lackenby of the University of Oxford taught DeepMinds machine learning models to look for patterns in geometric objects called knots. The models detected connections that Juhsz and Lackenby elaborated to bridge two areas of knot theory that mathematicians had long speculated should be related. In separate work, Williamson used machine learning to refine an old conjecture that connects graphs and polynomials.

Andrs Juhsz and Marc Lackenby of the University of Oxford taught DeepMinds machine learning models to look for patterns in geometric objects called knots. The models detected connections that Juhsz and Lackenby elaborated to bridge two areas of knot theory that mathematicians had long speculated should be related. In separate work, Williamson used machine learning to refine an old conjecture that connects graphs and polynomials.

The most amazing thing about this work and it really is a big breakthrough is the fact that all the pieces came together and that these people worked as a team, said Radmila Sazdanovic of North Carolina State University.

Some observers, however, view the collaboration as less of a sea change in the way mathematical research is conducted. While the computers pointed the mathematicians toward a range of possible relationships, the mathematicians themselves needed to identify the ones worth exploring.

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Mathematicians to Build New Connections With Machine Learning: Know-How - Analytics Insight