Mediaboss Marketing

Summary: New artificial intelligence technology is able to screen for genetic mutations in brain cancer tumors in less than 90 seconds.

Source: University of Michigan

Using artificial intelligence, researchers have discovered how to screen for genetic mutations in cancerous brain tumors in under 90 seconds and possibly streamline the diagnosis and treatment of gliomas, a study suggests.

A team of neurosurgeons and engineers at Michigan Medicine, in collaboration with investigators from New York University, University of California, San Francisco and others, developed an AI-based diagnostic screening system called DeepGlioma that uses rapid imaging to analyze tumor specimens taken during an operation and detect genetic mutations more rapidly.

In a study of more than 150 patients with diffuse glioma, the most common and deadly primary brain tumor, the newly developed system identified mutations used by the World Health Organization to define molecular subgroups of the condition with an average accuracy over 90%.

The results arepublished inNature Medicine.

This AI-based tool has the potential to improve the access and speed of diagnosis and care of patients with deadly brain tumors, said lead author and creator of DeepGliomaTodd Hollon, M.D., a neurosurgeon at University of Michigan Health and assistant professor of neurosurgery at U-M Medical School.

Molecular classification is increasingly central to the diagnosis and treatment of gliomas, as the benefits and risks of surgery vary among brain tumor patients depending on their genetic makeup.

In fact, patients with a specific type of diffuse glioma called astrocytomas cangain an average of five yearswith complete tumor removal compared to other diffuse glioma subtypes.

However, access to molecular testing for diffuse glioma is limited and not uniformly available at centers that treat patients with brain tumors. When it is available, Hollon says, the turnaround time for results can take days, even weeks.

Barriers to molecular diagnosis can result in suboptimal care for patients with brain tumors, complicating surgical decision-making and selection of chemoradiation regimens, Hollon said.

Prior to DeepGlioma, surgeons did not have a method to differentiate diffuse gliomas during surgery. An idea that started in 2019, the system combines deep neural networks with an optical imaging method known as stimulated Raman histology, which was also developed at U-M, to image brain tumor tissue in real time.

DeepGlioma creates an avenue for accurate and more timely identification that would give providers a better chance to define treatments and predict patient prognosis, Hollon said.

Even with optimal standard-of-care treatment, patients with diffuse glioma face limited treatment options. The median survival time for patients with malignant diffuse gliomas is only 18 months.

While the development of medications to treat the tumors is essential,fewer than 10%of patients with glioma are enrolled in clinical trials, which often limit participation by molecular subgroups. Researchers hope that DeepGlioma can be a catalyst for early trial enrollment.

Progress in the treatment of the most deadly brain tumors has been limited in the past decades- in part because it has been hard to identify the patients who would benefit most from targeted therapies, said senior authorDaniel Orringer, M.D., an associate professor of neurosurgery and pathology at NYU Grossman School of Medicine, who developed stimulated Raman histology.

Rapid methods for molecular classification hold great promise for rethinking clinical trial design and bringing new therapies to patients.

Additional authors include Cheng Jiang, Asadur Chowdury, Akhil Kondepudi, Arjun Adapa, Wajd Al-Holou, Jason Heth, Oren Sagher, Maria Castro, Sandra Camelo-Piragua, Honglak Lee, all of University of Michigan, Mustafa Nasir-Moin, John Golfinos, Matija Snuderl, all of New York University, Alexander Aabedi, Pedro Lowenstein, Mitchel Berger, Shawn Hervey-Jumper, all of University of California, San Francisco, Lisa Irina Wadiura, Georg Widhalm, both of Medical University Vienna, Volker Neuschmelting, David Reinecke, Niklas von Spreckelsen, all of University Hospital Cologne, and Christian Freudiger, Invenio Imaging, Inc.

Funding: This work was supported by the National Institutes of Health, Cook Family Brain Tumor Research Fund, the Mark Trauner Brain Research Fund, the Zenkel Family Foundation, Ians Friends Foundation and the UM Precision Health Investigators Awards grant program.

Author: Noah FromsonSource: University of MichiganContact: Noah Fromson University of MichiganImage: The image is in the public domain

Original Research: Closed access.Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging by Todd Hollon et al. Nature Medicine

Abstract

Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging

Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for patients with brain tumors is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment.

In this study, we developed DeepGlioma, a rapid (<90seconds), artificial-intelligence-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas.

DeepGlioma is trained using a multimodal dataset that includes stimulated Raman histology (SRH); a rapid, label-free, non-consumptive, optical imaging method; and large-scale, public genomic data. In a prospective, multicenter, international testing cohort of patients with diffuse glioma (n=153) who underwent real-time SRH imaging, we demonstrate that DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy (IDH mutation, 1p19q co-deletion and ATRX mutation), achieving a mean molecular classification accuracy of 93.31.6%.

Our results represent how artificial intelligence and optical histology can be used to provide a rapid and scalable adjunct to wet lab methods for the molecular screening of patients with diffuse glioma.

Read the original:
Artificial Intelligence Predicts Genetics of Cancerous Brain Tumors ... - Neuroscience News

Note: Press "Esc" on your keyboard to close Fullscreen mode, or click the minimize icon.

In the ongoing aftermath of COVID-19, manufacturing enterprises are seeking sustainable supply chain strategies that include extensive use of artificial intelligenceTom Fisk - Pexels

Manufacturing Growth Advocacy Network (MAGNET)Bob Perkoski

AI can automate routine tasks around order tracking and quality control, reducing costs and improving efficiencyTiger Lily - Pexels

On the transportation side, AI could spit out optimal delivery routes, or exact windows for trucks to arrive or depart a transportation centerTima Miroshnichenko - Pexels

How can ChatGPT be used by manufacturers to help their businesses?

Anyone with a computer and a bit of curiosity can pose this question to the ChatGPT online artificial intelligence (AI) tool. With a single prompt, the AI-powered chatbot will sing the praises of digital transformationa new age for the industry where customer service and lead generation exist on the cutting edge.

Yet, for sector proponents including Clevelands Manufacturing Growth Advocacy Network (MAGNET), the technologys potential for supply chain optimization is the real eye-opener.

In recent years, supply chains have become significantly more challenging to manage, notes MAGNET president and CEO Ethan Karp. Existing vulnerabilities in the flow of raw materials and finished goods were worsened by the pandemic, disrupting new product creation and leaving companies scrambling for answers.

In the ongoing aftermath of COVID-19, manufacturing enterprises are seeking sustainable supply chain strategies that include extensive use of artificial intelligence. The ChatGPT innovations ability to understand relationships and analyze huge volumes of data can change how these companies approach everything from sales to materials procurement.

The functionality of ChatGPT can take data from inventory systems and generate an email to a supplier that says, We need this on X date, explains Karp.

Whereas preventive maintenance is perhaps the most talked-about use case for AI tools like ChatGPT, the techs pattern identifying abilities can be harnessed for supply chain issues as well, according to Karp.

In theory, the powerful chatbot could forecast supply disruptionsallowing manufacturers to plan for problems before they occur. Additionally, AI can automate routine tasks around order tracking and quality control, reducing costs and improving efficiency.

Previously siloed manufacturing departments and stakeholders, meanwhile, could be brought together by AI. The technology has the industry covered on risk management as well, giving builders lead time on natural disasters or geopolitical events before major supply network disruptions arise.

Enterprise resource planning systems (ERP) are likely the best supply-related application for the nascent chatbot, says Karp. As ERP is integrated into daily business processes, including AI in that equation only makes sense.

All those functions about communicating with suppliers would be embedded in a software package that becomes more powerful and user friendly, Karp says.

Western Reserve University professor Michael GoulderDont get ahead of yourself

Case Western Reserve University (CWRU) professor Michael Goulder knows very well the possibilities of an AI-assisted supply chain. Along with his duties as a professor at CWRU, Goulder also leads the colleges master of supply chain management program.

In his previous career, Goulder oversaw the supply network for Hudson-based JoAnn Fabrics, giving him a full understanding of the complex system that starts with raw materials and ends when a user receives a finished product. Supply administration done correctly reduces costs and leads to a more efficient production cycle, he says.

Considering how fragile supply lines became during COVID-19, using AI and machine learning to strengthen the system seems an obvious choice.

However, the boundless buzz around AI reminds the CWRU prof of the late 1990s Internet boom and subsequent bust.

There is a vast overestimation of the speed at which these technologies will be perfected and commercialized, says Goulder. It took 10-plus years for the Internet to mature, and likewise it will take longer than people think for AI to mature.

Though Goulder is cautious about AIs immediate impacts, there are reasons to be excited about the technologys future. AI could be fed big supply chain data sets and return thousands of actionable variables.

The beauty of machine learning is that it will determine the variables that make the most sense, Goulder says. That will revolutionize supply chain forecasting when the technology matures.

Inventory and transportation management are additional circumstances where AI can shine. On the transportation side, artificial intelligence could spit out optimal delivery routes, or exact windows for trucks to arrive or depart a transportation center.

Companies will have a model about what products are selling in what parts of the country, then start shipping those goods knowing what the demands are, says Goulder. The [AI] models will learn and get better over time.

MAGNET president and CEO Ethan KarpPlacing a bet on AI

Currently, most organizations do not have the sophistication to leverage emerging AI technologies. Any manufacturing firm interested in pursuing digital designs must know how to capture the innovations full value, Goulder says.

That means purpose-built analytics rather than half-hearted experimentation with an application like ChatGPT. Goulder says he expects talents around Python and other programming languages will be in demand as artificial intelligence takes hold in manufacturing and beyond.

Business leaders want highly developed analytical skillsthey wont hire someone if that person doesnt know Python, says Goulder. Those skills are now table stakes. If I was a young person in the supply chain or a mid-career manager, Id make a big bet on those tools.

MAGNETs Karp agrees that ChatGPT cannot just be bolted on to a companys supply chain network. Simply giving the chatbot a few prompts reveals the errors in what passes for its thinking.

Ultimately, it sounds like a person and makes you believe its thinking like a person, but its just taking information and smashing it together with no mind toward sense, Karp argues.

Caveats aside, Karp cannot help but be thrilled by AIs down-the-line benefits for the manufacturing supply chain.

There have been conversations about AI for years, but this makes it real for people, says Karp. The supply chain [for this tech] makes sense, because there is a lot of communication that goes back and forth. The more real-time [we can get], the better.

See the original post here:
The dawn of ChatGPT: Artificial intelligence could be a boon for the ... - freshwatercleveland

Keying in on industrial and academic uses of artificial intelligence, Houston Community College will host a conference on both subjects March 30 and 31 from 9:45 a.m. to 4:30 p.m. at Houston Community College's West Houston Institute at 2811 Hayes Road, Houston.

On March 30, the Gulf Coast Annual Conference on Artificial Intelligence will feature a student panel on academic and career pursuits in AI and machine learning as well as keynote speakers and presentations from industry experts and educators. NVIDIA Senior Solutions Architect Pavel Dimitrov will speak on Modulus, MTC & Omniverse, while Austin Carson, founder and president of SeedAI, will speak on building AI across America.

U.S. Rep. Michael McCaul, R-Austin, is also expected to speak on March 30.

Additional speakers from NVIDIA, the Mitre Corp., semiconductor company Advanced Micro Devices, Amazon Web Services and Dell Technologies are expected to present on March 31.

Tickets are available to students for $10 a day or to professional registrants for $40 a day. Virtual registration is available for $25 and group registration is also available.

Visit link:
Houston Community College to host Gulf Coast Annual Conference ... - Community Impact Newspaper

Runway, an artificial intelligence (AI) platform, has recently released its latest software, Gen-2, which can create full videos from text descriptions. Gen-2 was developed after Runways previous model, Gen-1, which used existing video data to make new videos. The new platform is the first publicly available text-to-video model on the market and can realistically synthesize new videos from text descriptions. Gen-2 combines the features of its predecessor and can create entirely new video content from text prompts. The web-based platform can generate high-resolution videos that demonstrate the technologys power, even if they are not photorealistic.

The second generation includes three new modes:

Traditionally, creating a video requires a lot of time, effort, and resources. The process was often laborious and expensive, from scriptwriting to filming and editing. However, with the introduction of text to video, creating a video has become significantly more accessible and efficient.

Text-to-video is an AI-powered tool that enables users to generate a video from written text. Essentially, the tool transforms a piece of text into a video, complete with images, animations, and voiceover. The result is a professional-looking video that can be used for many purposes, such as marketing, education, and entertainment.

The process of creating a video using text-to-video is straightforward. First, the user enters their text into the platform and any visual or audio assets they want to include in the video. Next, the platform uses its AI algorithms to analyze the text and generate a storyboard, which outlines the sequence of images and animations used in the video.

From there, the user can fine-tune the video by adjusting the visuals and audio, adding music or sound effects, and selecting a voiceover artist. Once complete, the video can be exported in various formats, including high-definition (HD) and 4K.

One of the key advantages of text-to-video is its speed and efficiency. Rather than spending hours or days filming and editing a video, users can generate a professional-looking video in minutes or hours. This makes it an ideal tool for marketers, educators, and content creators who must produce high-quality videos regularly.

Another advantage of text-to-video is its accessibility. Unlike traditional video production, which requires a significant investment in equipment and expertise, text-to-video is a highly accessible tool that anyone can use regardless of their technical expertise or budget.

Finally, text-to-video can transform how we share information and tell stories. By making video production more accessible and efficient, we can expect a significant increase in the amount and variety of video content produced. This, in turn, will open up new opportunities for communication, education, and entertainment and help us to share our ideas and experiences with a wider audience.

In conclusion, text-to-video is a game-changing tool that can transform how we create and share video content. Whether you are a marketer, educator, or content creator, this innovative new tool is worth exploring to streamline your video production process and reach a wider audience with your message.

Check out theGen-2 Tool.All Credit For This Research Goes To the Researchers on This Project. Also,dont forget to joinour 16k+ ML SubReddit,Discord Channel,andEmail Newsletter, where we share the latest AI research news, cool AI projects, and more.

Niharika is a Technical consulting intern at Marktechpost. She is a third year undergraduate, currently pursuing her B.Tech from Indian Institute of Technology(IIT), Kharagpur. She is a highly enthusiastic individual with a keen interest in Machine learning, Data science and AI and an avid reader of the latest developments in these fields.

See the article here:
If You Can Say It, Now You Can See It: RunWays Latest Artificial Intelligence Tool Can Generate Videos With Nothing But Words - MarkTechPost

Machine Learning Vs. Artificial Intelligence? How They Differ And How They Will Disrupt The Technological Landscape  Forbes

View post:
Machine Learning Vs. Artificial Intelligence? How They Differ And How They Will Disrupt The Technological Landscape - Forbes