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COVID-19

Owkin Launches the Collaborative COVID-19 Open AI Consortium (COAI)

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After a fresh round of funding, Owkin recently launched the Covid-19 Open AI Consortium (COAI). This consortium will enable advanced collaborative research and accelerate clinical development of effective treatments for patients who are infected with COVID-19.

The first stage of the project is on fully understanding and treating cardiovascular complications in COVID-19 patients, this will be performed in collaboration with CAPACITY, an international registry working with over 50 centers around the world. Other areas of research will include patient outcomes and triage, and the prediction and characterization of immune response.

Owkin’s manifesto perfectly states the company’s vision:

“We are fully engaged in this new frontier with the goal of improving drug development and patient outcomes. Founded in 2016, Owkin has quickly emerged as a leader in bringing Artificial Intelligence (AI) and Machine Learning (ML) technologies to the healthcare industry. Our solutions improve the traditional medical research paradigm by turning a previously siloed, disjointed system into an innovative and collaborative one that, above all, puts the privacy of patients first.”

Federated Learning

To understand the model that Owkin is engaging one must fully understand a new technology which is called Federated Learning. Federated learning offers a framework for AI development that enables enterprises to train machine learning models on data that is distributed at scale across multiple medical institutions without centralizing the data. The benefits of this are two-fold, there is no loss of privacy since the data is not directly linked to any specific patient, and the data is maintained at the healthcare institution that collects this data.

The use of Federated Learning thereby enables a significantly wider range of data than what any single organization possesses in-house. What this means is that by used Federated Learning researchers have access to as much data as available, and the more big data a machine learning system possesses, the more accurate the AI becomes.

There are currently multiple national efforts in using AI to tackle COVID-19. The problem with many of these nationalistic disjointed efforts is that the data is specific to one country. Collecting data from a single region may fail to reveal important information that would enable researchers to fully understand how exposure to environmental elements, ethnic makeup, genetics, age, and gender may play important roles in understanding this disease. This is why collaboration is so important, and why gathering data from multiple jurisdictions is even more important.

As described by Owkin, they seek to used Federated Learning for the following:

“We aim to help them understand why drug efficacy varies from patient to patient, enhance the drug development process and identify the best drug for the right patient at the right time, to improve treatment outcomes.”

Understanding and treading cardiovascular health issues will be the first challenge undertaken by Owkin. As important as data is, what is even more important are the efforts of researchers and contributors who are spearheading this effort. This is why Unite.AI will be releasing three interviews with researchers that are contributing to the COAI project.

The Interviews

Sanjay Budhdeo, MD, Business Development:

Sanjay is a practicing physician. He holds Medical Sciences and Medical degrees from Oxford University and a Masters Degree from Cambridge University. Sanjay has research experience in neuroimaging, epidemiology and digital health. Prior to joining Owkin as a Partnership Manager, he was a Senior Associate at Boston Consulting Group, where he focused on data and digital in healthcare. He sits on the Patient Safety Committee at the Royal Society of Medicine and was previously a Specialist Advisor at the Care Quality Commission.

Click Here to read the interview with Sanjay.

Dr. Stephen Weng, Principal Researcher:

Stephen is an Assistant Professor of Integrated Epidemiology and Data Science who leads the data science research within the Primary Care Stratified Medicine Research Group.

He integrate traditional epidemiological methods and study design with new informatics-based approaches, harnessing and interrogating “big health care data” from electronic medical records for the purpose of risk prediction modeling, phenotyping chronic diseases, data science methods research, and translation of stratified medicine into primary care.

Click Here to read the interview with Stephen

Folkert W. Asselbergs, Principal Investigator

Folkert is professor of precision medicine in cardiovascular disease at Institute of Cardiovascular Science, UCL, Director NIHR BRC Clinical Research Informatics Unit at UCLH, professor of cardiovascular genetics and consultant cardiologist at the department of Cardiology, University Medical Center Utrecht, and chief scientific officer of the Durrer Center for Cardiovascular Research, Netherlands Heart Institute. Prof Asselbergs published more than 275 scientific papers and obtained funding from leDucq foundation, British and Dutch Heart Foundation, EU (FP7, ERA-CVD, IMI, BBMRI), and RO1 National Institutes of Health.

Click Here to read the interview with Folkert

Our Hope

The hope of Unite.AI is that by using biomedical images, genomics, and clinical data to discover biomarkers and mechanisms associated with diseases and treatment outcomes this will propel the next generation of treatment to tackle COVID-19. We are contributing to this important project by highlighting the personalities behind this important global effort.

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Antoine Tardif is a Futurist who is passionate about the future of AI and robotics. He is the CEO of BlockVentures.com, and has invested in over 50 AI & blockchain projects. He is the Co-Founder of Securities.io a news website focusing on digital securities, and is a founding partner of unite.AI. He is also a member of the Forbes Technology Council.

COVID-19

Intel AI Powered Virtual Assistant Mobilized to Assist Reopening of Military Museum

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A Canadian museum is safely reopening from its pandemic closure with the help of a virtual assistant powered by artificial intelligence (AI). Originally designed by CloudConstable to welcome visitors to the Ontario Regiment Museum, virtual assistant Master Corporal Lana interacts with visitors over a large screen, and was reconfigured with Intel® RealSense™ and AI technology to enable the safe return of the many volunteers who keep the museum and vehicles operating.

The Ontario Regiment Museum houses North America’s largest collection of operational military vehicles, many dating back to the 1940s. The collection allows the public to experience a piece of history, both at the museum and through the historical films in which the vehicles often appear.  

At the start of the pandemic in early 2020, CloudConstable began working with the museum to design Master Corporal Lana as an AI virtual assistant who would greet visitors, check them in and provide museum details.  

Before Lana’s deployment, COVID-19 closed the museum to the public. But with over 120 military vehicles that need constant servicing and driving, the museum needed its volunteers to continue their essential maintenance and operations work at the site.  

“The Ontario Regiment Museum is one of the few museums in the world with such a large and diverse collection of operating military vehicles, which help people experience history in a very real way. Regular maintenance is crucial, even during the worst of the pandemic, which is why we turned to CloudConstable and Intel to help build an autonomous solution,” said Jeremy Blowers, executive director of the Ontario Regiment Museum.  

CloudConstable relied on the Intel RealSense team’s insight that Lana’s existing and unique capabilities — already built on the Intel RealSense Depth Camera and using the Intel® Distribution of OpenVINO™ toolkit for accelerated machine vision inferences — could be extended for a more comprehensive and safer COVID-19 screening solution. Adding an Intel® NUC 9 Pro with Intel Active Management Technology, as part of the Intel vPro® platform, the team reworked Lana to take temperatures via thermal scans and ask a series of questions to assess COVID-19 risk and exposure. Since June, Lana has provided an enhanced, fully automated and touchless screening process so volunteers can continue to do their important work with the vehicles.  

“Intel RealSense technology is used to develop products that enrich people’s lives by enabling machines and devices to perceive the world in 3D. CloudConstable leverages Intel’s technology to help create a state-of-the-art natural voice and vision interface with touchless, self-service COVID-19 screening,” said Joel Hagberg, head of product management and marketing, Intel’s RealSense group. 

With the Ontario Regiment Museum now preparing to reopen to the public, CloudConstable, along with Intel, is now working to bring the new COVID-19 protection capabilities into the original concept for Lana as a greeter for visitors. Lana will greet visitors, provide contactless check-in, scan temperatures and ensure the museum adheres to visitor limits and other COVID-19 health protection protocols. Eventually, she’ll even thank them for coming and help visitors keep in touch with all the latest activities at the museum.

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COVID-19

U.S. National Institutes of Health Turns to AI for Fight Against COVID-19

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The National Institutes of Health has turned to artificial intelligence (AI) for diagnosis, treatment, and monitoring of COVID-19 through the creation of the Medical Imaging and Data Resource Center (MIDRC). 

What is the MIDRC?

The MIDRC consists of multiple institutions working together, led by the National Institute of Biomedical Imaging and Bioengineering (NIBIB), which is part of NIH. The collaboration aims to develop new technologies that will help physicians detect the virus early and create personalized therapies for patients.

Bruce J. Tromberg, Ph.D., is Director of the NIBIB.

“This program is particularly exciting because it will give us new ways to rapidly turn scientific findings into practical imaging tools that benefit COVID-19 patients,” Tromberg said. “It unites leaders in medical imaging and artificial intelligence from academia, professional societies, industry, and government to take on this important challenge.”

One of the ways experts assess the severity of a COVID-19 case is by looking at the features of infected lungs and hearts on medical images. This can also help predict how a patient will respond to treatment and improve the overall outcomes. 

The big challenge surrounding this method is that it’s difficult to rapidly and accurately identify these signatures and evaluate the information, especially when there are other clinical symptoms and tests. 

Machine Learning Algorithms

The MIDRC aims to develop and implement new and effective diagnostics. One of these will be machine learning algorithms, which solve some of those issues. Machine learning algorithms can help physicians optimize treatment after accurately and rapidly assessing the disease. 

Guoying Liu, Ph.D., is the NIBIB scientific program lead on the new approach.

“This effort will gather a large repository of COVID-19 chest images,” Liu explained, “allowing researchers to evaluate both lung and cardiac tissue data, ask critical research questions, and develop predictive COVID-19 imagining signatures that can be delivered to healthcare providers.”

Krishna Kandarpa, M.D., Ph.D., is director of research sciences and strategic directions at NIBIB. 

“This major initiative responds to the international imagining community’s expressed unmet need for a secure technological network to enable the development and ethical application of artificial intelligence to make the best medical decisions for COVID-19 patients,” Kandarpa said. “Eventually, the approaches developed could benefit other conditions as well.”

Some of the other major names on this project include Maryellen L. Giger, Ph.D., who is taking the lead. She is Professor of Radiology, Committee on Medical Physics at the University of Chicago. Co-investigators include Etta Pisano, MD, and Michael Tikin, MS, from the American College of Radiology (ACR), Curtis Langlotz, MD, Ph.D., and Adam Flanders, MD, from the Radiological Society of North America (RSNA), and Paul Kinahan, Ph.D., from the American Association of Physicists in Medicine (AAPM). 

Through collaborations between the ACR, RSNA, and AAPM, the MIDRC will work toward rapid collection, analysis, and dissemination of imagining and other clinical data. 

While many believe that the adoption of AI for pandemic-related solutions is long overdue, the National Institutes of Health’s new MIDRC is a step in that direction. It is only a matter of time before AI plays a major role in the detection, response, and eventual prevention of global pandemic causing viruses. 

 

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Autonomous Vehicles

Supply Chains after Covid-19: How Autonomous Solutions are Changing the Game

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Early measures by the material handling industry to curb the coronavirus pandemic saw border and plant closures all over the world. While for machine and vehicle manufacturers in eastern Europe and China production is in full swing again, the rest of Europe, North America and other western countries are struggling to get back to their pre-Covid-19 production strength.

Restrictions in freight transport across Europe are still very noticeable and are causing bottlenecks in supply chains. The strict stay-at-home-orders imposed in most European countries to contain the pandemic have had and are having a major impact on industrial production as the personnel are simply missing on site.

Security measures like keeping minimum distance or wearing masks are proving to be an organizational challenge for many production facilities around the world. In order to be able to comply with the safety requirements, in many premises only half of the workforce is allowed on-site, or the production line is divided into shifts. This in turn is restricting the flow of goods. Even when components exist, they stockpile, and cannot be integrated due to a lack of staff or time for those on reduced activity.

After the crisis, the industry will face new challenges. There is already speculation about a trend moving away from globalization towards regionalization. It is not necessarily the sourcing of production that could be affected by a possible regionalization, but rather warehouse management. Regardless of restricted supply chains, access to material inventory is essential for every production line. As a lesson-learned from the Covid-19 crisis, we could see a move from large central warehouses to smaller regional warehouses.

The automotive industry, for instance, was hit hard by supply shortages due to restrictions stemming from the pandemic. Automotive OEMs and their suppliers have long and complex supply chains with many steps in the production process. After the experienced bottlenecks, their follow-up measures might include a diversification of suppliers, as well as the decentralization of inventories in order to maintain agility in case of a crisis.

This presupposes digitalization of warehouse management: if existing stockpiling data is used rationally, transparency in the entire supply chain can easily be created. This would mean everyone involved could use existing data to optimize their processes. This requires intelligent warehouse management systems (WMS) and intelligent solutions for material handling to work hand-in-hand.

Automated guided vehicles (AGVs) are not a novelty in in-house material handling processes but their evolution could hold the key to the industry’s future. Since their introduction, technologies in autonomous vehicles have developed rapidly, enabling the transport of people in complex environments. Bringing this level of intelligence to industrial vehicles hails the next era of logistics automation: new AGV generations accessing complex outdoor environments are a real game changer and could potentially become more attractive after the Covid-19 crisis. As these vehicles become increasingly deployed in dynamic environments without infrastructure, these technologies have quickly migrated from manufacturing applications to supporting warehousing for manufacturing and distribution.

The process automation in supply chains – part of the so-called Industry 4.0 – will play a significant role. It could allow companies to keep or even reduce overall logistics operational costs, and eventually maintain a minimal operational flow even in times of crisis.

Rethinking the industrial supply chain: intelligence is key

The autonomous tow tractor TractEasy by autonomous technology leader EasyMile is a perfect example of this new generation. It masters the automation of outdoor and intralogistics processes on factory premises, logistics centers and airports. The company is currently demonstrating the maturity of these autonomous tow tractors at automotive supplier Peugeot Société Anonyme (PSA)’s manufacturing plant in Sochaux, France. Operated by GEODIS, PSA is using the tractor to find opportunities to optimize costs in the flows on its site.

The impact of the ongoing crisis has revealed the fragility of existing supply chains. Companies are reassessing large and complex procurement networks. Ultimately, the Covid -19 pandemic is putting supply chains to the test, but global supply chains should be prepared for crises as part of risk management anyway. The sheer number of natural disasters in recent years has meant that the international supply chains have been repeatedly overhauled. From this point of view, the Covid-19 crisis is an example of unpredictability that supply chains have to adapt to in order to develop.

What is certain is that the industry is on an upward trend toward more sustainable and stable industrial ecosystems. Automation is a concept that will play a major role in these future considerations, from manufacturers to logistic operators across the globe.

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