The Global Deep Learning in CT Scanners Market report draws precise insights by examining the latest and prospective industry trends and helping readers recognize the products and services that are boosting revenue growth and profitability. The study performs a detailed analysis of all the significant factors, including drivers, constraints, threats, challenges, prospects, and industry-specific trends, impacting the Deep Learning in CT Scanners market on a global and regional scale. Additionally, the report cites worldwide market scenario along with competitive landscape of leading participants.

The recent study on Deep Learning in CT Scanners market offers a detailed analysis of this business vertical by expounding the key development trends, restraints & limitations, and opportunities that will influence the industry dynamics in the coming years. Proceeding further, it sheds light on the regional markets and identifies the top areas to further business development, followed by a thorough scrutiny of the prominent companies in this business sphere. Additionally, the report explicates the impact of the Covid-19 pandemic on the profitability graph and highlights the business strategies adopted by major players to adapt to the instabilities in the market.

Major highlights from the Covid-19 impact analysis:

• Footprint of the Covid-19 pandemic on the global economy.
• Fluctuations in the supply & demand.
• Predicted outlook of the pandemic on business expansion.

An overview of the regional analysis:

• Deep Learning in CT Scanners market is split into several regional markets, namely, North America, Europe, Asia-Pacific, South America, Middle East and Africa.
• Summary of each regional contributor, inclusive of their yearly growth rate over the stipulated timeframe is enclosed in the document.
• Net revenue & sales gathered by each region are also cited.

Additional highlights from the Deep Learning in CT Scanners market report:

• The product landscape of Deep Learning in CT Scanners market is divided into Stationary andPortable.
• Volume and revenue estimations of each product category along with statistically supporting information are given.
• Insights about the yearly growth rate and industry share of each product segment over the forecast period are highlighted.
• Speaking of application spectrum, the overall market is bifurcated into Hospital,Diagnostic Center,Research,Veterinary Clinic, ,Geographically, the detailed analysis of production, trade of the following countries is covered in Chapter 4.2, 5: ,United States ,Europe ,China ,Japan andIndia.
• Estimated annual growth rate and market share of each application category during the stipulated timeframe are duly presented.
• Organizations that have a strong presence in Deep Learning in CT Scanners market are Shimadzu,Hitachi,Neusoft Medical Systems,Toshiba Corporation,Medtronic,GE Health,Accuray,Siemens Healthcare GmbH,Samsung andPhilips.
• Exhaustive profiling of the listed companies is conducted in terms of their product offerings, manufacturing capacity, and remuneration.
• Other vital business facets such as pricing patterns, market share, and gross margins of each player are covered as well.
• Major competitive trends and its effect on businesses are discussed extensively.
• A comprehensive study of the supply chain with respect upstream & downstream basics, and distributions channels is incorporated in the report.
• Further, it undertakes SWOT analysis and Porter’s five forces assessment to evaluate the investment feasibility of a new project.

Strategic Points Covered in Table of Content of Global Deep Learning in CT Scanners Market:

• Chapter 1: Introduction, market driving force product Objective of Study and Research Scope Deep Learning in CT Scanners market
• Chapter 2: Exclusive Summary – the basic information of Deep Learning in CT Scanners Market.
• Chapter 3: Displaying the Market Dynamics- Drivers, Trends and Challenges of Deep Learning in CT Scanners
• Chapter 4: Presenting Deep Learning in CT ScannersMarket Factor Analysis Porters Five Forces, Supply/Value Chain, PESTEL analysis, Market Entropy, Patent/Trademark Analysis.
• Chapter 5: Displaying the by Type, End User and Region 2020-2026
• Chapter 6: Evaluating the leading manufacturers of Deep Learning in CT Scanners market which consists of its Competitive Landscape, Peer Group Analysis, BCG Matrix & Company Profile
• Chapter 7: To evaluate the market by segments, by countries and by manufacturers with revenue share and sales by key countries in these various regions.
• Chapter 8 & 9: Displaying the Appendix, Methodology and Data Source

The post Deep Learning in CT Scanners Market likely to touch new heights by end of forecast period 2021-2026 appeared first on Artificial Intelligence.

Ken Goldberg thinks artificial intelligence will enable surgical robots to achieve their best function — not replacing surgeons but augmenting their work by reducing the monotony of specific subtasks like suturing.

The William S. Floyd Jr. Distinguished Chair in Engineering at UC Berkeley, Goldberg and his research team have continued to advance the field.

The group — which includes postdoctoral researchers Minho Hwang and Jeffrey Ichnowski and PhD student Brijen Thananjeyan — has demonstrated how a deep neural network plus 3D-printed depth-sensing markers can train a da Vinci surgical robot to automatically perform peg transfer slightly faster and more accurately than an experienced surgical resident.

They also published a paper in Science Robotics last November about new AI software that allows robots to learn how to rapidly plan smooth motions that increase speed and reduce wear in factories, warehouses…and operating rooms.

Their work encompasses so much more, too. (Here’s a list of published research.)

Goldberg recently consulted with his research team to provide some insights to Medical Design & Outsourcing and MassDevice about where the surgical robotics space could advance in coming years:

MDO: You’ve spoken before of an emerging generation of surgical robots. Tell us more.

Goldberg: Many of the early patents on surgical-assist robots are expiring, so there are several new companies entering the market. For example, Johnson and Johnson purchased Auris, and Medtronic purchased Mazor in the past 2 years. Many new companies that are emerging in Asia — including China and Korea — are also developing a new generation of surgical-assist robots at lower cost and working to introduce some supervised autonomy.

Rather than replacing human surgeons, an emerging new generation of robots will assist surgeons by performing tedious subtasks such as suturing and debridement to improve consistency, reduce fatigue and open the door to long-distance tele-surgery.  Advances in AI can be applied to data collected from surgical systems such as Intuitive’s da Vinci to learn underlying control policies for subtasks including cutting, suturing, palpation, dissection, retraction and debridement.

MDO: How can artificial intelligence advance robot-assisted surgery? Give us an example.

Goldberg: We were recently able to automate peg transfer, a common training procedure for minimally invasive surgery, with 99.4% accuracy (357/360 trials) [on a da Vinci Research Kit] — even when the robot tools are switched. This task is challenging because it requires high accuracy, and the cables that drive surgical robots’ joints stretch during motion, which can significantly reduce their accuracy.[We’ve learned] to correct errors at key points in the task visually by using demonstrations from a human teleoperator.

We also present an alternate approach to this task in two papers, one published and one preprint. These approaches use fiducial markers and depth-sensing with deep learning to learn a model of how the robot moves as a function of its past motions, which is a complex function of its cabling properties. Using this learned model for control, we achieve 94-100% accuracy on bilateral and unilateral versions of the task, respectively. Using a trajectory optimization procedure, we are able to roughly match or outperform a human surgeon in terms of speed as well.

MDO:  TransEnterix executives see AI as an edge when it comes to competing better with its Senhance system. What do you think of their Intelligent Surgical Unit?

Goldberg: Learning how to position and move the surgical camera without manual adjustment is a very interesting problem.  It is tedious for the surgeon to do this herself, so it would be helpful if a robot could anticipate, for example, as a suture advances beyond the field of view and track the camera to move accordingly.  This is also subtle because erroneous camera motions could be frustrating for surgeons.

MDO: Overall, what are you most excited about in the robot-assisted surgery space?

Goldberg: I’m hopeful that we can develop systems that can learn to perform specific subtasks such as suturing or debridement faster and more accurately than human surgeons, thus relieving human surgeons of tedium, allowing them to focus on more nuanced aspects of surgery and also to reduce time in the operating room.

This research field is growing rapidly: There are now 30 labs worldwide doing experiments with a research version of the Intuitive surgical-assistant robot, and new hardware and algorithms are being published every month.

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