Evaluating brain MRI scans with the help of artificial intelligence
Greece is just one example of a population where the share of older people is expanding, and with it the incidences of neurodegenerative diseases. Among these, Alzheimer’s disease is the most prevalent, accounting for 70% of neurodegenerative disease cases in Greece. According to estimates published by the Alzheimer Society of Greece, 197,000 people are suffering from the disease at present. This number is expected to rise to 354,000 by 2050.
Dr. Andreas Papadopoulos1, a physician and scientific coordinator at Iatropolis Medical Group, a leading diagnostic provider near Athens, Greece, explains the key role of early diagnosis: “The likelihood of developing Alzheimer’s may be only 1% to 2% at age 65. But then it doubles every five years. Existing drugs cannot reverse the course of the degeneration; they can only slow it down. This is why it’s crucial to make the right diagnosis in the preliminary stages—when the first mild cognitive disorder appears—and to filter out Alzheimer’s patients2.”
Diseases like Alzheimer’s or other neurodegenerative pathologies characteristically have a very slow progression, which makes is difficult to recognize and quantify pathological changes on brain MRI images at an early stage. In evaluating scans, some radiologists describe the process as one of “guesstimation,” as visual changes in the highly complex anatomy of the brain are not always possible to observe well with the human eye. This is where technical innovations such as artificial intelligence can offer support in interpreting clinical images.
One such tool is the AI-Rad Companion Brain MR3. Part of a family of AI-based, decision-support solutions for imaging, AI-Rad Companion Brain MR is a brain volumetry software that provides automatic volumetric quantification of different brain segments. “It is able to segment them from each other: it isolates the hippocampi and the lobes of the brain and quantifies white matter and gray matter volumes for each segment individually.” says Dr. Papadopoulos. In total, it has the capacity to segment, measure volumes, and highlight more than 40 regions of the brain.
Calculating volumetric properties manually can be an extremely laborious and time-consuming task. “More importantly, it also involves a degree of precise observation that humans are simply not able to achieve.” says Dr. Papadopoulos. Papadopoulos has always been an early adopter and welcomed technological innovations in imaging throughout his career. This AI-powered tool means that he can now also compare the quantifications with normative data from a healthy population. And it’s not all about the automation: the software displays the data in a structured report and generates a highlighted deviation map based on user settings. This allows the user to also monitor volumetric changes manually with all the key data prepared automatically in advance.
Opportunities for more accurate observation and evaluation of volumetric changes in the brain encourages Papadopoulos when he considers how important the early detection of neurodegenerative diseases is. He explains: “In the early stages, the volumetric changes are small. In the hippocampus, for example, there is a volume reduction of 10% to 15%, which is very difficult for the eye to detect. But the objective calculations provided by the system could prove a big help.”
The aim of AI is to relieve physicians of a considerable burden and, ultimately, to save time when optimally embedded in the workflow. An extremely valuable role for this particular AI-powered postprocessing tool is that it can visualize a deviation of the different structures that might be hard to identify with the naked eye. Papadopoulos already recognizes that the greatest advantage in his work is “the objective framework that AI-Rad Companion Brain MR provides on which he can base his subjective assessment during an examination.”
AI-Rad Companion4 from Siemens Healthineers supports clinicians in their daily routine of diagnostic decision-making. To maintain a continuous value stream, our AI-powered tools include regular software updates and upgrades that are deployed to the customers via the cloud. Customers can decide whether they want to integrate a fully cloud-based approach into their working environment leveraging all the benefits of the cloud or a hybrid approach that allows them to process imaging data within their own hospital IT setup.
The upcoming software version of AI-Rad Companion Brain MR will contain new algorithms that are capable of segmenting, quantifying, and visualizing white matter hyperintensities (WMH). Along with the McDonald criteria, reporting WHM aids in multiple sclerosis (MS) evaluation.
The emergent industrial metaverse
Annika Hauptvogel, head of technology and innovation management at Siemens, describes the industrial metaverse as “immersive, making users feel as if they’re in a real environment; collaborative in real time; open enough for different applications to seamlessly interact; and trusted by the individuals and businesses that participate”—far more than simply a digital world.
The industrial metaverse will revolutionize the way work is done, but it will also unlock significant new value for business and societies. By allowing businesses to model, prototype, and test dozens, hundreds, or millions of design iterations in real time and in an immersive, physics-based environment before committing physical and human resources to a project, industrial metaverse tools will usher in a new era of solving real-world problems digitally.
“The real world is very messy, noisy, and sometimes hard to really understand,” says Danny Lange, senior vice president of artificial intelligence at Unity Technologies, a leading platform for creating and growing real-time 3-D content. “The idea of the industrial metaverse is to create a cleaner connection between the real world and the virtual world, because the virtual world is so much easier and cheaper to work with.”
While real-life applications of the consumer metaverse are still developing, industrial metaverse use cases are purpose-driven, well aligned with real-world problems and business imperatives. The resource efficiencies enabled by industrial metaverse solutions may increase business competitiveness while also continually driving progress toward the sustainability, resilience, decarbonization, and dematerialization goals that are essential to human flourishing.
This report explores what it will take to create the industrial metaverse, its potential impacts on business and society, the challenges ahead, and innovative use cases that will shape the future. Its key findings are as follows:
• The industrial metaverse will bring together the digital and real worlds. It will enable a constant exchange of information, data, and decisions and empower industries to solve extraordinarily complex real-world problems digitally, changing how organizations operate and unlocking significant societal benefits.
• The digital twin is a core metaverse building block. These virtual models simulate real-world objects in detail. The next generation of digital twins will be photorealistic, physics-based, AI-enabled, and linked in metaverse ecosystems.
• The industrial metaverse will transform every industry. Currently existing digital twins illustrate the power and potential of the industrial metaverse to revolutionize design and engineering, testing, operations, and training.
The Download: China’s retro AI photos, and experts’ AI fears
Across social media, a number of creators are generating nostalgic photographs of China with the help of AI. Even though these images get some details wrong, they are realistic enough to trick and impress many of their followers.
The pictures look sophisticated in terms of definition, sharpness, saturation, and color tone. Their realism is partly down to a recent major update of image-making artificial-intelligence program Midjourney that was released in mid-March, which is better not only at generating human hands but also at simulating various photography styles.
It’s still relatively easy, even for untrained eyes, to tell that the photos are generated by an AI. But for some creators, their experiments are more about trying to recall a specific era in time than trying to trick their audience. Read the full story.
Zeyi’s story is from China Report, his weekly newsletter giving you the inside track on tech in China. Sign up to receive it in your inbox every Tuesday.
Read more of our reporting on AI-generated images:
+ These new tools let you see for yourself how biased AI image models are. Bias and stereotyping are still huge problems for systems like DALL-E 2 and Stable Diffusion, despite companies’ attempts to fix it. Read the full story.
Evolutionary organizations reimagine the future
The global technology consultancy Thoughtworks describes organizations that can respond to marketplace changes with continuous adaptation as “evolutionary organizations.” It argues that, instead of focusing only on technology change, organizations should focus on building capabilities that support ongoing reinvention. While many organizations recognize the benefit of adopting agile approaches in their technology capabilities and architectures, they have not extended these structures and ways of thinking throughout the operating model, which would allow their impact to extend beyond that of a single transformation project.
Global spending on digital transformation is growing at a brisk pace: 16.4% per year according to IDC. The firm’s 2021 “Worldwide Digital Transformation Spending Guide” forecasts that annual transformation expenditures will reach $2.8 trillion in 2025, more than double the spending in 2020.1 At the same time, research from Boston Consulting Group shows that 7 out of 10 digital transformation initiatives fall short of their objectives. Organizations that succeed, however, achieve almost double the earnings growth of those that fail and more than double the growth in the total value of their enterprises.2 Understanding how to make these transitions successful, then, should be of key interest to all business leaders.
This MIT Technology Review Insights report is based on a survey of 275 corporate leaders, supplemented by interviews with seven experts in digital transformation. Its key findings include the following:
• Digital transformation is not solely a technology issue. Adopting new technology for its own sake does not set the organization up to continue to adapt to changing circumstances. Among survey respondents, however, transformation is still synonymous with tech, with 70% planning to adopt a new technology in the next year, but only 41% pursuing changes to their business model.
• The business environment is changing faster than many organizations think. Most survey respondents (81%) believe their organization is more adaptable than average and nearly all (89%) say that they’re keeping up with or ahead of their competitors—suggesting a wide gap between the rapidly evolving reality and executives’ perceptions of their preparedness.
• All organizations must build capabilities for continuous reinvention. The only way to keep up is for organizations to continuously change and evolve, but most traditional businesses lack the strategic flexibility necessary to do this. Nearly half of business leaders outside the C-suite (44%), for example, say organizational structure, silos, or hierarchy are the biggest obstacle to transformation at their firm.
• Focusing on customer value and empowering employees are keys to organizational evolution. The most successful transformations prioritize creating customer value and enhancing customer and employee experience. Meeting evolving customer needs is the constant source of value in a world where everything is changing, but many traditional organizations fail to take this long view, with only 15% of respondents most concerned about failing to meet customer expectations if they fail to transform.
• Rapid experimentation requires the ability to fail and recover quickly. Organizations agree that iterative, experimental processes are essential to finding the right solutions, with 81% saying they have adopted agile practices. Fewer are confident, however, in their ability to execute decisions quickly (76%)—or to shut down initiatives that aren’t working (60%).