The Covid-19 Open AI Consortium (COAI) intends to bring breakthrough medical discoveries and actionable findings to the fight against the Covid-19 pandemic.
COAI aims to increase collaborative research, to accelerate clinical development of effective treatments for Covid-19, and to share all of its findings with the global medical and scientific community. COAI will unite collaborators: academic institutions, researchers, data scientists and industrial partners, to fight the Covid-19 pandemic.
This is the Third of three interviews with principal leaders behind COAI.
Folkert W. Asselbergs 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.
A percentage of the population that is afflicted with COVID-19 show signs of cardiovascular damage. What type of heart related problems are being seen?
From the studies that have been published thus far, acute cardiac injury is observed in up to 27.8% of patients. In addition, a number of case reports have been published of patients that have developed myocarditis and myocardial infarction in the context of COVID-19. There is also an unexpected high incidence of pulmonary embolisms in this patient population.
Do you believe that we currently have any type of understanding as to why COVID-19 causes this type of heart damage?
Our current understanding is still very limited. The release of troponin in critically ill patients is common, and also frequently seen in other patient groups (trauma/surgical/sepsis etc.). Troponin release is thus a non-specific finding and the mechanisms explaining myocardial injury in COVID-19 are not fully understood.
What type of people do we need to join the COAI project in order to maximize its efficacy?
To maximize the efficacy of this project, I believe we must strive towards a multidisciplinary approach between data scientists, statisticians, epidemiologists and clinicians.
You are currently a Professor of Precision medicine at the Institute of Cardiovascular Science and Institute of Health Informatics at UCL. Can you discuss possible ways precision medicine can be used to target COVID-19 with the current information that we have?
It is still unclear which people develop severe symptoms due to COVID-19. Novel predictive models are needed to identify those patients at high-risk. Those patients should be monitored more intensively and be prioritized for novel treatments.
What information do we need to gather to make precision medicine an effective tool in treating COVID-19 patients?
Easy to obtain routinely collected data is needed to develop a risk calculator to identify those at high-risk such as demographics, medical history and drug use. Of course, collaboration across sites and countries is needed to validate any developed risk model to ensure external validity.
One of the current projects you are associated with is the Capacity Covid Registry. Can you discuss what this project is and why it is so important?
COVID-19 patients with cardiovascular disease are a vulnerable population. To give these patients the best possible care and to be prepared for future outbreaks, we need to know more about these patients and the best practices for treating them. The CAPACITY COVID Registry was launched. CAPACITY COVID Registry is an extension of the registry released by the International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) and WHO in response to the emerging outbreak of COVID-19. CAPACITY registers data regarding the cardiovascular history, diagnostic information and occurrence of cardiovascular complications in patients with COVID-19. By collecting this information in a standardised manner, CAPACITY aims to provide more insight in:
- the vulnerability and clinical course of COVID-19 in patients with underlying cardiovascular disease;
- the incidence of cardiovascular in patients diagnosed with COVID-19.
In a perfect world, what type of data should be collected from COVID-19 patients?
In a perfect world, data would be collected at an early stage in home-setting to detect those at high risk for admission and when admitted as much data should be extracted from clinical systems such as Electronic Health Records including laboratory measurements, physical measurements and complaints during time to have as much information possible to early identify those at risk.
Should we also be collecting data from the segment of the population that is immune to COVID-19, in order to better understand what makes them immune?
We should also focus on those people tested positive for COVID-19 but only have mild symptoms to learn who are less vulnerable to severe symptoms.
Is there anything else that you would like to share about either the COVID-19 Open AI Consortium or the Capacity Covid Registry?
Since the launch of the registry, 88 centres across 17 countries have registered to join CAPACITY. We hereby would like to invite other centres to participate in CAPACITY-COVID. To allow a quick set up of the project for centres that want to participate, we have developed a portfolio of resources, including the study protocol, patient information form and standard operating procedures that are all freely available. For more information visit our website: www.capacity-covid.eu
Thank you for the fantastic interview. Readers who wish to learn more, may read our article describing the COAI project.
The first interview in this series was with Owkin’s Sanjay Budhdeo, MD, Business Development.
The second interview in this series was with Dr. Stephen Weng, Principal Investigator.
You may also visit the Covid-19 Open AI Consortium website.
Intel AI Powered Virtual Assistant Mobilized to Assist Reopening of Military Museum
A Canadian museum is safely reopening from its pandemic closure with the help of a virtual
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
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.
U.S. National Institutes of Health Turns to AI for Fight Against COVID-19
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.
Supply Chains after Covid-19: How Autonomous Solutions are Changing the Game
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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